Last Ran: 2021-09-14

Task(s):

  1. Recode HHQ variable responce entries.
    • TASK:: Finish recoding recent illness (resolved factor), hospitalization, and surgury status.
  2. Aggregate/merge processed medication datasets.
    • Review manually extracted vs. USP classified beta blocking variables.
  3. Aggregate/merge w. basic demographics.
  4. Write out datasets.
  5. Import Other RedCap Questionnaires..
    • CIRS Data (for aggregated corplot w. medications)
    • Hru Data (for aggregated corplot w. medications, etc.)
    • Macarthur SES data, ses_ (edu prioritized, -to compare with HHQ edu vars)
    • V02 Test Data —
    • model VO2 of former v. current smokers, alternative v. cigs only, etc.
    • compare w. usp and manually generated beta blocker vars
    • check expected relations (Rest HR diff, beta.b, PeakV02/kgs, cardiovascular medications, etc.)
  6. Look for basic correlations pertaining to smoking, alcohol usage, and general demographics using health history questionnaire.
    • e.g., How does smoking history relate to the other measures of health history and demographics? What about alcohol intake?

Keywords: United States Pharmacopeia–National Formulary (USP–NF),

 

Health History Questionnaire (HHQ)

Import Data 
/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/HHQ
setwd("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/HHQ/")
FILE<-list.files("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/HHQ/", pattern="IGNITEDatabase-IGNITEBaselineHealth*")

data<-paste("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/HHQ/", FILE, sep="")
HHQraw.df<-read.csv(data,
                    stringsAsFactors = F,
                    na.strings = c("-99991","-99992","-99993","-99994", "-99995",
               "-99996","-99997","-99998","-99999", "-9999910",
               "-999992","----", "---",  "n/a", "Unknown", ""))

HHQraw.df$Notes<-""
Randomized_demos<-HHQraw.df[,(46:52)]

Creating Summary Variables...

  • HHQ_Mean.Score - Mean HHQ pain score (pain factors 1:13).
  • HHQ_Sum.Score - Summation of HHQ pain factors 1:13
  • HHQ_Health_Status.Factor - Ordered categorical factor (sumation of recent event factors)
HHQraw.df<-HHQraw.df %>% mutate(HHQ_Mean.Score=rowMeans(HHQraw.df[,7:20], na.rm = TRUE))
HHQraw.df<-HHQraw.df %>% mutate(HHQ_Sum.Score=rowSums(HHQraw.df[,7:20], na.rm = TRUE))

 

HHQ Caffeine Consumption -

history_cafe_history - 25). How many 8oz. cups of regular coffee do you have daily?
history_tea_history - 26). How many 8oz. cups of tea do you have daily?
history_soft_history - 27). How many 8oz. caffeinated soft drinks do you have daily?

Entered as Missing Data...

20045

Recode entries reported as...

1. Missing/Performed test incorrectly -

Response includes "decaf"... 

Melted.Caffeine.df$Notes<-if_else(str_detect(Melted.Caffeine.df$value,"decaf"), "*decaf*, recoded as 0",as.character(Melted.Caffeine.df$Notes))

Melted.Caffeine.df$value<-if_else(str_detect(Melted.Caffeine.df$value,"decaf"), "0",as.character(Melted.Caffeine.df$value))

"1 in winter" 

Melted.Caffeine.df$Notes<-if_else(str_detect(Melted.Caffeine.df$value,"1 in winter"), "==1 in winter, recoded as NA",as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(str_detect(Melted.Caffeine.df$value,"1 in winter"), "NA",as.character(Melted.Caffeine.df$value))

"occasionally" 

Melted.Caffeine.df$Notes<-if_else(str_detect(Melted.Caffeine.df$value,"occasionally"), "occasionally, recoded as NA",as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(str_detect(Melted.Caffeine.df$value,"occasionally"), "NA",as.character(Melted.Caffeine.df$value))

"---" 

Melted.Caffeine.df$Notes<-if_else(str_detect(Melted.Caffeine.df$value,"---"), "---, recoded as NA",as.character(Melted.Caffeine.df$Notes))

Melted.Caffeine.df$value<-if_else(str_detect(Melted.Caffeine.df$value,"---"), "NA",as.character(Melted.Caffeine.df$value))

2. Translated as dates (not included in codebook...)

Entries translated as dates exp)
* "6-Feb"=="2-6"=>"4"
* "5-Apr"=="4-5"=>"4.5"
* "6-Apr"=="4-6"=>"5"
* "6-May"=="5-6"=> "5.5"
* "2-Jan"=="1-2"=>"1,5"
* "3-Feb"=="2-3"=>"2.5"
* "9-Aug"=="9-10"=>"9.5"
* "7-Jun" =="6-7"=>"6.5"
* "12-Oct"=="10-12"=>"11"
* "4-Feb"=="2-4"=>"3"
* "5-Mar"=="3-5"=>"4"
* "10-Jul"=="7-10"=>"8.5"

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("5-Feb") , "3",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("5-Feb") , "3",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("5-Apr") , "4.5",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("6-Feb") , "4",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("6-Apr") , "5",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("2-Jan") , "1.5",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("3-Feb") , "2.5",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("6-May") , "5.5",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("7-Jun") , "6.5",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("9-Aug") , "8.5",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("4-Mar") , "3.5",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("7-Jun") , "6.5",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("12-Oct") , "11",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("4-Feb") , "3",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("5-Mar") , "4",as.character(Melted.Caffeine.df$value))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==("10-Jul") , "8.5",as.character(Melted.Caffeine.df$value))

3. Subjective estimates/ranges...

Typed Text - 1/week, 2/week, etc. 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="1/week", "1/7, recoded as 0.14285",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="1/week", "0.14285",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="2/week", "2/7, recoded as 0.2857",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="2/week", "0.2857143",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="4/week", "4/7, recoded as 0.5714",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="4/week", "0.5714",as.character(Melted.Caffeine.df$value))

Typed Text - 0-1/wk, 1-2/wk, etc. 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="0-1/wk", "0.5/7, recoded as 0.07142857",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="0-1/wk", "0.07142857",
                                  as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="1-2/wk", "1.5/7, recoded as 0.2142857",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="1-2/wk", "0.2142857",
                                  as.character(Melted.Caffeine.df$value))


Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="1-2/week", "1.5/7, recoded as  0.2142857",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="1-2/week", " 0.2142857",
                                  as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="2-3/weeek", "2.5/7, recoded as 0.3571",
                                  as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="2-3/weeek", "0.3571",as.character(Melted.Caffeine.df$value))

Typed Text - 1-0, 1-2, etc. 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="0-2", "range, recoded as avg",as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="0-2", "1",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="0-1", "range, recoded as avg",as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="0-1", "0.5",as.character(Melted.Caffeine.df$value))

Typed Text - "24 oz 1 or 2x daily" 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="24 oz  1 or 2x daily"  , "recoded as 1.5*3" ,as.character(Melted.Caffeine.df$Notes))

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="24 oz  1 or 2x daily", "4.50" ,as.character(Melted.Caffeine.df$value))

Typed Text - "1, about 3x/week" 

Three_per_wk<-3/7
Three_per_wk<-as.character(Three_per_wk)
Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="1, about 3x/week"  , "subjective range, 3/7"   ,as.character(Melted.Caffeine.df$Notes))

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="1, about 3x/week", Three_per_wk ,as.character(Melted.Caffeine.df$value))

Typed Text - "2 -16 oz (so 4 8oz.)" 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="2  -16 oz (so 4  8oz.)", "typed text",as.character(Melted.Caffeine.df$Notes))        
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="2  -16 oz (so 4  8oz.)", "4",as.character(Melted.Caffeine.df$value))

4. Mathmatical expressions...

'>1' => 2 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value==">1", "mathmatical expression",as.character(Melted.Caffeine.df$Notes))

Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value==">1", "2",as.character(Melted.Caffeine.df$value))

'<1' => 1 

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="1 if any", "typed mathmatical expression",as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="1 if any", "1",as.character(Melted.Caffeine.df$value))

Melted.Caffeine.df$Notes<-if_else(Melted.Caffeine.df$value=="<1", "mathmatical expression",as.character(Melted.Caffeine.df$Notes))
Melted.Caffeine.df$value<-if_else(Melted.Caffeine.df$value=="<1", "1",as.character(Melted.Caffeine.df$value))

Creating Variables...

  • daily_caffeine - Summated caffeine variables.
Outliers- 10898, 30
  • Units: Daily 8oz cups.`

 

HHQ Alcohol Consumption

  • history_beer_history - 28). How many cans of beer (12 oz.) do you have in normal week?
  • history_wine_history - 29). How many glasses of wine (5 oz.) do have in a normal week?
  • history_liquor_history - 30). How many serving of liquor (1.5 oz. shot) do have in a normal week?

Entered as Missing Data...

No Missing Alcohol Demographics Data...

Recode entries reported as...

1. Subjective estimates/ranges...

Any ranges are averaged prior to calculating weekly intake

Typed text 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="4/week", "subjective estimate",as.character(Melted.ALC.df$Notes)) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="4/week", "4",as.character(Melted.ALC.df$value)) 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="none", "typed text",as.character(Melted.ALC.df$Notes)) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="none", "0",as.character(Melted.ALC.df$value))

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="n/a", "typed text",as.character(Melted.ALC.df$Notes)) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="n/a", "0",as.character(Melted.ALC.df$value))

Typed fraction 
 exp) "1/3 <1 glass per week"
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="1/3  <1 glass per week" , "typed text, recoded as 0.33333" ,as.character(Melted.ALC.df$Notes))
third_per_week<-1/3 
third_per_week<-as.character(third_per_week) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="1/3  <1 glass per week" , "0.33333" ,as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="0.5 or less", "0.5" ,as.character(Melted.ALC.df$value))

Subjective estimate- e.g. "1/month", "2/month", etc. 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="1/month" , "subjective range, 1/4" ,as.character(Melted.ALC.df$Notes))
One_per_mo<-1/4 
One_per_mo<-as.character(One_per_mo) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="1/month", One_per_mo ,as.character(Melted.ALC.df$value))

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="2/month" , "subjective range, 2/4" ,as.character(Melted.ALC.df$Notes))
Two_per_mo<-2/4 
Two_per_mo<-as.character(Two_per_mo) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="2/month", Two_per_mo ,as.character(Melted.ALC.df$value))

Subjective estimate- e.g. "1/every other week" 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="1/every other week" , "subjective range, 1/2" ,as.character(Melted.ALC.df$Notes))
half_per_wk<-1/2 
half_per_wk<-as.character(half_per_wk)
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="1/every other week", half_per_wk ,as.character(Melted.ALC.df$value))

Subjective range- e.g. "1-2/month" 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="1-2/month" , "subjective range (1.5/4), recoded as 0.375" ,as.character(Melted.ALC.df$Notes))
One.5_per_mo<-1.5/4 
One.5_per_mo<-as.character(One.5_per_mo) 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="1-2/month", One.5_per_mo ,as.character(Melted.ALC.df$value))

Subjective range- Narrow spread between range- e.g. "0-1" 

Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="0-1", "0.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="1-2", "1.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="2-3", "2.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="3-4", "3.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="4-5", "4.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="5-6", "4.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="6-7", "6.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="7-8", "6.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="8-9", "8.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="9-10", "8.5",as.character(Melted.ALC.df$value))

# Response includes string of text...
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="1-2/week", "range",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="1-2/week", "1.5",as.character(Melted.ALC.df$value))

Subjective range- Moderate spread between integers- e.g. "0-2" 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="0-2", "range",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="2-4", "range",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="3-5", "range",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="4-6", "range",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="10-12", "range",as.character(Melted.ALC.df$Notes))

Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="0-2", "1",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="2-4", "3",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="3-5", "4",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="4-6", "5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="10-12", "11",as.character(Melted.ALC.df$value))

Subjective range- Large spread between integers- e.g. "2-6 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="7-10", "range",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="2-6", "range",as.character(Melted.ALC.df$Notes))

Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="7-10", "8.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="2-6", "4",as.character(Melted.ALC.df$value))

2. Translated as dates (not included in codebook...)
Entries translated as dates 
 exp)
  * "6-May"=="5-6"=> "5.5"  
  * "2-Jan"=="1-2"=>"1,5"  
  * "3-Feb"=="2-3"=>"2.5"  
  * "9-Aug"=="9-10"=>"9.5"  
  * "7-Jun" =="6-7"=>"6.5"  
  * "12-Oct"=="10-12"=>"11"  
  * "4-Feb"=="2-4"=>"3"  
  * "5-Mar"=="3-5"=>"4"  
  * "10-Jul"=="7-10"=>"8.5" 
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("2-Jan") , "1.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("3-Feb") , "2.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("6-May") , "5.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("7-Jun") , "6.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("9-Aug") , "8.5",as.character(Melted.ALC.df$value))

Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("4-Mar") , "3.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("7-Jun") , "6.5",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("12-Oct") , "11",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("4-Feb") , "3",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("5-Mar") , "4",as.character(Melted.ALC.df$value))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==("10-Jul") , "8.5",as.character(Melted.ALC.df$value))

3. Mathmatical expressions...

'>1' => 2 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value==">1", "mathmatical expression",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value==">1", "2",as.character(Melted.ALC.df$value))

'<1'. => 1. 

Melted.ALC.df$Notes<-if_else(Melted.ALC.df$value=="<1", "mathmatical expression",as.character(Melted.ALC.df$Notes))
Melted.ALC.df$value<-if_else(Melted.ALC.df$value=="<1", "1",as.character(Melted.ALC.df$value))

Creating Variables...

  • Weekly_alc.score - Continuous estimate.
  • Heavy_Drinker - (1/0) 14 drinks per week for men and seven per week for women.

Continuous Variable 'Weekly_alc.score'

Binary Variable 'Heavy_Drinker' 
 The national average was 17 per week. The Centers for Disease Control defines heavy drinking as 14 drinks per week for men and seven per week for women. A standard drink is defined as 12 ounces of beer, 5 ounces of win or 1.5 ounces of liquor. 
ALC.df$Heavy_Drinker<-if_else(ALC.df$screen_gender==2 & ALC.df$Weekly_alc.score>=7, "Heavy", " ")
ALC.df$Heavy_Drinker<-if_else(ALC.df$screen_gender==1 & ALC.df$Weekly_alc.score>=14, "Heavy",as.character(ALC.df$Heavy_Drinker) )
ALC.df$Heavy_Drinker<-if_else(ALC.df$Heavy_Drinker=="Heavy", 1, 0 )
df<-ALC.df %>% select(record_id,Site, screen_gender,Weekly_alc.score, Heavy_Drinker)
library(nVennR)

df$Male<-if_else(df$screen_gender  =="1"  ,1,0)
df$Female<-if_else(df$screen_gender  =="2"  ,1,0)
male<- subset(df, Male == "1")$record_id
female <- subset(df, Female == "1")$record_id
heavy_drinker <- subset(df, Heavy_Drinker == "1")$record_id

df %$%  ctable(screen_gender,Heavy_Drinker,chisq = TRUE, OR = TRUE, RR=TRUE, headings = FALSE) %>% print(method = "render")
Heavy_Drinker
screen_gender 0 1 Total
1 124 ( 87.3% ) 18 ( 12.7% ) 142 ( 100.0% )
2 313 ( 87.4% ) 45 ( 12.6% ) 358 ( 100.0% )
Total 437 ( 87.4% ) 63 ( 12.6% ) 500 ( 100.0% )
 Χ2 = 0.0000   df = 1   p = 1.0000
O.R. (95% C.I.) = 0.99  (0.55 - 1.78)
R.R. (95% C.I.) = 1.00  (0.93 - 1.08)

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ven<-plotVenn(list(Male=male, Female=female,  Heavy_Drinker=heavy_drinker), nCycles = 2000,labelRegions=T, showPlot = T)
str <- charToRaw(ven$svg)
rsvg::rsvg_png(str, file = '~/HeavyDrink_Gender_venn.png')
df$Site<-as.factor(df$Site)
df$Site<-as.character(df$Site)
df$UPitt<-if_else(df$Site  =="PITT"  ,1,0)
df$Kansas<-if_else(df$Site  =="KU"  ,1,0)
df$Northeastern<-if_else(df$Site  =="NEU"  ,1,0)
upitt<- subset(df, UPitt == "1")$record_id
ku<- subset(df, Kansas == "1")$record_id
neu<- subset(df, Northeastern == "1")$record_id
heavy_drinker <- subset(df, Heavy_Drinker == "1")$record_id
df$Site<-as.factor(df$Site)
df$heavy_drinker<-as.factor(df$Heavy_Drinker)
df %$%  ctable(Site,Heavy_Drinker,chisq = TRUE, OR = TRUE, RR=TRUE, headings = FALSE) %>% print(method = "render")
Heavy_Drinker
Site 0 1 Total
KU 153 ( 85.5% ) 26 ( 14.5% ) 179 ( 100.0% )
NEU 119 ( 86.2% ) 19 ( 13.8% ) 138 ( 100.0% )
PITT 165 ( 90.2% ) 18 ( 9.8% ) 183 ( 100.0% )
Total 437 ( 87.4% ) 63 ( 12.6% ) 500 ( 100.0% )
 Χ2 = 2.0429   df = 2   p = .3601

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ven<-plotVenn(list( Kansas=ku,UPitt=upitt,Heavy_Drinker=heavy_drinker,Northeastern=neu), nCycles = 20000,labelRegions=T, showPlot = T)
str <- charToRaw(ven$svg)
rsvg::rsvg_png(str, file = '~/HeavyDrink_site_venn.png')

Consumption groups 'Weekly_alc.group' 
 0, 1-3, 3+ 
ALC.df$Weekly_alc.group<-if_else(ALC.df$Weekly_alc.score==0, "none", as.character(ALC.df$Weekly_alc.score))

ALC.df$Weekly_alc.group<-if_else(ALC.df$Weekly_alc.score>0 &ALC.df$Weekly_alc.score<=3 , "1-3", as.character(ALC.df$Weekly_alc.group))

ALC.df$Weekly_alc.group<-if_else(ALC.df$Weekly_alc.score>3  , "3+", as.character(ALC.df$Weekly_alc.group))

ALC.df$Weekly_alc.group<-factor(ALC.df$Weekly_alc.group, levels=c("none", "1-3", "3+" ))

Caffeine.df$record_id<-as.character(Caffeine.df$record_id)
Alc_Caff<-left_join(ALC.df, Caffeine.df)


Outliers-
  • Greater than 2SD Site Avg: 46
  • Greater than 3SD Site Avg: 21
    • PITT 6
    • KU 8
    • NEU 7

 

HHQ Smoking Demographics

  • history_cig_history - 31). Do you currently smoke Cigarettes?
  • smoke_day - 31A) How many packs do you smoke per day?
  • yrs_smoke - 31B) For how many years have you smoked?
  • prior_smoke - 32) Did you previously smoke cigarettes, but quit?
  • packs_prior - 32A) How many packs did you previously smoke per day (on average)?
  • prior_smoke_yrs - 32B) For how many years did you smoke?
  • smoke_other - 33) Do you use any other forms of tobacco (Cigars, vaporizers, etc)?
  • other_type - 33A) Quantify how much of each kind you use, on average (ex. 1 cigar / month etc.)

Entered as Missing Data...

c(10151, 10499)

Missing secondary smoking demographics...

10136 missing prior.smoke.yrs
prior packs smoked complete
smoke_day complete
years smoked complete

Recode entries...

1. Missing/Performed Test Incorrectly -

Currently uses non-inhaled tabaccoo/nicotine products... 

#Smokes.df %>% filter(smoke_other!=0) %>%select(smoke_other, other_type)
Smokes.df$smoke_other<-if_else(Smokes.df$other_type=="nicorette", as.integer(0) , as.integer(Smokes.df$smoke_other))
#Smokes.df %>% filter(smoke_other!=0) %>%select(smoke_other, other_type)
Smokes.df$packs_prior<-if_else(Smokes.df$packs_prior=="10", "0.6",Smokes.df$packs_prior)

prior_smoke.factor (eval=FALSE,for now...) 

Smokes.df$prior_smoke<-as.character(Smokes.df$prior_smoke)
Smokes.df<-Smokes.df %>%  
  mutate("prior_smoke"= if_else(history_cig_history==1 & 
                             is.na(prior_smoke)==TRUE, 
                           "0", as.character(Smokes.df$prior_smoke)))
Smokes.df$prior_smoke<-as.numeric(Smokes.df$prior_smoke)

2. Subjective estimates/ranges...

Typed text - variable 

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("1 1/2") , "fraction",as.character(melted.Smokes.df$Notes))
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("1 1/2") , "1.5",as.character(melted.Smokes.df$value))

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("1-1.5") , "range",as.character(melted.Smokes.df$Notes))
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("1-1.5") , "1.25",as.character(melted.Smokes.df$value))

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("1 pack per day") , "typed text", as.character(melted.Smokes.df$Notes))
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("1 pack per day") , "1",as.character(melted.Smokes.df$value))

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("1 cigarette") , "typed text/subjective estimate",as.character(melted.Smokes.df$Notes))
cig_day<-1/20
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("1 cigarette") , as.character(cig_day),as.character(melted.Smokes.df$value))

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("about 3 cigarettes/day") , "typed text/subjective range",as.character(melted.Smokes.df$Notes))
cig3_day<-3/20
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("about 3 cigarettes/day") , as.character(cig3_day),as.character(melted.Smokes.df$value))

3. Translated as dates (not included in codebook...)

Translated as dates... 

melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("2-Jan") , "1.5",as.character(melted.Smokes.df$value))

4. Mathmatical expressions...

Typed text - Mathmatical expression 

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("social smoker, less than 1 pack per week") , "typed text/mathmatical expression",as.character(melted.Smokes.df$Notes))
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("social smoker, less than 1 pack per week") , "1",as.character(melted.Smokes.df$value))

'<1' => 1 

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==("<1") , "mathmatical expression",as.character(melted.Smokes.df$Notes))
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==("<1") , "1",as.character(melted.Smokes.df$value))

'>1' => 2 

melted.Smokes.df$Notes<-if_else(melted.Smokes.df$value==(">1") , "mathmatical expression",as.character(melted.Smokes.df$Notes))
melted.Smokes.df$value<-if_else(melted.Smokes.df$value==(">1") , "2",as.character(melted.Smokes.df$value))

Creating Variables...

  • Smoking.Status - (Current/Former/Never) Current includes primary inhaled alternative users.
  • Smoking.Status_unc - (Current/Former/Never) Primary inhaled alternative users are classified as "Never".
  • Current.Smoke.factor - Currently smokes inhaled tabacco/nicotine.
  • Primary.AltSmoke.factor - No history of cigarette smoking, currently uses alternative inhaled tabacco/nicotine (vape, cigars, etc.).
  • Secondary.AltSmoke.factor - Former or Current cigarette smoker, now uses alternative inhaled tabacco/nicotine.
  • pack_day - packs per day (former or current).
  • yrs_smoke - Years smoked (former or current).

 

'Uncorrected Smoking.Status' - Not inluding alternative tabbacco/nicotine users 

Smokes.df$Smoking.Status<-if_else(Smokes.df$history_cig_history=="1" &
                                    c(Smokes.df$prior_smoke=="0"| is.na(Smokes.df$prior_smoke)), 
                                "Current","")
Smokes.df$Smoking.Status<-if_else(Smokes.df$history_cig_history=="0" & 
                                    Smokes.df$prior_smoke=="1", 
                                  "Former", as.character(Smokes.df$Smoking.Status))

Smokes.df$Smoking.Status<-if_else(Smokes.df$history_cig_history=="0" & 
                                    Smokes.df$prior_smoke=="0", 
                                  "Never",as.character(Smokes.df$Smoking.Status))

Smokes.df$Smoking.Status_unc<-Smokes.df$Smoking.Status

Smoking.Status
Site Current Former Never <NA> Total
KU 5 ( 2.8% ) 61 ( 34.1% ) 113 ( 63.1% ) 0 ( 0.0% ) 179 ( 100.0% )
NEU 4 ( 2.9% ) 63 ( 45.7% ) 71 ( 51.4% ) 0 ( 0.0% ) 138 ( 100.0% )
PITT 3 ( 1.6% ) 92 ( 50.3% ) 87 ( 47.5% ) 1 ( 0.5% ) 183 ( 100.0% )
Total 12 ( 2.4% ) 216 ( 43.2% ) 271 ( 54.2% ) 1 ( 0.2% ) 500 ( 100.0% )
 Χ2 = 10.7784   df = 4   p = .0292

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

Smoking.Status
screen_gender Current Former Never Total
Female 11 ( 3.1% ) 140 ( 39.2% ) 206 ( 57.7% ) 357 ( 100.0% )
Male 1 ( 0.7% ) 76 ( 53.5% ) 65 ( 45.8% ) 142 ( 100.0% )
Total 12 ( 2.4% ) 216 ( 43.3% ) 271 ( 54.3% ) 499 ( 100.0% )
 Χ2 = 9.8515   df = 2   p = .0073

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

Uncorrected Binary - 'Current.Smoke.factor' 

Smokes_1<-Smokes.df %>% group_by(Site) %>% add_count(Site)

Smokes_1$Current.Smoke.factor<-if_else(Smokes_1$history_cig_history=="1", "1","0")
Smokes_1$Current.Smoke.factor<-as.factor(Smokes_1$Current.Smoke.factor)

Smokes_1$Site_N<-Smokes_1$n
Smokes_1<-Smokes_1 %>% select(-n)
Smokes_1<-Smokes_1 %>% group_by(Site) %>% add_count(Current.Smoke.factor)
Smokes_1$sample_perc<-Smokes_1$n/nrow(Smokes_1)*100
Smokes_1$site_perc<-Smokes_1$n/Smokes_1$Site_N*100

Smokes_1 %$%  ctable(Site,Current.Smoke.factor,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
Current.Smoke.factor
Site 0 1 Total
KU 174 ( 97.2% ) 5 ( 2.8% ) 179 ( 100.0% )
NEU 134 ( 97.1% ) 4 ( 2.9% ) 138 ( 100.0% )
PITT 180 ( 98.4% ) 3 ( 1.6% ) 183 ( 100.0% )
Total 488 ( 97.6% ) 12 ( 2.4% ) 500 ( 100.0% )
 Χ2 = 0.7167   df = 2   p = .6988

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ggplot(Smokes_1, aes(x = as.factor(Site), y=n, fill=Current.Smoke.factor)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=paste(n, " / ",round(sample_perc ,1.8), "%"," \n", round(site_perc, 1), "%",  sep="")), position = position_dodge(.9),colour="black", size=2.5)+  ylab("")+xlab("")+theme(legend.position = "bottom")

gender.df<-HHQraw.df %>% select(record_id, screen_gender)
gender.df$record_id<-as.character(gender.df$record_id)
plot.df<-left_join(gender.df,Smokes.df)
plot.df<-plot.df[complete.cases(plot.df$screen_gender),]
plot.df<-plot.df[complete.cases(plot.df$Smoking.Status),]

plot.df<-plot.df %>% group_by(screen_gender) %>% add_count(screen_gender)
plot.df$gender_n<-plot.df$n
plot.df<-plot.df %>% select(-n)

plot.df$Current.Smoke.factor<-if_else(plot.df$Smoking.Status=="Current", "Tabbacco User", "Non User")
plot.df$Current.Smoke.factor<-as.factor(plot.df$Current.Smoke.factor)
plot.df<-plot.df %>% group_by(screen_gender) %>% add_count(Current.Smoke.factor)
plot.df$sample_perc<-plot.df$n/nrow(plot.df)*100
plot.df$site_perc<-plot.df$n/plot.df$gender_n*100
plot.df$screen_gender<-if_else(plot.df$screen_gender==1, "Male","Female")
#plot.df$screen_gender<-as.factor(plot.df$screen_gender)
plot.df %$%  ctable(screen_gender,Current.Smoke.factor,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
Current.Smoke.factor
screen_gender Non User Tabbacco
User		 Total
Female 346 ( 96.9% ) 11 ( 3.1% ) 357 ( 100.0% )
Male 141 ( 99.3% ) 1 ( 0.7% ) 142 ( 100.0% )
Total 487 ( 97.6% ) 12 ( 2.4% ) 499 ( 100.0% )
 Χ2 = 1.5378   df = 1   p = .2149
O.R. (95% C.I.) = 0.22  (0.029 - 1.74)
R.R. (95% C.I.) = 0.98  (0.95 - 1.00)

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ggplot(plot.df, aes(x =as.factor(screen_gender) ,y=n, fill=Current.Smoke.factor)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=paste(n, " / ",round(sample_perc ,2), "%"," \n", round(site_perc, 1), "%",  sep="")), position = position_dodge(.9),colour="black", size=2.2)+ 
  ylab("")+xlab("")+theme(legend.position = "bottom")

former<-Smokes.df%>%filter( prior_smoke ==1)

Inhaled Smoking Alternatives 

Smokes.df$Primary_Alternative_User<-if_else(Smokes.df$prior_smoke=="0" & 
                                          Smokes.df$history_cig_history=="0" &
                                          Smokes.df$smoke_other=="1", 1,0)
Smokes.df$Primary_Alternative_User<-if_else(is.na(Smokes.df$Primary_Alternative_User==TRUE), "0",as.character(Smokes.df$Primary_Alternative_User))

Smokes.df$Primary_Alternative_User<-as.character(Smokes.df$Primary_Alternative_User)

Smokes.df$Secondary_Alternative_User<-if_else((Smokes.df$smoke_other=="1" & 
                                      Smokes.df$Primary_Alternative_User!="1"), 1, 0)
Smokes.df$Secondary_Alternative_User<-if_else(is.na(Smokes.df$Secondary_Alternative_User==TRUE), "0",as.character(Smokes.df$Secondary_Alternative_User))

Smokes.df$alt_smoke.factor<-if_else(Smokes.df$Primary_Alternative_User=="1"|
                                      Smokes.df$Secondary_Alternative_User=="1"|
                                     Smokes.df$smoke_other==1 , "1", "0")

Smokes.df$alt_smoke.factor<-if_else(is.na(Smokes.df$alt_smoke.factor==TRUE), "0",Smokes.df$alt_smoke.factor)

Smokes.df$alt_smoke.factor<-as.factor(Smokes.df$alt_smoke.factor)

alt.df<-Smokes.df %>% filter(smoke_other==1)
alt.df<-left_join(alt.df,gender.df)
alt.df$screen_gender<-if_else(alt.df$screen_gender==1, "Male","Female")
alt.df$screen_gender<-as.factor(alt.df$screen_gender)

#alt.df %>% select(Smoking.Status_unc,alt_smoke.factor, Site, screen_gender , prior_smoke_yrs,packs_prior,other_type) %>% DT::datatable(rownames=FALSE,options = list(pageLength = 13))

'Corrected Smoking.Status' - All inhaled tabaccoo/nicotine products 

Smokes.df$smoke_other<-as.character(Smokes.df$smoke_other)
Smokes.df$Smoking.Status<-if_else(Smokes.df$smoke_other=="1" & !is.na(Smokes.df$smoke_other), "Current",Smokes.df$Smoking.Status_unc)
Smokes.df$Current.Smoke.factor<-if_else(Smokes.df$Smoking.Status=="Current", 1,0)

Smoking.Status
Site Current Former Never <NA> Total
KU 9 ( 5.0% ) 59 ( 33.0% ) 111 ( 62.0% ) 0 ( 0.0% ) 179 ( 100.0% )
NEU 5 ( 3.6% ) 62 ( 44.9% ) 71 ( 51.4% ) 0 ( 0.0% ) 138 ( 100.0% )
PITT 10 ( 5.5% ) 86 ( 47.0% ) 86 ( 47.0% ) 1 ( 0.5% ) 183 ( 100.0% )
Total 24 ( 4.8% ) 207 ( 41.4% ) 268 ( 53.6% ) 1 ( 0.2% ) 500 ( 100.0% )
 Χ2 = 9.4292   df = 4   p = .0512

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

Smoking.Status
screen_gender Current Former Never Total
Female 13 ( 3.6% ) 138 ( 38.7% ) 206 ( 57.7% ) 357 ( 100.0% )
Male 11 ( 7.7% ) 69 ( 48.6% ) 62 ( 43.7% ) 142 ( 100.0% )
Total 24 ( 4.8% ) 207 ( 41.5% ) 268 ( 53.7% ) 499 ( 100.0% )
 Χ2 = 9.7065   df = 2   p = .0078

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

'Corrected Binary' - 'Current.Smoke.factor'
Current.Smoke.factor
Site 0 1 <NA> Total
KU 170 ( 95.0% ) 9 ( 5.0% ) 0 ( 0.0% ) 179 ( 100.0% )
NEU 133 ( 96.4% ) 5 ( 3.6% ) 0 ( 0.0% ) 138 ( 100.0% )
PITT 172 ( 94.0% ) 10 ( 5.5% ) 1 ( 0.5% ) 183 ( 100.0% )
Total 475 ( 95.0% ) 24 ( 4.8% ) 1 ( 0.2% ) 500 ( 100.0% )
 Χ2 = 0.6294   df = 2   p = .7300

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

gender.df<-HHQraw.df %>% select(record_id, screen_gender)
gender.df$record_id<-as.character(gender.df$record_id)
plot.df<-left_join(gender.df,Smokes.df)
plot.df<-plot.df[complete.cases(plot.df$screen_gender),]
plot.df<-plot.df[complete.cases(plot.df$Smoking.Status),]

plot.df<-plot.df %>% group_by(screen_gender) %>% add_count(screen_gender)
plot.df$gender_n<-plot.df$n
plot.df<-plot.df %>% select(-n)
plot.df$Current.Smoke.factor<-as.factor(plot.df$Current.Smoke.factor)
plot.df<-plot.df %>% group_by(screen_gender) %>% add_count(Current.Smoke.factor)
plot.df$sample_perc<-plot.df$n/nrow(plot.df)*100
plot.df$site_perc<-plot.df$n/plot.df$gender_n*100
plot.df$screen_gender<-if_else(plot.df$screen_gender==1, "Male","Female")
#plot.df$screen_gender<-as.factor(plot.df$screen_gender)
plot.df %$%  ctable(screen_gender,Current.Smoke.factor,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
Current.Smoke.factor
screen_gender 0 1 Total
Female 344 ( 96.4% ) 13 ( 3.6% ) 357 ( 100.0% )
Male 131 ( 92.3% ) 11 ( 7.7% ) 142 ( 100.0% )
Total 475 ( 95.2% ) 24 ( 4.8% ) 499 ( 100.0% )
 Χ2 = 2.8964   df = 1   p = .0888
O.R. (95% C.I.) = 2.22  (0.97 - 5.08)
R.R. (95% C.I.) = 1.04  (0.99 - 1.10)

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ggplot(plot.df, aes(x =as.factor(screen_gender) ,y=n, fill=Current.Smoke.factor)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=paste(n, " / ",round(sample_perc ,2), "%"," \n", round(site_perc, 1), "%",  sep="")), position = position_dodge(.9),colour="black", size=2.2)+ 
  ylab("")+xlab("")+labs(title="Current Smokers (including inhaled alternatives)")+theme(legend.position = "bottom")

Smokes.df$prior_smoke<-if_else(Smokes.df$history_cig_history==1 & is.na(Smokes.df$prior_smoke),"0",as.character(Smokes.df$prior_smoke ))

Smokes.df$smoke_other<-if_else(is.na(Smokes.df$smoke_other) & Smokes.df$other_type=="", "0", as.character(Smokes.df$smoke_other))

Smokes.df$smoke_day<-as.numeric(Smokes.df$smoke_day)
Smokes.df$packs_prior<-as.numeric(Smokes.df$packs_prior)
Smokes.df$prior_smoke_yrs<-as.numeric(Smokes.df$prior_smoke_yrs)
Smokes.df$yrs_smoke<-as.numeric(Smokes.df$yrs_smoke)

Former Smoker Demographics 
 
Average packs per day:1.0981111
Median prior packs per day:1

Average number of years smoked:16.814338
Median number of years Smoked:15

Current Smoker Demographics 
 
Average packs per day:0.00803
Median prior packs per day:0

Average number of years smoked:0.918
Median number of years Smoked:0
current<-Smokes.df%>%filter(history_cig_history==1)
gender.df<-HHQraw.df %>% select(record_id, screen_gender)
gender.df$record_id<-as.character(gender.df$record_id)
current<-left_join(current,gender.df, by="record_id")
current$gender<-if_else(current$screen_gender==1, "Male", "Female")
current$gender<-as.factor(current$gender)

current_1<-current %>% group_by(Site) %>% add_count(Site)

current_1<-current_1 %>%
  group_by(Site, add=TRUE) %>% 
  add_count(smoke_other)
current_1$sample_perc<-current_1$nn/nrow(current_1)*100
current_1$bar_perc<-current_1$nn/current_1$n*100

ggplot(current_1, aes(x = as.factor(Site),y=n, fill=as.factor(smoke_other))) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=paste(nn, " / ",round(sample_perc ,2), "%"," \n", round(bar_perc, 1), "%",  sep="")), position = position_dodge(.9),colour="black", size=2)+ 
  ylab("")+xlab("")+labs(title="Current Cigarette User")

 

HHQ Education

  • 'educ' - Years of education completed and degrees earned?
edu<-HHQraw.df %>% select(record_id,educ )
edu$Notes<-""
HHQraw.df %>% filter(is.na(HHQraw.df$educ))%>% select(record_id)
## [1] record_id
## <0 rows> (or 0-length row.names)

HHQ Mother Education

  • 'educ_mother' - 35. Years of education completed and degrees earned by mother?
m_edu<-HHQraw.df %>% select(record_id,educ_mother )
m_edu$educ_mother_was<-HHQraw.df$educ_mother
m_edu$Notes<-""
Entered as Missing Values...

c(10013, 10030, 10123, 10297, 10599, 10606, 10922, 20315, 20394, 20396, 20399, 30550, 30879, 30892, 30953, 31109)

Recode entries...

 

1. Missing Values/Performed Test Incorrectly -

"Unknown" 

m_edu$Notes<-if_else(str_detect(m_edu$educ_mother,"Unknown")==TRUE, "==Unknown, recoded as NA", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_detect(m_edu$educ_mother,"Unknown")==TRUE, "NA", as.character(m_edu$educ_mother))

"Adopted" Assumes parent education is purely genetic in nature..
Go back and check pt form to ensure no more info to include...

m_edu$Notes<-if_else(str_detect(m_edu$educ_mother,"adopted"), "Participant adopted, recoded as NA", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_detect(m_edu$educ_mother,"adopted")==TRUE, "NA", as.character(m_edu$educ_mother))

"Grammar school" 

m_edu$Notes<-if_else(str_detect(m_edu$educ_mother,"Grammar school")==TRUE, "==Grammar School, recoded as NA", as.character(m_edu$Notes))

m_edu$educ_mother<-if_else(str_detect(m_edu$educ_mother,"Grammar school")==TRUE, "NA", as.character(m_edu$educ_mother))

2. Automated Recoding

Assumed Responses -str_starts()) 

m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "4"), "4", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "5"), "5", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "6"), "6", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "7"), "7", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "8"), "8", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "9"), "9", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "10"), "10", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "11"), "11", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "12"), "12", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "13"), "13", as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "14"), "14",as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "15"), "15",as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "16"),"16",as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "17"),"17",as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "18"),"18",as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "19"),"19",as.character(m_edu$educ_mother))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "20"),"20",as.character(m_edu$educ_mother))

3. Manually Recode..

Zero Responses 

m_edu$Notes<-if_else(str_starts(m_edu$educ_mother, "None "), "None*, recoded as 0", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "None "), "0", as.character(m_edu$educ_mother))

m_edu$Notes<-if_else(str_starts(m_edu$educ_mother, "0 "), "0*, recoded as 0", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_starts(m_edu$educ_mother, "0 "), "0", as.character(m_edu$educ_mother))

Particpant Approximations -str_starts(x, pattern="~")) 

m_edu$Notes<-if_else(str_detect(m_edu$educ_mother, "10 yrs"), "10 yrs, recoded as 10", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_detect(m_edu$educ_mother, "10 yrs"),"10",as.character(m_edu$educ_mother))

Typed Text Responses 

m_edu$Notes<-if_else(str_detect(m_edu$educ_mother, "Masters"), "*Masters*, recoded as 14", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_detect(m_edu$educ_mother, "Masters"),"14",as.character(m_edu$educ_mother))

Remaining Manual Revisions -str_starts(x, pattern="~")) 

m_edu$Notes<-if_else(str_detect(m_edu$educ_mother,"H.S., 12"), "==H.S., recoded as 12", as.character(m_edu$Notes))
m_edu$educ_mother<-if_else(str_detect(m_edu$educ_mother, "H.S., 12"),"12",as.character(m_edu$educ_mother))

 

HHQ Recent Health Events...

  • 'history_recent_ill_specify'- Have you had any recent illness?
  • 'history_recent_hospital'- Have you recently been hospitalized?
  • 'history_recent_surgery'- Have you recently had any surgical procedures?

Entered as Missing Data...

illness: character(0) hospitalized: character(0) surgery: 10515

Chronic Illnesses -
Recent Illness -

Particpant specified within past 6 mo. or currently has...

Particpant specified within past 3 mo. or currently has... 

ill.df$history_recent_ill_TemporalScope_3<-
  if_else(str_detect(ill.df$history_recent_ill_specify, regex("currently has|at present|sorenes|just getting over it now|last week|last month|weeks ago|3 mo ago|a month ago|one month ago|~1 month ago|About 1 month ago|2 months ago|days ago|Currently has|1 month ago|3 months ago|1 week ago|this week|mo. ago|week ago|About 1 month ago|3 mo ago|at present|last week|currently")),"1", as.character(ill.df$history_recent_ill_specify))


#Code for TemporalScope using COG1 date & information provided... 


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-03-26" & str_detect(ill.df$history_recent_ill_specify, "end of December"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-04-01" & str_detect(ill.df$history_recent_ill_specify, "03/18"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-09-10" & str_detect(ill.df$history_recent_ill_specify, "August 25th"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))




ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-01-28" & str_detect(ill.df$history_recent_ill_specify, "Dec '18"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-10-23" & str_detect(ill.df$history_recent_ill_specify, "July 2018"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-01-10" & str_detect(ill.df$history_recent_ill_specify, "Jan 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2020-01-15" & str_detect(ill.df$history_recent_ill_specify, "Flu in Dec"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-01-10" & str_detect(ill.df$history_recent_ill_specify, "Jan 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-03-26" & str_detect(ill.df$history_recent_ill_specify, "December"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-02-05" & str_detect(ill.df$history_recent_ill_specify, "12/20-12/26"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-01-14" & str_detect(ill.df$history_recent_ill_specify, "November '18"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-06-26" & str_detect(ill.df$history_recent_ill_specify, "in April"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-05-20" & str_detect(ill.df$history_recent_ill_specify, "April 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-06-18" & str_detect(ill.df$history_recent_ill_specify, "June 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-05-30" & str_detect(ill.df$history_recent_ill_specify, "Pneumonia 2/19"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-10-23" & str_detect(ill.df$history_recent_ill_specify, "July 2018"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-07-31" & str_detect(ill.df$history_recent_ill_specify, "June 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2020-01-15" & ill.df$history_recent_ill_specify=="Flu in Dec - all good now.", "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-04-30" & str_detect(ill.df$history_recent_ill_specify,"March 2019") , "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-04-01" & ill.df$history_recent_ill_specify=="Head cold on 03/18 for 8 days - diarrhea, cold, fatigue - good now.","1" ,as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-04-01" & ill.df$history_recent_ill_specify=="Head cold on 03/18 for 8 days - diarrhea, cold, fatigue - good now.","1" ,as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2017-09-12" & ill.df$history_recent_ill_specify=="Cold over July 4th weekend, congestion", "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-01-28" & ill.df$history_recent_ill_specify=="Head cold- no longer had (end of Dec '18)", "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-05-28" & str_detect(ill.df$history_recent_ill_specify,"April 5th"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-08-28" & str_detect(ill.df$history_recent_ill_specify,"July 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-09-10" & str_detect(ill.df$history_recent_ill_specify,"August 25th"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-10-28" & ill.df$history_recent_ill_specify=="Surgery for liver cancer - Aug '19.", "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$Notes<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-10-28" & ill.df$history_recent_ill_specify=="Surgery for liver cancer - Aug '19.", "Chronic",as.character(ill.df$Notes))

ill.df$Notes<-if_else(str_detect(ill.df$history_recent_ill_specify,regex("liver cancer|lasted off and on|chronic|Chronic")), "Chronic",as.character(ill.df$Notes))

ill.df$Notes<-if_else(str_detect(ill.df$history_recent_surgery_s,regex("chemo treatment")), "Chronic",as.character(ill.df$Notes))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-03-26" & str_detect(ill.df$history_recent_ill_specify,"12/2018-2/2019"), "1",  as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-01-10" & str_detect(ill.df$history_recent_ill_specify,"Jan 2019"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$history_recent_ill_specify=="ive had a bad cold (flu) that has lasted off and on for 1 month, maybe an asthmatic reaction to it.", "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(str_detect(ill.df$history_recent_ill_specify,"gall stones and inflamed bile duct") & ill.df$Date.of..Cognitive.Session.1=="2018-04-10", "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-03-26" & str_detect(ill.df$history_recent_ill_TemporalScope_3,"12/2018"), "1", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-05-23" & str_detect(ill.df$history_recent_ill_specify, "November 2017"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-05-31" & str_detect(ill.df$history_recent_ill_specify, "12/18"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-05-31" & ill.df$history_recent_ill_specify=="12/18 bad cold- good now", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2017-12-13" & ill.df$history_recent_ill_specify=="Pneumonia - April", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-05-23" & ill.df$history_recent_ill_specify=="Flu- November 2017", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-03-01" & str_detect(ill.df$history_recent_ill_specify, "october 2018"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-04-30" & str_detect(ill.df$history_recent_ill_specify, "Common cold, December 2018"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-02-19" & ill.df$history_recent_ill_specify=="Skin cancer removed in July 2017", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-02-13" & ill.df$history_recent_ill_specify=="head cold- Sept. 2018", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-04-01" & ill.df$history_recent_ill_specify=="head cold- Sept. 2018", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-10-08" & ill.df$history_recent_ill_specify=="pneumonia April/May- all good now", "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-02-25" & str_detect(ill.df$history_recent_ill_specify, "October '18"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-08-28" & str_detect(ill.df$history_recent_ill_specify, "Jan 2019"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-10-04" & str_detect(ill.df$history_recent_ill_specify, "2/19"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-06-26" & str_detect(ill.df$history_recent_ill_specify, "April 2017"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-09-27" & str_detect(ill.df$history_recent_ill_specify, "March/April"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-09-23" & str_detect(ill.df$history_recent_ill_specify, "April"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-08-26" & str_detect(ill.df$history_recent_ill_specify, "Feb '19"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(str_detect(ill.df$history_recent_ill_specify, "4 months ago"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(str_detect(ill.df$history_recent_ill_specify, "6 months"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2020-01-03" & str_detect(ill.df$history_recent_ill_specify, "September"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-04-02" & str_detect(ill.df$history_recent_ill_specify, "Sept 2017"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))


### UNSURE IF SHOULD BE WITHIN OR OUTSIDE

# Roughly 3+ mo. ago
ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-12-09" & str_detect(ill.df$history_recent_ill_specify, "Common cold roughly 3"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2018-01-31" & str_detect(ill.df$history_recent_ill_specify, "early fall"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))


ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-06-19" & str_detect(ill.df$history_recent_ill_specify, "Feb/March"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))



ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$Date.of..Cognitive.Session.1=="2019-05-31" & str_detect(ill.df$history_recent_ill_specify, "12/18"), "Not Recent", as.character(ill.df$history_recent_ill_TemporalScope_3))
ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$history_recent_ill_TemporalScope_3=="Not Recent", "0", as.character(ill.df$history_recent_ill_TemporalScope_3))

ill.df$history_recent_ill_TemporalScope_3<-if_else(ill.df$history_recent_ill_TemporalScope_3!="1" & ill.df$history_recent_ill_TemporalScope_3!="0" , "", as.character(ill.df$history_recent_ill_TemporalScope_3))
ill.df$history_recent_ill_TemporalScope_3<-as.numeric(ill.df$history_recent_ill_TemporalScope_3)

Particpant specified illness resolved... 

ill.df$history_recent_ill_resolved<-
  if_else(str_detect(ill.df$history_recent_ill_specify, regex("good now|healthy now|clear now|ok now|went away|Went away|great now|resolved|OK now|recovered|fine now|cleared up|Good to exercise|good to go|All better now|all clear now|good to exercise|no problems|under control|Clear now|good after that|resloved|Recovered|feels better now|back in|clean bill of health|no longer had|Good to go now|Passed kidney stones|24 hr stomach bug roughly 2 months ago|moderate case of hives|roughly remembers|had|Cold, Dec-Jan 2019|9/10-9/14|Common cold ~3 months ago|Common cold ~1 month ago|Common cold roughly 1 month ago|Had the flu last month|gall bladder attack|Appendix removed Sept 2017|acute|Cold- about 3 mo ago|Cold over July 4th weekend, congestion|good now|8 days|removed|Had a bout|24 hr stomach bug roughly 2 months ago|Passed kidney stones|moderate case of hives|hospitalized a few hrs. Shingles/diarrhea - given prescription.")),"1", as.character(ill.df$history_recent_ill_specify))


ill.df$history_recent_ill_resolved<-
  if_else(str_detect(ill.df$history_recent_ill_specify, regex("not been resolved|just getting over it now|at present|this week|Currently has|currently|chronic cough that comes and goes|ive had a bad cold|at present|still having")),"0", as.character(ill.df$history_recent_ill_resolved))

ill.df$history_recent_ill_resolved<-
  if_else(str_detect(ill.df$history_recent_ill_specify,"Common cold in November '18") & ill.df$Date.of..Cognitive.Session.1=="2019-01-14" , "1", as.character(ill.df$history_recent_ill_resolved))

Recently hospitalized -

Type of hospital visit... 

ill.df$history_recent_hospital_Type<-if_else(str_detect(ill.df$history_recent_hospital_s, regex("surgery|Surgery|replacement|Replacement|Removed|removed|replaced|Removed|Replaced|Repair|repair")), "Surgery", "")

ill.df$history_recent_hospital_Type<-if_else(str_detect(ill.df$history_recent_hospital_s, regex("ER-|emergency room|ER trip|Accident|attack|Dehydration|for a few hours")), "ER",ill.df$history_recent_hospital_Type )

ill.df$history_recent_hospital_Type<-if_else(str_detect(ill.df$history_recent_hospital_s, regex("overnight|Overnight|Observation|observation|nights|Acute gastric ulcer|cancer|Infection due to knee surgery")), "Inpatient", ill.df$history_recent_hospital_Type)

ill.df$history_recent_hospital_Type<-if_else(str_detect(ill.df$history_recent_hospital_s, regex("Outpatient|outpatient|1 day surgery|Tooth extraction")), "Outpatient Surgery", ill.df$history_recent_hospital_Type)

ill.df$history_recent_hospital_Type<-if_else(ill.df$history_recent_hospital_Type=="Surgery", "Inpatient Surgery", ill.df$history_recent_hospital_Type)

ill.df$history_recent_hospital_Type<-as.factor(ill.df$history_recent_hospital_Type)
TMP<-ill.df
TMP$Site<-if_else(str_starts(TMP$record_id, "1"), "PITT",as.character(TMP$record_id))
TMP$Site<-if_else(str_starts(TMP$record_id, "2"), "KU",as.character(TMP$Site))
TMP$Site<-if_else(str_starts(TMP$record_id, "3"), "NEU",as.character(TMP$Site))
TMP$Site<-as.factor(TMP$Site)
TMP<- TMP %>% group_by(Site) %>% add_count(Site)
TMP<-TMP %>% group_by(Site) %>%add_count(history_recent_hospital_Type) %>%
  mutate(
    site_perc=(nn/n)*100,
    sample_perc=(nn/nrow(TMP))*100)
TMP %$%  ctable(Site,history_recent_hospital_Type,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
history_recent_hospital_Type
Site ER Inpatient Inpatient
Surgery		 Outpatient
Surgery		 <NA> Total
KU 1 ( 0.6% ) 3 ( 1.7% ) 1 ( 0.6% ) 0 ( 0.0% ) 174 ( 97.2% ) 179 ( 100.0% )
NEU 3 ( 2.2% ) 3 ( 2.2% ) 2 ( 1.4% ) 0 ( 0.0% ) 130 ( 94.2% ) 138 ( 100.0% )
PITT 6 ( 3.3% ) 3 ( 1.6% ) 1 ( 0.5% ) 3 ( 1.6% ) 170 ( 92.9% ) 183 ( 100.0% )
Total 10 ( 2.0% ) 9 ( 1.8% ) 4 ( 0.8% ) 3 ( 0.6% ) 474 ( 94.8% ) 500 ( 100.0% )
 Χ2 = 6.1450   df = 6   p = .4071

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ggplot(TMP, aes(x = Site ,y=nn, fill=history_recent_hospital_Type)) + 
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=paste(nn, " ", "%"," \n", round(site_perc, 2), "%",  sep="")), position = position_dodge(.9),colour="black", size=2)+ 
  ylab("")+labs()+theme(legend.position = "bottom")

TMP %$%  ctable(Site,history_recent_ill_resolved==0,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
history_recent_ill_resolved == 0
Site FALSE TRUE <NA> Total
KU 52 ( 29.1% ) 4 ( 2.2% ) 123 ( 68.7% ) 179 ( 100.0% )
NEU 23 ( 16.7% ) 1 ( 0.7% ) 114 ( 82.6% ) 138 ( 100.0% )
PITT 35 ( 19.1% ) 6 ( 3.3% ) 142 ( 77.6% ) 183 ( 100.0% )
Total 110 ( 22.0% ) 11 ( 2.2% ) 379 ( 75.8% ) 500 ( 100.0% )
 Χ2 = 2.4857   df = 2   p = .2886

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

gender<-HHQraw.df %>% select(record_id,screen_gender)
TMP<-left_join(TMP,gender)
TMP %$%  ctable(screen_gender,history_recent_ill_resolved==0,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
history_recent_ill_resolved == 0
screen_gender FALSE TRUE <NA> Total
1 27 ( 19.0% ) 5 ( 3.5% ) 110 ( 77.5% ) 142 ( 100.0% )
2 83 ( 23.2% ) 6 ( 1.7% ) 269 ( 75.1% ) 358 ( 100.0% )
Total 110 ( 22.0% ) 11 ( 2.2% ) 379 ( 75.8% ) 500 ( 100.0% )
 Χ2 = 1.3011   df = 1   p = .2540
O.R. (95% C.I.) = 0.39  (0.11 - 1.38)
R.R. (95% C.I.) = 0.90  (0.77 - 1.06)

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

TMP %$%  ctable(Site,history_recent_ill_TemporalScope_6,chisq = TRUE, OR = TRUE, RR=TRUE,headings = FALSE) %>% print(method = "render")
history_recent_ill_TemporalScope_6
Site 0 1 A cold Acute
gastric
ulcer		 Allergic
reaction to
dust mites
and animal
dander. Took
antibiotics
for 4 days
and was good
after that.		 Antibiotics
5 days for
an infected
pimple- it
went away		 bronchitis Bronchitis
and Sinus
Infection		 Bronchitis,
UTI		 Cold cold
symptoms-
resloved 		cold, sore
throat,
cough		 cold/virus common cold Common cold Dental
problems -
extractions
, bone
grafts,
crowns, and
one implant.		 Diarrhea 8
days		 Doctor
thinks she
had a
possible
tick bite
and posion
ivy		 Eye and ear
infection		 gall stones
and inflamed
bile duct.
had gall
bladder
removed.		 Gallbladder
, digestive
problems		 Had a bout
of suspected
pneuomia -
in "
observation
" for 4 days
in BMC.		 Had a
chronic
cough that
comes and
goes. Doesn'
t feel like
it will
affect
exercise.		 had
bronchitis
from
allergies.		 Had poison
ivy-went on
steroids for
it. All
better now		 I had
hemorrhoids		 Mild left
ear
infection		 neck/
varicose
veins		 prefer not
to answer,
asked cog2,
the illness
has not been
resolved
and he says
is not a
safety issue		 Red Tide
Virus		 Shingles Simple cold Sinus
infection		 Sinus
infection-
may also be
allergy
related.		 Sinus
infection/
virus;
lymphedema		 Sinus issues Upper
respiratory
infection		 UTI-
infection or
possible
kidney
stones. Went
away		 UTIs and
sinus
infection		 Vertigo/
vomitting -
hospitalized
a few hrs.
Shingles/
diarrhea -
given
prescription
.		 white blood
count was
elevated,
lungs clear
, throwing
up. No clear
diagnosis
was given
levofloxacin
to take for
12 days. no
problems
after cycle		 <NA> Total
KU 4 ( 2.2% ) 42 ( 23.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 1 ( 0.6% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.6% ) 123 ( 68.7% ) 179 ( 100.0% )
NEU 2 ( 1.4% ) 3 ( 2.2% ) 1 ( 0.7% ) 1 ( 0.7% ) 0 ( 0.0% ) 1 ( 0.7% ) 1 ( 0.7% ) 0 ( 0.0% ) 0 ( 0.0% ) 2 ( 1.4% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.7% ) 0 ( 0.0% ) 1 ( 0.7% ) 0 ( 0.0% ) 1 ( 0.7% ) 0 ( 0.0% ) 1 ( 0.7% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.7% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.7% ) 1 ( 0.7% ) 1 ( 0.7% ) 1 ( 0.7% ) 0 ( 0.0% ) 2 ( 1.4% ) 1 ( 0.7% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.7% ) 0 ( 0.0% ) 114 ( 82.6% ) 138 ( 100.0% )
PITT 2 ( 1.1% ) 24 ( 13.1% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 2 ( 1.1% ) 1 ( 0.5% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 1 ( 0.5% ) 0 ( 0.0% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 142 ( 77.6% ) 183 ( 100.0% )
Total 8 ( 1.6% ) 69 ( 13.8% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 4 ( 0.8% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 2 ( 0.4% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 2 ( 0.4% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 1 ( 0.2% ) 379 ( 75.8% ) 500 ( 100.0% )
 Χ2 = 117.5287   df = 80   p = .0040

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

Recent Surgery -

Currently includes:
* Oral surgeries - Tooth extraction, Wisdom Tooth Removal, etc.
* Procedures- Endoscopy, Colonoscopy, Biopsy, Esophagus stretched, kidney stones removed, etc.
* Needs Clarfication (to look up): "Neuroma on foot" , "Hernia"

Creating Variables...

  • history_recent_ill_resolved - 1/0 If notes specify the illness was resolved...

  • history_recent_ill_TemporalScope_3/6 - 1/0 Illness within the past 3/6 months (longer than 6mo. below)...

 

  • history_recent_hospital_Type - Interpreted from specifications...

 

  • history_recent_surgery_surgery - 1/0 partially parced for procedures vs. surgery (0's below...)

  • history_recent_surgery_Temporal_Scope_yr - 1/0 If notes specify the illness was within the past year

 

HHQ language...

** First language not English**


### HHQ Summary Variables...

HHQ_Health_Status.Factor

ggplot(data, aes(Site, HHQ_Health_Status.Factor,color=as.factor(Site)))+geom_boxplot()+theme(legend.position = "none")

data$Site<-as.factor(data$Site)
data$Site<-as.character(data$Site)
data$UPitt<-if_else(data$Site  =="PITT"  ,1,0)
data$Kansas<-if_else(data$Site  =="KU"  ,1,0)
data$Northeastern<-if_else(data$Site  =="NEU"  ,1,0)
upitt<- subset(df, UPitt == "1")$record_id
ku<- subset(df, Kansas == "1")$record_id
neu<- subset(df, Northeastern == "1")$record_id
HHQ_Health_Status.Factor_1 <- subset(data, HHQ_Health_Status.Factor == "1")$record_id
HHQ_Health_Status.Factor_2 <- subset(data, HHQ_Health_Status.Factor == "2")$record_id
HHQ_Health_Status.Factor_3 <- subset(data, HHQ_Health_Status.Factor == "3")$record_id
data$Site<-as.factor(data$Site)

ven<-plotVenn(list( Kansas=ku,UPitt=upitt,HHQ_Health_Status.Factor_3=HHQ_Health_Status.Factor_3,Northeastern=neu), nCycles = 20000,labelRegions=T, showPlot = T)
str <- charToRaw(ven$svg)
rsvg::rsvg_png(str, file = '~/HHQ_Health_Status.Factor_3_site_venn.png')


ven<-plotVenn(list( Kansas=ku,UPitt=upitt,HHQ_Health_Status.Factor_1=HHQ_Health_Status.Factor_1,Northeastern=neu), nCycles = 20000,labelRegions=T, showPlot = T)
str <- charToRaw(ven$svg)
rsvg::rsvg_png(str, file = '~/HHQ_Health_Status.Factor_1_site_venn.png')


ven<-plotVenn(list( Kansas=ku,UPitt=upitt,HHQ_Health_Status.Factor_2=HHQ_Health_Status.Factor_2,Northeastern=neu), nCycles = 20000,labelRegions=T, showPlot = T)
str <- charToRaw(ven$svg)
rsvg::rsvg_png(str, file = '~/HHQ_Health_Status.Factor_2_site_venn.png')
library(nVennR)

data$Recent_Hospitalization<-if_else(data$history_recent_hospital  =="1"  ,1,0)
data$PITT<-if_else(data$Site  =="PITT"  ,1,0)
data$KU<-if_else(data$Site  =="KU"  ,1,0)
data$NEU<-if_else(data$Site  =="NEU"  ,1,0)


PITT<- subset(data, PITT == "1")$record_id
KU <- subset(data, KU == "1")$record_id
NEU <- subset(data, NEU == "1")$record_id

Recent_Hospitalization <- subset(data, history_recent_hospital == "1")$record_id
data %$%  ctable(Site,HHQ_Sum.Score,chisq = TRUE, OR = TRUE, RR=TRUE, headings = FALSE) %>% print(method = "render")
HHQ_Sum.Score
Site 0 1 2 3 4 5 6 7 8 9 10 Total
KU 44 ( 24.6% ) 65 ( 36.3% ) 38 ( 21.2% ) 16 ( 8.9% ) 12 ( 6.7% ) 3 ( 1.7% ) 0 ( 0.0% ) 1 ( 0.6% ) 0 ( 0.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 179 ( 100.0% )
NEU 16 ( 11.6% ) 26 ( 18.8% ) 29 ( 21.0% ) 25 ( 18.1% ) 16 ( 11.6% ) 3 ( 2.2% ) 15 ( 10.9% ) 4 ( 2.9% ) 1 ( 0.7% ) 2 ( 1.4% ) 1 ( 0.7% ) 138 ( 100.0% )
PITT 30 ( 16.4% ) 38 ( 20.8% ) 38 ( 20.8% ) 41 ( 22.4% ) 14 ( 7.7% ) 13 ( 7.1% ) 6 ( 3.3% ) 2 ( 1.1% ) 1 ( 0.5% ) 0 ( 0.0% ) 0 ( 0.0% ) 183 ( 100.0% )
Total 90 ( 18.0% ) 129 ( 25.8% ) 105 ( 21.0% ) 82 ( 16.4% ) 42 ( 8.4% ) 19 ( 3.8% ) 21 ( 4.2% ) 7 ( 1.4% ) 2 ( 0.4% ) 2 ( 0.4% ) 1 ( 0.2% ) 500 ( 100.0% )
 Χ2 = 75.6749   df = 20   p = .0000

Generated by summarytools 0.9.9 (R version 3.6.1)
2021-09-14

ven<-plotVenn(list(PITT=PITT, KU=KU, NEU=NEU, Recent_Hospitalization=Recent_Hospitalization), nCycles = 2000,labelRegions=T, showPlot = T)
str <- charToRaw(ven$svg)
rsvg::rsvg_png(str, file = '~/Recent_Hospitalization_Site_venn.png')

USP Classified Medications:

Need to check on labeling scheme that EPICC Used for the following...

usp_data<-readxl::read_excel("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/Meds/OUT/IGNITE_MEDS_V3_April26.xlsx",sheet = "FINAL", skip = 3)
usp_data$record_id<-as.character(usp_data$`Record ID`)

 

Creating Variables...
  • usp_data_classified_rxs - Number of classified prescription medications
  • usp_data_classified_otcs - Number of classified OTC medications
  • usp_data_anticholinergic_rx.factor - Reports taking prescription anticholinergic medications
  • usp_data_anticholinergic_otc.factor - Reports taking OTC anticholinergic medications
  • usp_data_beta.factor - On a medication which includes beta blocking ingredients

  • usp_data_beta_oral.factor - Using oral beta blocking medication

  • Number of each of the major USP categories..
    • Analgestics, Antidepressants, Anxiolytics, Cardiovascular Agents, Blood Products and Modifiers, Anti-Obesity Agents, Blood Glucose Regulators, Anticonvulsants, Antimigraine Agents, Antidementia Agents, Antiemetics, Antimyasthenic Agents, Antineoplastics, Antiparkinson Agents, Antispasticity Agents, Antibacterials, Antivirals, Antiparasitics, Antifungals, Antigout Agents, Anesthetics, Anti-Addiction/ Substance Abuse Treatment Agents, Central Nervous System Agents, Electrolytes/ Minerals/ Metals/ Vitamins, Gastrointestinal Agents, Genitourinary Agents, Hormonal Agents, Stimulant/ Replacement/ Modifying (Adrenal), Hormonal Agents, Stimulant/ Replacement/ Modifying (Sex Hormones/ Modifiers), Hormonal Agents, Stimulant/ Replacement/ Modifying (Thyroid), Immunological Agents, Inflammatory Bowel Disease Agents, Metabolic Bone Disease Agents, Respiratory Tract/ Pulmonary Agents, Sexual Disorder Agents, Skeletal Muscle Relaxants, Sleep Disorder Agents, Dental and Oral Agents, Dermatological Agents, Ophthalmic Agents

Prescription Anticholinergics 

MED.df$RX_ANTICHOLINERGIC <- if_else(MED.df$OTC_RX=="Prescription" & MED.df$ANTICHOLINERGIC==1, 1,0)

RX_ANTICHOLINERGIC.df <- MED.df %>% filter(RX_ANTICHOLINERGIC==1)

RX_antichol_2<-RX_ANTICHOLINERGIC.df %>% filter(duplicated(RX_ANTICHOLINERGIC.df$`Record ID`)) %>% select(`Record ID`)

usp_data$usp_data_anticholinergic_rx.factor<-if_else(usp_data$record_id  %in% RX_ANTICHOLINERGIC.df$`Record ID`, 1, 0)

Over The Counter (OTC) Anticholinergics 

MED.df$OTC_ANTICHOLINERGIC<-if_else(MED.df$OTC_RX=="OTC" & MED.df$ANTICHOLINERGIC==1,1,0)

OTC_ANTICHOLINERGIC.df<-MED.df %>% filter(OTC_ANTICHOLINERGIC==1)

OTC_ANTICHOL_2<-OTC_ANTICHOLINERGIC.df %>% filter(duplicated(OTC_ANTICHOLINERGIC.df$`Record ID`)) %>% select(`Record ID`)

usp_data$usp_data_anticholinergic_otc.factor<-if_else(usp_data$record_id  %in% OTC_ANTICHOLINERGIC.df$`Record ID`, 1, 0)

All Beta Blocking Ingredients 

## CHECK Cardio Cols
RAW_MED_ENTRIES<-readxl::read_excel("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/Meds/OUT/IGNITE_MEDS_V3_April26.xlsx",sheet = "ALL_MEDS" )

RAWCARD_1<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("bisoprolol"))==TRUE,]
RAWCARD_2<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("acebutolol"))==TRUE,]
RAWCARD_3<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("atenolol"))==TRUE,]
RAWCARD_4<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("metoprolol"))==TRUE,]
RAWCARD_5<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("nadolol"))==TRUE,]
RAWCARD_6<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("nebivolol"))==TRUE,]
RAWCARD_6<-RAW_MED_ENTRIES[str_detect(RAW_MED_ENTRIES$USP_DRUG, c("propranolol"))==TRUE,]
RAWCARD<-rbind(RAWCARD_1, RAWCARD_2,RAWCARD_3,RAWCARD_4,RAWCARD_5,RAWCARD_6)

usp_data$usp_data_beta.factor<-if_else(usp_data$`Record ID` %in% unique(RAWCARD$`Record ID`), "1", "0")

Oral Beta Blocking Medications 

RAWCARD_1<-MED.df[str_detect(MED.df$USP_DRUG, c("bisoprolol")) & str_detect(MED.df$CODE.factor, c("oral")) ==TRUE,]
RAWCARD_2<-MED.df[str_detect(MED.df$USP_DRUG, c("acebutolol"))& str_detect(MED.df$CODE.factor, c("oral"))==TRUE,]
RAWCARD_3<-MED.df[str_detect(MED.df$USP_DRUG, c("atenolol"))& str_detect(MED.df$CODE.factor, c("oral"))==TRUE,]
RAWCARD_4<-MED.df[str_detect(MED.df$USP_DRUG, c("metoprolol"))& str_detect(MED.df$CODE.factor, c("oral"))==TRUE,]
RAWCARD_5<-MED.df[str_detect(MED.df$USP_DRUG, c("nadolol"))& str_detect(MED.df$CODE.factor, c("oral"))==TRUE,]
RAWCARD_6<-MED.df[str_detect(MED.df$USP_DRUG, c("nebivolol"))& str_detect(MED.df$CODE.factor, c("oral"))==TRUE,]
RAWCARD_6<-MED.df[str_detect(MED.df$USP_DRUG, c("propranolol"))& str_detect(MED.df$CODE.factor, c("oral"))==TRUE,]
RAWCARD<-rbind(RAWCARD_1, RAWCARD_2,RAWCARD_3,RAWCARD_4,RAWCARD_5,RAWCARD_6)
usp_data$usp_data_beta_oral.factor<-if_else(usp_data$`Record ID` %in% unique(RAWCARD$`Record ID`), "1", "0")
#usp_data<-usp_data %>% select(`Record ID`, usp_data_beta.factor, usp_data_beta_oral.factor)
data<-left_join(data,usp_data, by="record_id")
data$record_id<-as.character(data$record_id)

Manually Count USP Classified BETA BLOCKERS/RELATED Agents
  • usp_beta.n - USP defined beta blocking agents
  • usp_beta.factor - USP defined beta blocking agents
CARDIO_MEDS<-readxl::read_excel("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/Meds/OUT/IGNITE_MEDS_V3_April26.xlsx",sheet = "CARDIO_1", skip = 2)
CARDIO_MEDS$record_id<-as.character(CARDIO_MEDS$Record_ID)
CARDIO_MEDS$usp_beta.n<-CARDIO_MEDS$`BETA BLOCKERS/RELATED`
CARDIO_MEDS$usp_beta.factor<-if_else(CARDIO_MEDS$usp_beta.n>=1, 1,0)
### BASED ON EPICC RENAME THE REST OF CARDIO VARIABLES AS ABOVE, IF NEEDED.
data<-left_join(data, CARDIO_MEDS)
CARDIO_MEDS_0<-CARDIO_MEDS

 

Merge HVLT Data

FILE<-list.files("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/HH_Provided/", pattern="HV")
PATH<-"/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/HH_Provided/"
file<-paste(PATH, FILE, sep="")
na_values<-c("99991","99992","99993","99994", "99995","99996","99997","99998","99999", "999910")
file<-read_csv(file,  na=na_values)
file<-remove_empty_cols(file)
file$record_id<-as.character(file$screen_id)
file<-file[,-c(2:3)]
data<-left_join(data,file, by="record_id")
Randomized_dat<-data

 

Write out processed/merged variables...

write_csv(Randomized_dat, "/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/HHQ/OUT_DATA/Working_Recode_Vars.txt")

 

Import/Process Full RedCap Database as.is:

FILE<-list.files("/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/", pattern=".r")
PATH<-"/Volumes/IGNITE_Imaging/QC_Output/R_IGNITE/RedCap/PRE/Data/"
file<-paste(PATH, FILE, sep="")
source(file)
data$record_id<-as.character(data$record_id)
data<-data %>% filter(str_detect(data$record_id, "--1")==FALSE)
data<-data %>% filter(str_detect(data$record_id, "--2")==FALSE)
data<-data %>% filter(str_detect(data$record_id, "--")==FALSE)
data<-data[complete.cases(data$vo2_site),]
data<-data %>% select(record_id,  ses_year_education_2,  ses_year_education_2.factor,                   
                      starts_with(c("vo2", "cirs", "pss_", "ses_", "rand")),
                      contains(c("race", "score","bmi","nih", "adj_",
                                "hru_","tscore" , "_t")))
data$record_id<-as.character(data$record_id)
data<-left_join(Randomized_dat,data,by=("record_id"))
1. Education Variables
  • HHQ
  • MacArthur SES Scale
## RECODE
data$ses_earnings<-na_if(data$ses_earnings,10)
data$ses_earnings<-na_if(data$ses_earnings,11)
data$ses_earnings.factor<-na_if(data$ses_earnings.factor, "No response")
data$ses_earnings.factor<-na_if(data$ses_earnings.factor, "Dont Know")


data$EDU_HS<-if_else(str_detect(data$ses_year_education_2.factor, "High School")==TRUE, 1,0)
data$EDU_College<-if_else(str_detect(data$ses_year_education_2.factor, "College")==TRUE, 1,0)
data$EDU_Grad<-if_else(str_detect(data$ses_year_education_2.factor, "Graduate School")==TRUE, 1,0)

data$EDU.factor<-if_else(str_detect(data$ses_year_education_2.factor, "High School")==TRUE, "High School",as.character(data$ses_year_education_2.factor))
data$EDU.factor<-if_else(str_detect(data$EDU.factor, "College")==TRUE, "College",as.character(data$EDU.factor))
data$EDU.factor<-if_else(str_detect(data$ses_year_education_2.factor, "Graduate School")==TRUE, "Graduate",as.character(data$EDU.factor))
data$EDU.factor<-as.factor(data$EDU.factor)

Discrepancies- Education Variable

DT::datatable(data %>% select(educ,ses_year_education_2, ses_year_education_2.factor), rownames = FALSE, options = list(pageLength = 5))
2. VO2 Test Data
  • Absolute VO2 = Liters per min (L/min)
  • Relative VO2 = milliliters per minute per kilogram (a unit of mass)
class(data$ses_year_education_2)<-"integer"
class(data$vo2_age)<-"integer"
class(data$vo2sum_peak_ml)<-"integer"
class(data$vo2_sex)<-"integer"

####data$vo2sum_peak_ml<-data$vo2sum_ma
data$BMI<-(data$vo2_data_weight/data$vo2_data_height/data$vo2_data_height)*10000
data$BMI<-labelled::remove_var_label(data$BMI)
data$BMI<-as.numeric(data$BMI)
class(data$BMI)="numeric" 

data$BMI_group<-if_else(data$BMI<18.5, 1, as.numeric(data$BMI)) #under
data$BMI_group<-if_else( (data$BMI_group>1 & data$BMI_group<24.9)  ,2 , as.numeric(data$BMI_group)) #healthy
data$BMI_group<-if_else(data$BMI_group<29.9  & data$BMI_group>2 ,3, as.numeric(data$BMI_group)) #over
data$BMI_group<-if_else(data$BMI_group<34.9 & data$BMI_group>3 ,4, as.numeric(data$BMI_group)) #obese I
data$BMI_group<-if_else(data$BMI_group<39.9 & data$BMI_group>4 ,5, as.numeric(data$BMI_group)) #obese II
data$BMI_group<-if_else(data$BMI_group<49.9 & data$BMI_group>5 ,6, as.numeric(data$BMI_group)) #obese III
data$BMI_group<-if_else(data$BMI_group>50 ,7, as.numeric(data$BMI_group)) #OUTLIER?
data$BMI_group<-as.character(data$BMI_group)

data$BMI_GROUP<-if_else(data$BMI_group=="1", "Underweight", data$BMI_group)
data$BMI_GROUP<-if_else(data$BMI_group=="2", "Healthy", data$BMI_GROUP)
data$BMI_GROUP<-if_else(data$BMI_group=="3", "Overweight", data$BMI_GROUP)
data$BMI_GROUP<-if_else(data$BMI_group=="4", "Obese I", data$BMI_GROUP)
data$BMI_GROUP<-if_else(data$BMI_group=="5", "Obese II", data$BMI_GROUP)
data$BMI_GROUP<-if_else(data$BMI_group=="6", "Obese III", data$BMI_GROUP)
data$BMI_GROUP<-if_else(data$BMI_group=="7", "ERROR", data$BMI_GROUP)
#data %>% filter(data$BMI_GROUP=="ERROR")
#data<-data[!(data$BMI_GROUP=="ERROR"),] 
#data %>% filter(is.na(data$BMI_GROUP)==TRUE)
data$BMI_GROUP<-factor(data$BMI_GROUP, levels=c("Underweight", "Healthy", "Overweight","Obese I", "Obese II", "Obese III"))


data$BMI_GROUP<-as.character(data$BMI_GROUP)
data$BMI_Underweight<-if_else(data$BMI_GROUP=="Underweight", 1,0)
data$BMI_Healthy<-if_else(data$BMI_GROUP== "Healthy", 1,0)
data$BMI_Obese<-if_else(data$BMI_GROUP== "Overweight", "1",as.character(data$BMI_GROUP))
data$BMI_Obese<-if_else(data$BMI_Obese=="Obese I", "1",as.character(data$BMI_Obese))
data$BMI_Obese<-if_else(data$BMI_Obese=="Obese II", "1",as.character(data$BMI_Obese))
data$BMI_Obese<-if_else(data$BMI_Obese=="Obese III", "1",as.character(data$BMI_Obese))
data$BMI_Obese<-if_else(data$BMI_Obese=="Healthy", "0",as.character(data$BMI_Obese))
data$BMI_Obese<-if_else(data$BMI_Obese=="Underweight", "0",as.character(data$BMI_Obese))

Discrepancies- beta blocker variables 

# V02_data_beta != VA Classifiers != custom gather oral beta blocking ingredients.      
data$usp_data_beta.factor<-as.factor(data$usp_data_beta.factor)
data %>%
  filter(data$vo2_data_beta != data$usp_beta.factor | 
           data$vo2_data_beta !=data$usp_data_beta_oral.factor  |
           data$vo2_data_beta !=data$usp_data_beta.factor
         ) %>%
  select(record_id,vo2_data_beta,usp_data_beta_oral.factor,usp_data_beta.factor ) %>%
  DT::datatable(rownames=FALSE, options = list(pageLength = 5))

3. CIRS Data
#TBA
4. Hru Data
#TBA

 

 

Mean Tables

 

Site

KU
(N=179)		 NEU
(N=138)		 PITT
(N=183)		 Overall
(N=500)
usp_data_classified_rxs
Mean (SD) 3.70 (3.05) 3.69 (3.01) 3.91 (3.03) 3.77 (3.03)
Median [Min, Max] 3.00 [0, 19.0] 3.00 [0, 15.0] 3.00 [0, 21.0] 3.00 [0, 21.0]
usp_data_classified_otcs
Mean (SD) 2.64 (2.15) 2.79 (2.00) 3.31 (2.31) 2.93 (2.19)
Median [Min, Max] 2.00 [0, 10.0] 3.00 [0, 10.0] 3.00 [0, 10.0] 2.00 [0, 10.0]
Weekly_alc.score
Mean (SD) 3.17 (5.17) 3.32 (4.69) 2.84 (4.76) 3.09 (4.89)
Median [Min, Max] 1.00 [0, 28.0] 1.00 [0, 21.0] 1.00 [0, 30.0] 1.00 [0, 30.0]
daily_caffeine
Mean (SD) 2.52 (1.93) 2.52 (1.74) 2.87 (2.83) 2.65 (2.27)
Median [Min, Max] 2.00 [0, 10.0] 2.00 [0, 10.0] 2.50 [0, 30.0] 2.00 [0, 30.0]
Missing 1 (0.6%) 3 (2.2%) 0 (0%) 4 (0.8%)
yrs_smoke
Mean (SD) 1.03 (6.50) 1.12 (6.91) 0.656 (5.20) 0.918 (6.17)
Median [Min, Max] 0 [0, 50.0] 0 [0, 54.0] 0 [0, 50.0] 0 [0, 54.0]
prior_smoke_yrs
Mean (SD) 5.49 (10.6) 5.95 (9.26) 9.99 (13.4) 7.26 (11.6)
Median [Min, Max] 0 [0, 59.0] 0 [0, 40.0] 0 [0, 50.0] 0 [0, 59.0]
hvlt_total_recall_tscore
Mean (SD) 54.1 (8.64) 53.1 (9.10) 53.8 (8.86) 53.7 (8.84)
Median [Min, Max] 54.0 [28.0, 73.0] 54.0 [31.0, 71.0] 54.0 [29.0, 72.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 55.9 (8.70) 52.3 (11.5) 53.6 (10.9) 54.1 (10.4)
Median [Min, Max] 58.0 [27.0, 68.0] 55.0 [24.0, 68.0] 55.0 [20.0, 68.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 2.91 (2.23) 3.28 (2.32) 3.72 (2.56) 3.31 (2.40)
Median [Min, Max] 3.00 [0, 12.0] 3.00 [0, 11.0] 3.00 [0, 12.0] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 22.6 (4.97) 22.3 (5.18) 20.4 (4.63) 21.7 (5.00)
Median [Min, Max] 22.0 [12.0, 39.0] 22.0 [11.0, 34.0] 20.0 [11.0, 34.0] 21.0 [11.0, 39.0]
Max RER
Mean (SD) 1.09 (0.0652) 1.10 (0.0817) 1.09 (0.0783) 1.09 (0.0748)
Median [Min, Max] 1.09 [0.930, 1.28] 1.10 [0.870, 1.36] 1.10 [0.880, 1.30] 1.09 [0.870, 1.36]
   

Sex differences-
Male
(N=142)		 Female
(N=358)		 P-value
usp_data_classified_rxs
Mean (SD) 4.02 (2.79) 3.68 (3.11) 0.229
Median [Min, Max] 4.00 [0, 12.0] 3.00 [0, 21.0]
usp_data_classified_otcs
Mean (SD) 2.27 (1.86) 3.18 (2.26) <0.001
Median [Min, Max] 2.00 [0, 9.00] 3.00 [0, 10.0]
Weekly_alc.score
Mean (SD) 4.71 (6.59) 2.44 (3.85) <0.001
Median [Min, Max] 2.00 [0, 30.0] 1.00 [0, 21.0]
pack_day
Mean (SD) 1.20 (0.759) 1.01 (0.720) 0.067
Median [Min, Max] 1.00 [0.0330, 3.00] 1.00 [0.00500, 3.00]
Missing 67 (47.2%) 208 (58.1%)
smoke_yrs
Mean (SD) 17.7 (13.4) 18.2 (13.0) 0.822
Median [Min, Max] 15.0 [0.167, 59.0] 15.0 [1.00, 54.0]
Missing 66 (46.5%) 207 (57.8%)
daily_caffeine
Mean (SD) 3.08 (3.05) 2.48 (1.84) 0.03
Median [Min, Max] 3.00 [0, 30.0] 2.00 [0, 12.0]
Missing 0 (0%) 4 (1.1%)
hvlt_total_recall_tscore
Mean (SD) 50.5 (8.64) 55.0 (8.60) <0.001
Median [Min, Max] 50.0 [28.0, 71.0] 55.0 [31.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 53.0 (10.5) 54.5 (10.4) 0.156
Median [Min, Max] 55.0 [24.0, 68.0] 58.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 3.33 (2.48) 3.30 (2.38) 0.895
Median [Min, Max] 3.00 [0, 11.0] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 24.2 (5.52) 20.7 (4.42) <0.001
Median [Min, Max] 24.5 [11.0, 39.0] 20.0 [11.0, 34.0]
Max RER
Mean (SD) 1.10 (0.0754) 1.09 (0.0744) 0.087
Median [Min, Max] 1.09 [0.890, 1.33] 1.10 [0.870, 1.36]

Smoking Demos -

Cigarettes only
0
(N=488)		 1
(N=12)		 P-value
usp_data_classified_rxs
Mean (SD) 3.76 (2.95) 4.25 (5.55) 0.767
Median [Min, Max] 3.00 [0, 19.0] 3.00 [0, 21.0]
Weekly_alc.score
Mean (SD) 3.08 (4.86) 3.54 (6.01) 0.796
Median [Min, Max] 1.00 [0, 30.0] 1.00 [0, 20.5]
pack_day
Mean (SD) 1.11 (0.736) 0.365 (0.212) <0.001
Median [Min, Max] 1.00 [0.0140, 3.00] 0.330 [0.00500, 0.750]
Missing 274 (56.1%) 1 (8.3%)
smoke_yrs
Mean (SD) 16.9 (12.2) 38.3 (13.0) <0.001
Median [Min, Max] 15.0 [0.167, 59.0] 40.5 [15.0, 54.0]
Missing 273 (55.9%) 0 (0%)
hvlt_total_recall_tscore
Mean (SD) 53.7 (8.88) 54.0 (7.22) 0.884
Median [Min, Max] 54.0 [28.0, 73.0] 51.5 [42.0, 70.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 54.2 (10.4) 48.7 (12.0) 0.141
Median [Min, Max] 55.0 [20.0, 68.0] 51.5 [27.0, 67.0]
CIRS Total Score
Mean (SD) 3.31 (2.42) 3.17 (1.80) 0.79
Median [Min, Max] 3.00 [0, 12.0] 2.50 [0, 6.00]
Peak VO2 (ml/kg/min):
Mean (SD) 21.8 (5.01) 18.0 (3.22) 0.002
Median [Min, Max] 22.0 [11.0, 39.0] 18.0 [12.0, 25.0]
Max RER
Mean (SD) 1.09 (0.0751) 1.07 (0.0599) 0.125
Median [Min, Max] 1.10 [0.870, 1.36] 1.07 [0.950, 1.17]
All Inhaled Alternatives
0
(N=476)		 1
(N=24)		 P-value
usp_data_classified_rxs
Mean (SD) 3.74 (2.96) 4.46 (4.21) 0.417
Median [Min, Max] 3.00 [0, 19.0] 3.50 [0, 21.0]
Weekly_alc.score
Mean (SD) 2.94 (4.68) 6.04 (7.50) 0.056
Median [Min, Max] 1.00 [0, 30.0] 3.75 [0, 24.0]
pack_day
Mean (SD) 1.10 (0.747) 0.751 (0.545) 0.013
Median [Min, Max] 1.00 [0.0140, 3.00] 0.625 [0.00500, 2.00]
Missing 271 (56.9%) 4 (16.7%)
smoke_yrs
Mean (SD) 16.8 (12.3) 30.1 (14.9) <0.001
Median [Min, Max] 15.0 [0.167, 59.0] 30.0 [3.00, 54.0]
Missing 270 (56.7%) 3 (12.5%)
hvlt_total_recall_tscore
Mean (SD) 53.8 (8.87) 52.0 (8.23) 0.325
Median [Min, Max] 54.0 [28.0, 73.0] 51.5 [34.0, 70.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 54.3 (10.3) 49.1 (11.2) 0.034
Median [Min, Max] 55.0 [20.0, 68.0] 49.0 [27.0, 67.0]
CIRS Total Score
Mean (SD) 3.30 (2.41) 3.46 (2.32) 0.748
Median [Min, Max] 3.00 [0, 12.0] 3.50 [0, 9.00]
Peak VO2 (ml/kg/min):
Mean (SD) 21.8 (4.98) 20.8 (5.48) 0.43
Median [Min, Max] 21.5 [11.0, 39.0] 20.0 [12.0, 32.0]
Max RER
Mean (SD) 1.09 (0.0754) 1.08 (0.0628) 0.415
Median [Min, Max] 1.10 [0.870, 1.36] 1.08 [0.950, 1.22]
Summated Smoking factors
0
(N=269)		 1
(N=221)		 2
(N=10)		 Overall
(N=500)
usp_data_classified_rxs
Mean (SD) 3.56 (2.83) 4.01 (3.27) 4.40 (2.37) 3.77 (3.03)
Median [Min, Max] 3.00 [0, 13.0] 3.00 [0, 21.0] 4.50 [1.00, 8.00] 3.00 [0, 21.0]
Weekly_alc.score
Mean (SD) 2.42 (3.78) 3.66 (5.61) 8.35 (8.84) 3.09 (4.89)
Median [Min, Max] 1.00 [0, 21.0] 1.00 [0, 30.0] 5.50 [0, 24.0] 1.00 [0, 30.0]
pack_day
Mean (SD) NA (NA) 1.07 (0.747) 1.13 (0.502) 1.07 (0.737)
Median [Min, Max] NA [NA, NA] 1.00 [0.00500, 3.00] 1.00 [0.330, 2.00] 1.00 [0.00500, 3.00]
Missing 269 (100%) 6 (2.7%) 0 (0%) 275 (55.0%)
smoke_yrs
Mean (SD) NA (NA) 17.8 (13.1) 22.3 (13.3) 18.0 (13.1)
Median [Min, Max] NA [NA, NA] 15.0 [0.167, 59.0] 20.3 [3.00, 50.0] 15.0 [0.167, 59.0]
Missing 269 (100%) 4 (1.8%) 0 (0%) 273 (54.6%)
hvlt_total_recall_tscore
Mean (SD) 53.5 (9.21) 54.0 (8.47) 53.1 (6.87) 53.7 (8.84)
Median [Min, Max] 54.0 [29.0, 72.0] 54.0 [28.0, 73.0] 54.0 [42.0, 64.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 54.9 (9.98) 53.2 (11.0) 52.1 (8.24) 54.1 (10.4)
Median [Min, Max] 58.0 [27.0, 68.0] 55.0 [20.0, 68.0] 51.5 [41.0, 63.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 3.16 (2.29) 3.47 (2.51) 3.90 (2.73) 3.31 (2.40)
Median [Min, Max] 3.00 [0, 11.0] 3.00 [0, 12.0] 4.00 [1.00, 9.00] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 21.9 (4.88) 21.4 (5.08) 22.9 (6.45) 21.7 (5.00)
Median [Min, Max] 22.0 [11.0, 36.0] 21.0 [11.0, 39.0] 21.5 [14.0, 32.0] 21.0 [11.0, 39.0]
Max RER
Mean (SD) 1.10 (0.0755) 1.09 (0.0743) 1.11 (0.0652) 1.09 (0.0748)
Median [Min, Max] 1.10 [0.880, 1.30] 1.08 [0.870, 1.36] 1.12 [1.02, 1.22] 1.09 [0.870, 1.36]
Smoking Demos Stratified
Never Smoked
(N=269)		 Former Cigarette User
(N=207)		 Primary Alternative User
(N=3)		 Secondary Alternative User
(N=10)		 Primary Cigarette User
(N=11)		 Overall
(N=500)
usp_data_classified_rxs
Mean (SD) 3.56 (2.83) 3.98 (3.11) 6.00 (2.65) 4.40 (2.37) 4.09 (5.79) 3.77 (3.03)
Median [Min, Max] 3.00 [0, 13.0] 3.00 [0, 19.0] 5.00 [4.00, 9.00] 4.50 [1.00, 8.00] 3.00 [0, 21.0] 3.00 [0, 21.0]
Weekly_alc.score
Mean (SD) 2.42 (3.78) 3.62 (5.58) 6.33 (7.09) 8.35 (8.84) 3.86 (6.20) 3.09 (4.89)
Median [Min, Max] 1.00 [0, 21.0] 1.00 [0, 30.0] 5.00 [0, 14.0] 5.50 [0, 24.0] 1.00 [0, 20.5] 1.00 [0, 30.0]
pack_day
Mean (SD) NA (NA) 1.10 (0.747) NA (NA) 1.13 (0.502) 0.369 (0.223) 1.07 (0.737)
Median [Min, Max] NA [NA, NA] 1.00 [0.0140, 3.00] NA [NA, NA] 1.00 [0.330, 2.00] 0.415 [0.00500, 0.750] 1.00 [0.00500, 3.00]
Missing 269 (100%) 2 (1.0%) 3 (100%) 0 (0%) 1 (9.1%) 275 (55.0%)
smoke_yrs
Mean (SD) NA (NA) 16.8 (12.3) NA (NA) 22.3 (13.3) 37.2 (13.1) 18.0 (13.1)
Median [Min, Max] NA [NA, NA] 15.0 [0.167, 59.0] NA [NA, NA] 20.3 [3.00, 50.0] 40.0 [15.0, 54.0] 15.0 [0.167, 59.0]
Missing 269 (100%) 1 (0.5%) 3 (100%) 0 (0%) 0 (0%) 273 (54.6%)
hvlt_total_recall_tscore
Mean (SD) 53.5 (9.21) 54.2 (8.40) 42.7 (11.7) 53.1 (6.87) 53.6 (7.46) 53.7 (8.84)
Median [Min, Max] 54.0 [29.0, 72.0] 54.0 [28.0, 73.0] 38.0 [34.0, 56.0] 54.0 [42.0, 64.0] 51.0 [42.0, 70.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 54.9 (9.98) 53.6 (10.7) 44.0 (17.3) 52.1 (8.24) 47.7 (12.1) 54.1 (10.4)
Median [Min, Max] 58.0 [27.0, 68.0] 55.0 [20.0, 68.0] 35.0 [33.0, 64.0] 51.5 [41.0, 63.0] 49.0 [27.0, 67.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 3.16 (2.29) 3.49 (2.54) 3.67 (3.21) 3.90 (2.73) 3.00 (1.79) 3.31 (2.40)
Median [Min, Max] 3.00 [0, 11.0] 3.00 [0, 12.0] 5.00 [0, 6.00] 4.00 [1.00, 9.00] 2.00 [0, 6.00] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 21.9 (4.88) 21.6 (5.11) 24.3 (4.16) 22.9 (6.45) 18.0 (3.38) 21.7 (5.00)
Median [Min, Max] 22.0 [11.0, 36.0] 21.0 [11.0, 39.0] 23.0 [21.0, 29.0] 21.5 [14.0, 32.0] 18.0 [12.0, 25.0] 21.0 [11.0, 39.0]
Max RER
Mean (SD) 1.10 (0.0755) 1.09 (0.0752) 1.08 (0.0379) 1.11 (0.0652) 1.06 (0.0602) 1.09 (0.0748)
Median [Min, Max] 1.10 [0.880, 1.30] 1.09 [0.870, 1.36] 1.06 [1.05, 1.12] 1.12 [1.02, 1.22] 1.06 [0.950, 1.17] 1.09 [0.870, 1.36]
Corrected Smoking Status (all inhaled alternatives)...
Current
(N=24)		 Former
(N=207)		 Never
(N=268)		 Overall
(N=500)
usp_data_classified_rxs
Mean (SD) 4.46 (4.21) 3.98 (3.11) 3.55 (2.83) 3.77 (3.03)
Median [Min, Max] 3.50 [0, 21.0] 3.00 [0, 19.0] 3.00 [0, 13.0] 3.00 [0, 21.0]
Weekly_alc.score
Mean (SD) 6.04 (7.50) 3.62 (5.58) 2.43 (3.79) 3.09 (4.89)
Median [Min, Max] 3.75 [0, 24.0] 1.00 [0, 30.0] 1.00 [0, 21.0] 1.00 [0, 30.0]
pack_day
Mean (SD) 0.751 (0.545) 1.10 (0.747) NA (NA) 1.07 (0.737)
Median [Min, Max] 0.625 [0.00500, 2.00] 1.00 [0.0140, 3.00] NA [NA, NA] 1.00 [0.00500, 3.00]
Missing 4 (16.7%) 2 (1.0%) 268 (100%) 275 (55.0%)
smoke_yrs
Mean (SD) 30.1 (14.9) 16.8 (12.3) NA (NA) 18.0 (13.1)
Median [Min, Max] 30.0 [3.00, 54.0] 15.0 [0.167, 59.0] NA [NA, NA] 15.0 [0.167, 59.0]
Missing 3 (12.5%) 1 (0.5%) 268 (100%) 273 (54.6%)
hvlt_total_recall_tscore
Mean (SD) 52.0 (8.23) 54.2 (8.40) 53.4 (9.19) 53.7 (8.84)
Median [Min, Max] 51.5 [34.0, 70.0] 54.0 [28.0, 73.0] 54.0 [29.0, 72.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 49.1 (11.2) 53.6 (10.7) 54.8 (9.97) 54.1 (10.4)
Median [Min, Max] 49.0 [27.0, 67.0] 55.0 [20.0, 68.0] 58.0 [27.0, 68.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 3.46 (2.32) 3.49 (2.54) 3.15 (2.30) 3.31 (2.40)
Median [Min, Max] 3.50 [0, 9.00] 3.00 [0, 12.0] 3.00 [0, 11.0] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 20.8 (5.48) 21.6 (5.11) 21.9 (4.89) 21.7 (5.00)
Median [Min, Max] 20.0 [12.0, 32.0] 21.0 [11.0, 39.0] 22.0 [11.0, 36.0] 21.0 [11.0, 39.0]
Adjudication Outcomes

Performing poorly on Memory tests
(N=67)		 Performing poorly on Non-Memory tests
(N=77)		 Performing within normal ranges on tests
(N=356)		 Overall
(N=500)
usp_data_classified_rxs
Mean (SD) 4.49 (3.32) 3.91 (3.19) 3.61 (2.92) 3.77 (3.03)
Median [Min, Max] 4.00 [0, 15.0] 3.00 [0, 19.0] 3.00 [0, 21.0] 3.00 [0, 21.0]
usp_data_classified_otcs
Mean (SD) 2.43 (1.94) 3.06 (2.15) 2.99 (2.23) 2.93 (2.19)
Median [Min, Max] 2.00 [0, 8.00] 3.00 [0, 9.00] 3.00 [0, 10.0] 2.00 [0, 10.0]
Weekly_alc.score
Mean (SD) 3.87 (4.39) 2.56 (3.89) 3.06 (5.16) 3.09 (4.89)
Median [Min, Max] 2.00 [0, 17.5] 1.00 [0, 20.0] 1.00 [0, 30.0] 1.00 [0, 30.0]
daily_caffeine
Mean (SD) 2.76 (1.95) 2.49 (1.92) 2.66 (2.39) 2.65 (2.27)
Median [Min, Max] 2.50 [0, 8.50] 2.00 [0, 10.0] 2.00 [0, 30.0] 2.00 [0, 30.0]
Missing 0 (0%) 1 (1.3%) 3 (0.8%) 4 (0.8%)
pack_day
Mean (SD) 0.975 (0.554) 1.03 (0.579) 1.10 (0.797) 1.07 (0.737)
Median [Min, Max] 1.00 [0.140, 2.00] 1.00 [0.250, 3.00] 1.00 [0.00500, 3.00] 1.00 [0.00500, 3.00]
Missing 40 (59.7%) 39 (50.6%) 196 (55.1%) 275 (55.0%)
smoke_yrs
Mean (SD) 19.8 (15.2) 14.8 (10.3) 18.5 (13.3) 18.0 (13.1)
Median [Min, Max] 15.0 [0.167, 50.0] 12.0 [0.230, 40.0] 15.0 [1.00, 59.0] 15.0 [0.167, 59.0]
Missing 40 (59.7%) 38 (49.4%) 195 (54.8%) 273 (54.6%)
hvlt_total_recall_tscore
Mean (SD) 44.5 (8.10) 52.3 (7.40) 55.7 (8.07) 53.7 (8.84)
Median [Min, Max] 44.0 [28.0, 60.0] 52.0 [34.0, 69.0] 56.0 [35.0, 73.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 39.0 (10.3) 53.2 (7.78) 57.1 (8.21) 54.1 (10.4)
Median [Min, Max] 37.0 [20.0, 67.0] 53.0 [36.0, 68.0] 59.0 [33.0, 68.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 3.33 (2.09) 3.34 (2.56) 3.30 (2.43) 3.31 (2.40)
Median [Min, Max] 3.00 [0, 9.00] 4.00 [0, 12.0] 3.00 [0, 12.0] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 21.2 (5.31) 21.9 (4.55) 21.8 (5.05) 21.7 (5.00)
Median [Min, Max] 21.0 [11.0, 33.0] 21.0 [13.0, 35.0] 21.0 [11.0, 39.0] 21.0 [11.0, 39.0]
Max RER
Mean (SD) 1.08 (0.0806) 1.10 (0.0625) 1.10 (0.0762) 1.09 (0.0748)
Median [Min, Max] 1.08 [0.870, 1.28] 1.10 [0.930, 1.24] 1.10 [0.880, 1.36] 1.09 [0.870, 1.36]
 

ITT / Withdrew (?)

No Treatment
(N=6)		 Treatment
(N=494)		 Overall
(N=500)
usp_data_classified_rxs
Mean (SD) 2.17 (2.14) 3.79 (3.03) 3.77 (3.03)
Median [Min, Max] 1.50 [0, 6.00] 3.00 [0, 21.0] 3.00 [0, 21.0]
usp_data_classified_otcs
Mean (SD) 2.50 (1.38) 2.93 (2.20) 2.93 (2.19)
Median [Min, Max] 2.00 [1.00, 5.00] 2.00 [0, 10.0] 2.00 [0, 10.0]
Weekly_alc.score
Mean (SD) 6.17 (8.01) 3.05 (4.84) 3.09 (4.89)
Median [Min, Max] 3.50 [0, 20.0] 1.00 [0, 30.0] 1.00 [0, 30.0]
daily_caffeine
Mean (SD) 1.90 (2.25) 2.66 (2.27) 2.65 (2.27)
Median [Min, Max] 1.00 [0, 5.00] 2.00 [0, 30.0] 2.00 [0, 30.0]
Missing 1 (16.7%) 3 (0.6%) 4 (0.8%)
pack_day
Mean (SD) 2.00 (1.41) 1.06 (0.729) 1.07 (0.737)
Median [Min, Max] 2.00 [1.00, 3.00] 1.00 [0.00500, 3.00] 1.00 [0.00500, 3.00]
Missing 4 (66.7%) 271 (54.9%) 275 (55.0%)
smoke_yrs
Mean (SD) 12.3 (0.354) 18.1 (13.2) 18.0 (13.1)
Median [Min, Max] 12.3 [12.0, 12.5] 15.0 [0.167, 59.0] 15.0 [0.167, 59.0]
Missing 4 (66.7%) 269 (54.5%) 273 (54.6%)
hvlt_total_recall_tscore
Mean (SD) 53.7 (7.55) 53.7 (8.86) 53.7 (8.84)
Median [Min, Max] 55.5 [42.0, 63.0] 54.0 [28.0, 73.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 50.5 (8.62) 54.1 (10.4) 54.1 (10.4)
Median [Min, Max] 51.5 [39.0, 63.0] 55.0 [20.0, 68.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 1.33 (0.816) 3.33 (2.41) 3.31 (2.40)
Median [Min, Max] 1.50 [0, 2.00] 3.00 [0, 12.0] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 23.7 (6.83) 21.7 (4.98) 21.7 (5.00)
Median [Min, Max] 21.5 [17.0, 34.0] 21.0 [11.0, 39.0] 21.0 [11.0, 39.0]
BMI
Mean (SD) 28.4 (4.12) 30.1 (10.9) 30.0 (10.9)
Median [Min, Max] 27.1 [22.8, 33.7] 28.9 [18.0, 238] 28.9 [18.0, 238]
Missing 0 (0%) 1 (0.2%) 1 (0.2%)
 

COVID Timelines

COVID
(N=6)		 Pre-Covid
(N=494)		 P-value
usp_data_classified_rxs
Mean (SD) 3.17 (2.64) 3.78 (3.03) 0.595
Median [Min, Max] 2.00 [1.00, 7.00] 3.00 [0, 21.0]
usp_data_classified_otcs
Mean (SD) 2.33 (1.37) 2.93 (2.19) 0.335
Median [Min, Max] 2.50 [0, 4.00] 2.00 [0, 10.0]
Weekly_alc.score
Mean (SD) 7.58 (7.07) 3.03 (4.84) 0.176
Median [Min, Max] 5.25 [1.00, 17.0] 1.00 [0, 30.0]
daily_caffeine
Mean (SD) 2.42 (1.56) 2.65 (2.27) 0.73
Median [Min, Max] 3.00 [0, 4.00] 2.00 [0, 30.0]
Missing 0 (0%) 4 (0.8%)
pack_day
Mean (SD) 1.13 (0.629) 1.07 (0.740) 0.876
Median [Min, Max] 1.00 [0.500, 2.00] 1.00 [0.00500, 3.00]
Missing 2 (33.3%) 273 (55.3%)
smoke_yrs
Mean (SD) 6.75 (5.56) 18.2 (13.1) 0.021
Median [Min, Max] 4.50 [3.00, 15.0] 15.0 [0.167, 59.0]
Missing 2 (33.3%) 271 (54.9%)
hvlt_total_recall_tscore
Mean (SD) 50.2 (9.83) 53.7 (8.83) 0.416
Median [Min, Max] 51.5 [37.0, 65.0] 54.0 [28.0, 73.0]
BVMT Delayed Recall (Norm) T Score
Mean (SD) 49.5 (14.0) 54.1 (10.4) 0.455
Median [Min, Max] 55.0 [32.0, 67.0] 55.0 [20.0, 68.0]
CIRS Total Score
Mean (SD) 3.83 (1.60) 3.30 (2.41) 0.457
Median [Min, Max] 4.00 [1.00, 6.00] 3.00 [0, 12.0]
Peak VO2 (ml/kg/min):
Mean (SD) 17.5 (3.94) 21.8 (5.00) 0.045
Median [Min, Max] 17.5 [11.0, 22.0] 21.0 [11.0, 39.0]
   

 

 

Correlograms

Dummy Coded Groups (dispersion)

   

V02/Physical Function/Fitness


   

Medications

 

 

 

Stratified Box Plots:

For information on outlier threshold see: https://waterdata.usgs.gov/blog/boxplots/
 

 

 ##Job complexity edu variable
ggplot(data, aes(x=educ, y=hvlt_total_recall_tscore, color=Site))+geom_point()

 ggplot(data, aes(x=vo2_age, y=hvlt_total_recall_tscore, color=Smoking.Status))+geom_smooth()

   ggplot(data, aes(x=vo2_age, y=bvmt_total_recall_raw, color=Smoking.Status))+geom_smooth()

data$Current.Smoke.factor<-if_else(is.na(data$Current.Smoke.factor), 0, as.double(data$Current.Smoke.factor))
ggplot(data, aes(x=educ, y=hvlt_total_recall_tscore, color=Site))+geom_smooth()+theme(legend.position = "none")

 data$bvmt_total_recall_raw
## LABEL: BVMT Total Recall Raw Score 
## VALUES:
## 23, 25, 12, 27, 21, 17, 18, 13, 17, 18, 22, 26, 23, 9, 20, 28, 20, 31, 24, 25, 30, 32, 30, 29, 29, 28, 31, 26, 34, 23, 18, 25, 23, 17, 26, 26, 34, 23, 19, 25, 19, 30, 22, 16, 13, 16, 16, 11, 22, 20... 50 items printed out of 500
mod<-(lm(bvmt_total_recall_raw~ 
           vo2_gender_summary.factor+
           Weekly_alc.score+
           Current.Smoke.factor+
            poly(vo2_age,2)+
            educ:
            race.factor_white+
           Site,
          data))
summary(mod)
## 
## Call:
## lm(formula = bvmt_total_recall_raw ~ vo2_gender_summary.factor + 
##     Weekly_alc.score + Current.Smoke.factor + poly(vo2_age, 2) + 
##     educ:race.factor_white + Site, data = data)
## 
## Residuals:
## LABEL: BVMT Total Recall Raw Score 
## VALUES:
## -13.4727, -4.0059, 0.3072, 4.0268, 15.0795
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      19.12008    0.96815  19.749  < 2e-16 ***
## vo2_gender_summary.factorFemale   1.17303    0.61177   1.917  0.05576 .  
## Weekly_alc.score                 -0.01588    0.05680  -0.280  0.77997    
## Current.Smoke.factor             -1.74157    1.27231  -1.369  0.17168    
## poly(vo2_age, 2)1               -25.26185    6.05473  -4.172 3.57e-05 ***
## poly(vo2_age, 2)2                10.97179    5.98091   1.834  0.06719 .  
## SiteNEU                          -1.83197    0.69266  -2.645  0.00843 ** 
## SitePITT                         -1.02687    0.63128  -1.627  0.10445    
## educ:race.factor_white            0.20377    0.04252   4.792 2.19e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.954 on 491 degrees of freedom
## Multiple R-squared:  0.1197, Adjusted R-squared:  0.1053 
## F-statistic: 8.344 on 8 and 491 DF,  p-value: 1.218e-10
visreg::visreg(mod,"educ" , by="race.factor_white", overlay=TRUE)


mod<-(lm(bvmt_total_recall_raw~ 
           vo2_gender_summary.factor+
           Weekly_alc.score+
           Current.Smoke.factor+
            poly(vo2_age,2)+
            educ:
            race.factor_white+
           Site,
          data))
summary(mod)
## 
## Call:
## lm(formula = bvmt_total_recall_raw ~ vo2_gender_summary.factor + 
##     Weekly_alc.score + Current.Smoke.factor + poly(vo2_age, 2) + 
##     educ:race.factor_white + Site, data = data)
## 
## Residuals:
## LABEL: BVMT Total Recall Raw Score 
## VALUES:
## -13.4727, -4.0059, 0.3072, 4.0268, 15.0795
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      19.12008    0.96815  19.749  < 2e-16 ***
## vo2_gender_summary.factorFemale   1.17303    0.61177   1.917  0.05576 .  
## Weekly_alc.score                 -0.01588    0.05680  -0.280  0.77997    
## Current.Smoke.factor             -1.74157    1.27231  -1.369  0.17168    
## poly(vo2_age, 2)1               -25.26185    6.05473  -4.172 3.57e-05 ***
## poly(vo2_age, 2)2                10.97179    5.98091   1.834  0.06719 .  
## SiteNEU                          -1.83197    0.69266  -2.645  0.00843 ** 
## SitePITT                         -1.02687    0.63128  -1.627  0.10445    
## educ:race.factor_white            0.20377    0.04252   4.792 2.19e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.954 on 491 degrees of freedom
## Multiple R-squared:  0.1197, Adjusted R-squared:  0.1053 
## F-statistic: 8.344 on 8 and 491 DF,  p-value: 1.218e-10
visreg::visreg(mod,"educ" , by="race.factor_white", overlay=TRUE)

hvlt_total_recall_tscore - Model non-linear main effects

data<-data[complete.cases(data$educ),]
mod1<- lm(hvlt_total_recall_tscore ~
           vo2_sex.factor+
           poly(vo2_age, 2)+ 
           poly(educ,2)+
           vo2_site.factor,
         data=data)

mod2<- lm(hvlt_total_recall_tscore ~
            vo2_sex.factor+
            poly(vo2_age, 2)+ 
            poly(educ, 2)+   
            vo2_site.factor+
            race.factor,
         data=data)

mod3<- lm(hvlt_total_recall_tscore ~
            vo2_sex.factor+
            poly(vo2_age, 2)+ 
            educ+
            vo2_site.factor/
            race.factor,
         data=data)


mod4<- lm(hvlt_total_recall_tscore ~
            vo2_sex.factor+
            poly(vo2_age, 2)+ 
            poly(educ,2)/
            vo2_site.factor/
            race.factor,
         data=data)


mod5<- lm(hvlt_total_recall_tscore ~
            vo2_sex.factor+
            poly(vo2_age, 2)+ 
            educ/
            vo2_site.factor/
            race.factor+
            Current.Smoke.factor,
         data=data)


#data$educ
#data$educ
Dependent variable:
hvlt_total_recall_tscore
(1) (2) (3) (4)
vo2_sex.factorFemale 5.16*** (3.54, 6.78) 5.53*** (3.92, 7.13) 5.59*** (3.98, 7.20) 5.20*** (3.56, 6.84)
poly(vo2_age, 2)1 27.18*** (10.72, 43.63) 26.48*** (10.25, 42.71) 26.18*** (9.84, 42.51) 27.12*** (10.57, 43.66)
poly(vo2_age, 2)2 -18.46** (-34.61, -2.31) -18.86** (-34.79, -2.93) -19.32** (-35.37, -3.27) -17.77** (-34.26, -1.28)
poly(educ, 2)1 44.61*** (28.25, 60.97) 36.91*** (20.34, 53.47) 28.96* (-3.44, 61.36)
poly(educ, 2)2 -19.17** (-35.35, -2.98) -15.20* (-31.28, 0.89) -11.30 (-47.28, 24.68)
educ 0.71*** (0.38, 1.05)
vo2_site.factorKansas 0.06 (-1.65, 1.76) -0.40 (-2.10, 1.29) -0.29 (-2.09, 1.51)
vo2_site.factorNortheastern -1.32 (-3.15, 0.52) -0.98 (-2.81, 0.84) -0.41 (-2.48, 1.67)
race.factorBlack -4.70*** (-6.99, -2.40)
race.factorOther -1.17 (-4.75, 2.41)
vo2_site.factorPitt:race.factorBlack -3.43** (-6.74, -0.13)
vo2_site.factorKansas:race.factorBlack -1.54 (-7.73, 4.65)
vo2_site.factorNortheastern:race.factorBlack -7.59*** (-11.08, -4.10)
vo2_site.factorPitt:race.factorOther -1.51 (-10.88, 7.86)
vo2_site.factorKansas:race.factorOther -3.34 (-9.10, 2.42)
vo2_site.factorNortheastern:race.factorOther 0.48 (-4.80, 5.77)
poly(educ, 2)1:vo2_site.factorKansas -1.03 (-45.00, 42.94)
poly(educ, 2)2:vo2_site.factorKansas -4.36 (-49.30, 40.58)
poly(educ, 2)1:vo2_site.factorNortheastern 15.34 (-35.71, 66.40)
poly(educ, 2)2:vo2_site.factorNortheastern -14.29 (-69.30, 40.72)
poly(educ, 2)1:vo2_site.factorPitt:race.factorBlack 55.82 (-21.25, 132.88)
poly(educ, 2)2:vo2_site.factorPitt:race.factorBlack 15.67 (-52.67, 84.02)
poly(educ, 2)1:vo2_site.factorKansas:race.factorBlack -23.51 (-180.23, 133.21)
poly(educ, 2)2:vo2_site.factorKansas:race.factorBlack 34.34 (-114.99, 183.66)
poly(educ, 2)1:vo2_site.factorNortheastern:race.factorBlack 125.78*** (42.44, 209.13)
poly(educ, 2)2:vo2_site.factorNortheastern:race.factorBlack 47.74 (-54.45, 149.93)
poly(educ, 2)1:vo2_site.factorPitt:race.factorOther -47.37 (-424.66, 329.92)
poly(educ, 2)2:vo2_site.factorPitt:race.factorOther 18.53 (-306.68, 343.73)
poly(educ, 2)1:vo2_site.factorKansas:race.factorOther 47.94 (-112.55, 208.42)
poly(educ, 2)2:vo2_site.factorKansas:race.factorOther 34.09 (-242.89, 311.06)
poly(educ, 2)1:vo2_site.factorNortheastern:race.factorOther -85.27 (-248.47, 77.94)
poly(educ, 2)2:vo2_site.factorNortheastern:race.factorOther 111.43 (-74.17, 297.04)
Constant 50.34*** (48.70, 51.99) 50.79*** (49.16, 52.43) 38.88*** (33.09, 44.68) 50.26*** (48.87, 51.65)
Observations 500 500 500 500
R2 0.15 0.17 0.18 0.18
Adjusted R2 0.13 0.16 0.16 0.14
Residual Std. Error 8.22 (df = 492) 8.11 (df = 490) 8.12 (df = 487) 8.18 (df = 478)
F Statistic 12.02*** (df = 7; 492) 11.41*** (df = 9; 490) 8.73*** (df = 12; 487) 4.95*** (df = 21; 478)
Note: p<0.1; p<0.05; p<0.01

 

hvlt_total_recall_tscore - Test mod 4 - Overfit

Might edu gap explain variations in hvlt scores at NEU? 

## 
## Call:
## lm(formula = hvlt_total_recall_tscore ~ vo2_sex.factor + poly(vo2_age, 
##     2) + poly(educ, 2)/vo2_site.factor/race.factor, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.3677  -5.2901   0.2773   5.6479  18.0574 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                  50.2611     0.7105
## vo2_sex.factorFemale                                          5.2009     0.8350
## poly(vo2_age, 2)1                                            27.1164     8.4420
## poly(vo2_age, 2)2                                           -17.7691     8.4114
## poly(educ, 2)1                                               28.9633    16.5313
## poly(educ, 2)2                                              -11.3021    18.3586
## poly(educ, 2)1:vo2_site.factorKansas                         -1.0334    22.4342
## poly(educ, 2)2:vo2_site.factorKansas                         -4.3577    22.9286
## poly(educ, 2)1:vo2_site.factorNortheastern                   15.3438    26.0484
## poly(educ, 2)2:vo2_site.factorNortheastern                  -14.2897    28.0685
## poly(educ, 2)1:vo2_site.factorPitt:race.factorBlack          55.8177    39.3184
## poly(educ, 2)2:vo2_site.factorPitt:race.factorBlack          15.6748    34.8717
## poly(educ, 2)1:vo2_site.factorKansas:race.factorBlack       -23.5102    79.9612
## poly(educ, 2)2:vo2_site.factorKansas:race.factorBlack        34.3359    76.1878
## poly(educ, 2)1:vo2_site.factorNortheastern:race.factorBlack 125.7844    42.5230
## poly(educ, 2)2:vo2_site.factorNortheastern:race.factorBlack  47.7353    52.1387
## poly(educ, 2)1:vo2_site.factorPitt:race.factorOther         -47.3709   192.4975
## poly(educ, 2)2:vo2_site.factorPitt:race.factorOther          18.5271   165.9243
## poly(educ, 2)1:vo2_site.factorKansas:race.factorOther        47.9351    81.8825
## poly(educ, 2)2:vo2_site.factorKansas:race.factorOther        34.0851   141.3142
## poly(educ, 2)1:vo2_site.factorNortheastern:race.factorOther -85.2682    83.2686
## poly(educ, 2)2:vo2_site.factorNortheastern:race.factorOther 111.4342    94.6980
##                                                             t value Pr(>|t|)
## (Intercept)                                                  70.743  < 2e-16
## vo2_sex.factorFemale                                          6.229 1.03e-09
## poly(vo2_age, 2)1                                             3.212  0.00141
## poly(vo2_age, 2)2                                            -2.112  0.03516
## poly(educ, 2)1                                                1.752  0.08041
## poly(educ, 2)2                                               -0.616  0.53843
## poly(educ, 2)1:vo2_site.factorKansas                         -0.046  0.96328
## poly(educ, 2)2:vo2_site.factorKansas                         -0.190  0.84935
## poly(educ, 2)1:vo2_site.factorNortheastern                    0.589  0.55610
## poly(educ, 2)2:vo2_site.factorNortheastern                   -0.509  0.61092
## poly(educ, 2)1:vo2_site.factorPitt:race.factorBlack           1.420  0.15637
## poly(educ, 2)2:vo2_site.factorPitt:race.factorBlack           0.449  0.65327
## poly(educ, 2)1:vo2_site.factorKansas:race.factorBlack        -0.294  0.76887
## poly(educ, 2)2:vo2_site.factorKansas:race.factorBlack         0.451  0.65243
## poly(educ, 2)1:vo2_site.factorNortheastern:race.factorBlack   2.958  0.00325
## poly(educ, 2)2:vo2_site.factorNortheastern:race.factorBlack   0.916  0.36037
## poly(educ, 2)1:vo2_site.factorPitt:race.factorOther          -0.246  0.80572
## poly(educ, 2)2:vo2_site.factorPitt:race.factorOther           0.112  0.91114
## poly(educ, 2)1:vo2_site.factorKansas:race.factorOther         0.585  0.55855
## poly(educ, 2)2:vo2_site.factorKansas:race.factorOther         0.241  0.80950
## poly(educ, 2)1:vo2_site.factorNortheastern:race.factorOther  -1.024  0.30635
## poly(educ, 2)2:vo2_site.factorNortheastern:race.factorOther   1.177  0.23989
##                                                                
## (Intercept)                                                 ***
## vo2_sex.factorFemale                                        ***
## poly(vo2_age, 2)1                                           ** 
## poly(vo2_age, 2)2                                           *  
## poly(educ, 2)1                                              .  
## poly(educ, 2)2                                                 
## poly(educ, 2)1:vo2_site.factorKansas                           
## poly(educ, 2)2:vo2_site.factorKansas                           
## poly(educ, 2)1:vo2_site.factorNortheastern                     
## poly(educ, 2)2:vo2_site.factorNortheastern                     
## poly(educ, 2)1:vo2_site.factorPitt:race.factorBlack            
## poly(educ, 2)2:vo2_site.factorPitt:race.factorBlack            
## poly(educ, 2)1:vo2_site.factorKansas:race.factorBlack          
## poly(educ, 2)2:vo2_site.factorKansas:race.factorBlack          
## poly(educ, 2)1:vo2_site.factorNortheastern:race.factorBlack ** 
## poly(educ, 2)2:vo2_site.factorNortheastern:race.factorBlack    
## poly(educ, 2)1:vo2_site.factorPitt:race.factorOther            
## poly(educ, 2)2:vo2_site.factorPitt:race.factorOther            
## poly(educ, 2)1:vo2_site.factorKansas:race.factorOther          
## poly(educ, 2)2:vo2_site.factorKansas:race.factorOther          
## poly(educ, 2)1:vo2_site.factorNortheastern:race.factorOther    
## poly(educ, 2)2:vo2_site.factorNortheastern:race.factorOther    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.184 on 478 degrees of freedom
## Multiple R-squared:  0.1786, Adjusted R-squared:  0.1425 
## F-statistic: 4.948 on 21 and 478 DF,  p-value: 1.549e-11

 

 

Dependent variable:
hvlt_total_recall_tscore
vo2_sex.factorFemale 5.0*** (3.4, 6.6)
poly(educ, 2)1 43.5*** (27.2, 59.8)
poly(educ, 2)2 -20.1** (-36.3, -4.0)
poly(vo2_age, 2)1 25.7*** (9.4, 42.0)
poly(vo2_age, 2)2 -19.0** (-35.1, -2.9)
usp_data_anticholinergic_otc.factor 3.3* (-0.2, 6.8)
Constant 50.0*** (48.6, 51.3)
Observations 500
R2 0.1
Adjusted R2 0.1
Residual Std. Error 8.2 (df = 493)
F Statistic 14.2*** (df = 6; 493)
Note: p<0.1; p<0.05; p<0.01

   

 

 

MULTINOMIAL LOGISTIC REGRESSION

https://stats.idre.ucla.edu/r/dae/multinomial-logistic-regression/
 

Adjudication ~ vo2_sex.factor/Weekly_alc.score+vo2_site.factor

## # weights:  21 (12 variable)
## initial  value 549.306144 
## iter  10 value 392.959092
## iter  20 value 388.145238
## iter  20 value 388.145238
## iter  20 value 388.145238
## final  value 388.145238 
## converged

## # weights:  18 (10 variable)
## initial  value 549.306144 
## iter  10 value 397.154656
## final  value 391.285192 
## converged

 

Adjudication ~ hvlt_total_recall_tscore+race.factor+vo2_site.factor

## # weights:  21 (12 variable)
## initial  value 549.306144 
## iter  10 value 346.156713
## iter  20 value 343.193241
## final  value 343.193220 
## converged

 

Adjudication ~ hvlt_total_recall_tscore/Smoking.Status

## # weights:  21 (12 variable)
## initial  value 548.207532 
## iter  10 value 359.830937
## final  value 342.616306 
## converged