miRNA MARCH 2024 FEMALE ONLY Associating E2–>Pain by QUINTILES
Alice Woolard
2024-03-14
#library(edgeR);#library(sva)
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## collapseNEW DATA FILES
#aurora data
df0 <- read.csv("full_aurora.csv")estrogen data
#estrogen levels
e2_conc <- read.csv("E2_concentrated.csv")
#e2_od <- read.csv("E2_OD.csv") #Do I need this data?
#filter out the 0 values, 420 values, and 0 CV values
e2_conc$Mean.Concentration..pg.mL. <- as.numeric(e2_conc$Mean.Concentration..pg.mL.)## Warning: NAs introduced by coercione2_conc$X..CV <- as.numeric(e2_conc$X..CV)## Warning: NAs introduced by coercion#filter add bad samples
e2_conc <- e2_conc %>%
rename(mean.conc = Mean.Concentration..pg.mL.) %>%
filter(mean.conc > 0) %>%
filter(mean.conc < 420) %>%
filter(`X..CV` > 0) %>%
rename(CV = X..CV)
#square root transform concentration
e2_conc <- e2_conc %>%
mutate(sqrt.conc = sqrt(mean.conc)) %>%
select(everything(),mean.conc, sqrt.conc)
#remove extra columns
e2_conc <- e2_conc[, -c(4, 5, 7, 9, 10)] #dimensions 860 x 6keys
#PID to inventory ID key for estrogen
key <- read_excel("e2_pid_key.xlsx")
#sample ID to inventory ID and PID Keys for miRNA
sampleID <- read.csv("ID_to_SampleID_all.csv")
ID_link <- readxl::read_excel("Final Sample List_UNC BSP RNA&Plasma.xlsx")miRNA data
#I don't think updated miRNA data was sent? I'll use the miRNA data from 2021 from this original code
#this file was generated from "AURORA_new_QC_sRNA_06142021.R", which I do not have, this is from Ying
miRNA_cleaned <- read.csv("AURORA_sRNA_cleaned_596_06.14.2021.csv")
miRNA_cleaned$sample <- rownames(miRNA_cleaned)
#miRNA_cleaned dimensions 596 x 1552Link estrogen data with key
e2_keyed <- e2_conc %>%
inner_join(key,by=c("Inventory.Code" = "InventoryCode"))
t0 <- e2_keyed %>%
filter(AlternateID == "T0")
t2 <- e2_keyed %>%
filter(AlternateID == "T1")
t3 <- e2_keyed %>%
filter(AlternateID == "T2")
t4 <- e2_keyed %>%
filter(AlternateID == "T3")
length(unique(t0$PID)) # unique PID 294## [1] 432#the key has 906 inventory ID/PIDs and the estrogen only has 860 samples
length(unique(e2_keyed[["PID"]])) #474## [1] 466Link miRNA with keys
ID2 <- ID_link %>%
right_join(sampleID,by=c("INVENTORY CODE" = "Inventory.code", "SAMPLE ID" = "sampleID_sub"))
ID_miRNA <- miRNA_cleaned %>%
mutate(sample=as.numeric(sample)) %>%
left_join(ID2,by="sample") #596 samples
length(unique(ID_miRNA$PID)) # unique PID 294## [1] 294subset to only the timepoint 0/ED visit miRNA values
ID_miRNA_T0 <- ID_miRNA %>%
filter(`ALT ID` == "T0") #dimensions 294 x 1557March 2024 data Create wide df of estrogen and pain data
#aurora data is df0 #4745 rows
#estrogen data is e2_keyed #906 rows
#combine e2 and aurora data
e2_keyed_full <- e2_keyed %>%
left_join(df0,by=c("PID" = "PID")) #1217 rows x 353 columns## Warning in left_join(., df0, by = c(PID = "PID")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 15 of `x` matches multiple rows in `y`.
## ℹ Row 946 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.#where is the NRS pain score
grep("NRS", names(e2_keyed_full), value = TRUE)## [1] "ED_Somatic_NRS_SUM" "WK2_Somatic_NRS_SUM" "WK8_Somatic_NRS_SUM"
## [4] "M3_Somatic_NRS_SUM" "M6_Somatic_NRS_SUM"#This is only the NRS sum count, so a value greater than 10, not the 0-10 NRS value. Will use something else.
#Do I use ED_NowPain_C or ED_Pain_C for ED pain?
#I don't have the "Date.Run_T0" value for this new set of 2024 estrogen values. maybe I don't need it ?
#added plate number to control for batch effects
#create wide df below
e2_full_wide <- e2_keyed_full[,c("PID","ED_Age","ED_HighestGrade","BMI","ED_RaceEthCode","sqrt.conc","ED_NowPain_C","WK8_Pain_C", "M3_Pain_C", "M6_Pain_C","Plate.Number", "AlternateID")] #dim 1217 x 12
e2_full_wide %>%
inner_join(ID2,by="PID") ## Warning in inner_join(., ID2, by = "PID"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 123 of `x` matches multiple rows in `y`.
## ℹ Row 11 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.## PID ED_Age ED_HighestGrade BMI ED_RaceEthCode
## 1 101394 22 High school graduate NA Non-Hispanic Black
## 2 101394 22 High school graduate NA Non-Hispanic Black
## 3 101394 22 High school graduate NA Non-Hispanic Black
## 4 101394 22 High school graduate NA Non-Hispanic Black
## 5 101394 22 High school graduate NA Non-Hispanic Black
## 6 101394 22 High school graduate NA Non-Hispanic Black
## 7 101394 22 High school graduate NA Non-Hispanic Black
## 8 101394 22 High school graduate NA Non-Hispanic Black
## 9 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 10 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 11 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 12 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 13 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 14 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 15 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 16 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 17 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 18 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 19 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 20 110747 60 Some college, no degree 33.7 Non-Hispanic Black
## 21 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 22 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 23 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 24 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 25 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 26 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 27 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 28 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 29 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 30 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 31 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 32 110965 20 Some college, no degree 25.1 Non-Hispanic White
## 33 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 34 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 35 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 36 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 37 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 38 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 39 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 40 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 41 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 42 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 43 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 44 110813 35 Bachelor's degree: BA, AB, BS, BBA 23.1 Non-Hispanic Black
## 45 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 46 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 47 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 48 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 49 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 50 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 51 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 52 111301 23 Some college, no degree 17.3 Non-Hispanic White
## 53 110468 41 High school graduate 51.1 Non-Hispanic White
## 54 110468 41 High school graduate 51.1 Non-Hispanic White
## 55 110468 41 High school graduate 51.1 Non-Hispanic White
## 56 110468 41 High school graduate 51.1 Non-Hispanic White
## 57 110468 41 High school graduate 51.1 Non-Hispanic White
## 58 110468 41 High school graduate 51.1 Non-Hispanic White
## 59 110468 41 High school graduate 51.1 Non-Hispanic White
## 60 110468 41 High school graduate 51.1 Non-Hispanic White
## 61 111201 21 High school graduate 22.7 Non-Hispanic Black
## 62 111201 21 High school graduate 22.7 Non-Hispanic Black
## 63 111201 21 High school graduate 22.7 Non-Hispanic Black
## 64 111201 21 High school graduate 22.7 Non-Hispanic Black
## 65 111201 21 High school graduate 22.7 Non-Hispanic Black
## 66 111201 21 High school graduate 22.7 Non-Hispanic Black
## 67 111128 29 High school graduate 32.3 Non-Hispanic White
## 68 111128 29 High school graduate 32.3 Non-Hispanic White
## 69 111128 29 High school graduate 32.3 Non-Hispanic White
## 70 111128 29 High school graduate 32.3 Non-Hispanic White
## 71 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 72 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 73 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 74 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 75 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 76 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 77 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 78 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 79 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 80 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 81 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 82 104627 52 Some college, no degree 23.8 Non-Hispanic White
## 83 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 84 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 85 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 86 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 87 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 88 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 89 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 90 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 91 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 92 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 93 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## 94 108726 43 Some college, no degree 34.0 Non-Hispanic Black
## sqrt.conc ED_NowPain_C WK8_Pain_C M3_Pain_C M6_Pain_C Plate.Number
## 1 14.870289 6 0 0 0 4
## 2 14.870289 6 0 0 0 4
## 3 14.870289 6 0 0 0 4
## 4 14.870289 6 0 0 0 4
## 5 14.870289 6 0 0 0 4
## 6 14.870289 6 0 0 0 4
## 7 14.870289 6 0 0 0 4
## 8 14.870289 6 0 0 0 4
## 9 3.411891 4 4 5 3 9
## 10 3.411891 4 4 5 3 9
## 11 3.411891 4 4 5 3 9
## 12 3.411891 4 4 5 3 9
## 13 3.411891 4 4 5 3 9
## 14 3.411891 4 4 5 3 9
## 15 3.411891 4 4 5 3 9
## 16 3.411891 4 4 5 3 9
## 17 3.411891 4 4 5 3 9
## 18 3.411891 4 4 5 3 9
## 19 3.411891 4 4 5 3 9
## 20 3.411891 4 4 5 3 9
## 21 4.063250 5 0 0 0 9
## 22 4.063250 5 0 0 0 9
## 23 4.063250 5 0 0 0 9
## 24 4.063250 5 0 0 0 9
## 25 4.063250 5 0 0 0 9
## 26 4.063250 5 0 0 0 9
## 27 4.063250 5 0 0 0 9
## 28 4.063250 5 0 0 0 9
## 29 4.063250 5 0 0 0 9
## 30 4.063250 5 0 0 0 9
## 31 4.063250 5 0 0 0 9
## 32 4.063250 5 0 0 0 9
## 33 13.573504 4 8 8 7 10
## 34 13.573504 4 8 8 7 10
## 35 13.573504 4 8 8 7 10
## 36 13.573504 4 8 8 7 10
## 37 13.573504 4 8 8 7 10
## 38 13.573504 4 8 8 7 10
## 39 13.573504 4 8 8 7 10
## 40 13.573504 4 8 8 7 10
## 41 13.573504 4 8 8 7 10
## 42 13.573504 4 8 8 7 10
## 43 13.573504 4 8 8 7 10
## 44 13.573504 4 8 8 7 10
## 45 3.735706 6 4 NA 3 10
## 46 3.735706 6 4 NA 3 10
## 47 3.735706 6 4 NA 3 10
## 48 3.735706 6 4 NA 3 10
## 49 3.735706 6 4 NA 3 10
## 50 3.735706 6 4 NA 3 10
## 51 3.735706 6 4 NA 3 10
## 52 3.735706 6 4 NA 3 10
## 53 4.842365 7 NA 6 5 10
## 54 4.842365 7 NA 6 5 10
## 55 4.842365 7 NA 6 5 10
## 56 4.842365 7 NA 6 5 10
## 57 4.842365 7 NA 6 5 10
## 58 4.842365 7 NA 6 5 10
## 59 4.842365 7 NA 6 5 10
## 60 4.842365 7 NA 6 5 10
## 61 6.199113 7 7 6 6 11
## 62 6.199113 7 7 6 6 11
## 63 6.199113 7 7 6 6 11
## 64 6.199113 7 7 6 6 11
## 65 6.199113 7 7 6 6 11
## 66 6.199113 7 7 6 6 11
## 67 4.701542 8 7 7 6 16
## 68 4.701542 8 7 7 6 16
## 69 4.701542 8 7 7 6 16
## 70 4.701542 8 7 7 6 16
## 71 3.681304 6 7 4 8 17
## 72 3.681304 6 7 4 8 17
## 73 3.681304 6 7 4 8 17
## 74 3.681304 6 7 4 8 17
## 75 3.681304 6 7 4 8 17
## 76 3.681304 6 7 4 8 17
## 77 3.681304 6 7 4 8 17
## 78 3.681304 6 7 4 8 17
## 79 3.681304 6 7 4 8 17
## 80 3.681304 6 7 4 8 17
## 81 3.681304 6 7 4 8 17
## 82 3.681304 6 7 4 8 17
## 83 4.382009 5 6 4 4 17
## 84 4.382009 5 6 4 4 17
## 85 4.382009 5 6 4 4 17
## 86 4.382009 5 6 4 4 17
## 87 4.382009 5 6 4 4 17
## 88 4.382009 5 6 4 4 17
## 89 4.382009 5 6 4 4 17
## 90 4.382009 5 6 4 4 17
## 91 4.382009 5 6 4 4 17
## 92 4.382009 5 6 4 4 17
## 93 4.382009 5 6 4 4 17
## 94 4.382009 5 6 4 4 17
## AlternateID SAMPLE ID ALT ID INVENTORY CODE Sample Type sample
## 1 T1 824-1024 T3 XA0050274 RNA1 522
## 2 T1 824-1024 T0 XA0045353 RNA1 2
## 3 T1 824-1024 T3 XA0050274 RNA1 522
## 4 T1 824-1024 T0 XA0045353 RNA1 2
## 5 T1 824-1024 T3 XA0050274 RNA1 522
## 6 T1 824-1024 T0 XA0045353 RNA1 2
## 7 T1 824-1024 T3 XA0050274 RNA1 522
## 8 T1 824-1024 T0 XA0045353 RNA1 2
## 9 T3 846-1109 T1 XA0055516 RNA1 336
## 10 T3 846-1109 T0 XA0055259 RNA1 148
## 11 T3 846-1109 T1 XA0055516 RNA1 336
## 12 T3 846-1109 T0 XA0055259 RNA1 148
## 13 T3 846-1109 T1 XA0055516 RNA1 336
## 14 T3 846-1109 T0 XA0055259 RNA1 148
## 15 T3 846-1109 T1 XA0055516 RNA1 336
## 16 T3 846-1109 T0 XA0055259 RNA1 148
## 17 T3 846-1109 T1 XA0055516 RNA1 336
## 18 T3 846-1109 T0 XA0055259 RNA1 148
## 19 T3 846-1109 T1 XA0055516 RNA1 336
## 20 T3 846-1109 T0 XA0055259 RNA1 148
## 21 T3 846-1112 T1 XA0055530 RNA1 341
## 22 T3 846-1112 T0 XA0055329 RNA1 430
## 23 T3 846-1112 T1 XA0055530 RNA1 341
## 24 T3 846-1112 T0 XA0055329 RNA1 430
## 25 T3 846-1112 T1 XA0055530 RNA1 341
## 26 T3 846-1112 T0 XA0055329 RNA1 430
## 27 T3 846-1112 T1 XA0055530 RNA1 341
## 28 T3 846-1112 T0 XA0055329 RNA1 430
## 29 T3 846-1112 T1 XA0055530 RNA1 341
## 30 T3 846-1112 T0 XA0055329 RNA1 430
## 31 T3 846-1112 T1 XA0055530 RNA1 341
## 32 T3 846-1112 T0 XA0055329 RNA1 430
## 33 T3 846-1110 T1 XA0055534 RNA1 343
## 34 T3 846-1110 T0 XA0055331 RNA1 151
## 35 T3 846-1110 T1 XA0055534 RNA1 343
## 36 T3 846-1110 T0 XA0055331 RNA1 151
## 37 T3 846-1110 T1 XA0055534 RNA1 343
## 38 T3 846-1110 T0 XA0055331 RNA1 151
## 39 T3 846-1110 T1 XA0055534 RNA1 343
## 40 T3 846-1110 T0 XA0055331 RNA1 151
## 41 T3 846-1110 T1 XA0055534 RNA1 343
## 42 T3 846-1110 T0 XA0055331 RNA1 151
## 43 T3 846-1110 T1 XA0055534 RNA1 343
## 44 T3 846-1110 T0 XA0055331 RNA1 151
## 45 T3 846-1117 T1 XA0055521 RNA1 338
## 46 T3 846-1117 T0 XA0055300 RNA1 429
## 47 T3 846-1117 T1 XA0055521 RNA1 338
## 48 T3 846-1117 T0 XA0055300 RNA1 429
## 49 T3 846-1117 T1 XA0055521 RNA1 338
## 50 T3 846-1117 T0 XA0055300 RNA1 429
## 51 T3 846-1117 T1 XA0055521 RNA1 338
## 52 T3 846-1117 T0 XA0055300 RNA1 429
## 53 T3 873-1054 T0 XA0054511 RNA1 299
## 54 T3 873-1054 T1 XA0054622 RNA1 305
## 55 T3 873-1054 T0 XA0054511 RNA1 299
## 56 T3 873-1054 T1 XA0054622 RNA1 305
## 57 T3 873-1054 T0 XA0054511 RNA1 299
## 58 T3 873-1054 T1 XA0054622 RNA1 305
## 59 T3 873-1054 T0 XA0054511 RNA1 299
## 60 T3 873-1054 T1 XA0054622 RNA1 305
## 61 T3 825-1110 T0 XA0055257 RNA1 168
## 62 T3 825-1110 T1 XA0055225 RNA1 575
## 63 T3 825-1110 T0 XA0055257 RNA1 168
## 64 T3 825-1110 T1 XA0055225 RNA1 575
## 65 T3 825-1110 T0 XA0055257 RNA1 168
## 66 T3 825-1110 T1 XA0055225 RNA1 575
## 67 T3 828-1216 T0 XA0054998 RNA1 573
## 68 T3 828-1216 T1 XA0054147 RNA1 119
## 69 T3 828-1216 T0 XA0054998 RNA1 573
## 70 T3 828-1216 T1 XA0054147 RNA1 119
## 71 T3 827-1008 T1 XA0047505 RNA1 485
## 72 T3 827-1008 T0 XA0045856 RNA1 450
## 73 T3 827-1008 T1 XA0047505 RNA1 485
## 74 T3 827-1008 T0 XA0045856 RNA1 450
## 75 T3 827-1008 T1 XA0047505 RNA1 485
## 76 T3 827-1008 T0 XA0045856 RNA1 450
## 77 T3 827-1008 T1 XA0047505 RNA1 485
## 78 T3 827-1008 T0 XA0045856 RNA1 450
## 79 T3 827-1008 T1 XA0047505 RNA1 485
## 80 T3 827-1008 T0 XA0045856 RNA1 450
## 81 T3 827-1008 T1 XA0047505 RNA1 485
## 82 T3 827-1008 T0 XA0045856 RNA1 450
## 83 T3 828-1145 T0 XA0053287 RNA1 406
## 84 T3 828-1145 T1 XA0053178 RNA1 86
## 85 T3 828-1145 T0 XA0053287 RNA1 406
## 86 T3 828-1145 T1 XA0053178 RNA1 86
## 87 T3 828-1145 T0 XA0053287 RNA1 406
## 88 T3 828-1145 T1 XA0053178 RNA1 86
## 89 T3 828-1145 T0 XA0053287 RNA1 406
## 90 T3 828-1145 T1 XA0053178 RNA1 86
## 91 T3 828-1145 T0 XA0053287 RNA1 406
## 92 T3 828-1145 T1 XA0053178 RNA1 86
## 93 T3 828-1145 T0 XA0053287 RNA1 406
## 94 T3 828-1145 T1 XA0053178 RNA1 86#at this above step which includes all time points of E2 for the March 2024 data, there are 94 overlapping PIDs with the 2021 miRNA data
#rename some of the columns
e2_full_wide <- e2_full_wide %>%
rename(ED_Pain = ED_NowPain_C) %>%
rename(WK8_Pain = WK8_Pain_C) %>%
rename(M3_Pain = M3_Pain_C) %>%
rename(M6_Pain = M6_Pain_C) %>%
rename(Race = ED_RaceEthCode)
#Remove cases without estrogen measurements
e2_full_wide <- e2_full_wide[complete.cases(e2_full_wide$sqrt.conc), ] #dimensions 1161 x 12
#Keep ONLY estrogen time point T0, E2 at time of ED visit
e2_full_wide <- e2_full_wide %>%
filter(AlternateID == "T0") #dimensions 558 x 12
#note: after the above step of keeping only T0 values, there remain no overlapping PIDs between the March 2024 E2 data and the 2021 miRNA data.
#summarize wide df and see how many unique PIDs
#summary(e2_full_wide)
length(unique(e2_full_wide[["PID"]])) #432 unique PID## [1] 432NEW MARCH 2024 CODE fix the education category
unique(e2_full_wide$ED_HighestGrade)## [1] "High school graduate"
## [2] "Some college, no degree"
## [3] "Associate degree: Occupational, technical, or vocational program"
## [4] "Bachelor's degree: BA, AB, BS, BBA"
## [5] "GED or equivalent"
## [6] "Associate degree: Academic program"
## [7] "Doctoral degree: PhD, EdD"
## [8] "Master's degree: MA, MS, MEng, MEd, MBA"
## [9] "Professional school degree: MD, DDS, DVM, JD"
## [10] "8th grade"
## [11] "11th grade"
## [12] "12th grade, no diploma"
## [13] "9th grade"
## [14] "10th grade"
## [15] "7th grade"#reduce education categories to 4 levels
level_mapping <- c(
"Some college, no degree" = "Some College or Associate Degree",
"Professional school degree: MD, DDS, DVM, JD" = "Advanced Degree",
"Associate degree: Occupational, technical, or vocational program" = "Some College or Associate Degree",
"High school graduate" = "High School or Less",
"Bachelor's degree: BA, AB, BS, BBA" = "Bachelor's Degree",
"Master's degree: MA, MS, MEng, MEd, MBA" = "Advanced Degree",
"GED or equivalent" = "High School or Less",
"8th grade" = "High School or Less",
"Doctoral degree: PhD, EdD" = "Advanced Degree",
"Associate degree: Academic program" = "Some College or Associate Degree",
"12th grade, no diploma" = "High School or Less",
"11th grade" = "High School or Less",
"9th grade" = "High School or Less",
"10th grade" = "High School or Less",
"7th grade" = "High School or Less"
)
#apply new mapping to education category
e2_full_wide$ED_HighestGrade <- factor(e2_full_wide$ED_HighestGrade, levels = names(level_mapping), labels = level_mapping)
#dim 558 x 12March 2024 data Create long df of estrogen and pain data changes: updated df used, removed Week2 pain rename
gather(e2_full_wide, key=names, value=pain,WK8_Pain,M3_Pain,M6_Pain) %>%
mutate(time = ifelse(names=="WK8_Pain",2,ifelse(names=="M3_Pain",3,ifelse(names=="M6_Pain",6,NA)))) %>%
arrange(PID,time) -> e2_full_long
e2_full_long <- e2_full_long[complete.cases(e2_full_long),] #dim 1541 x 12
length(unique(e2_full_long[["PID"]])) #397 PID## [1] 397#summary(e2_full_long$sqrt.conc)
#there are a few random duplicates that need to be removed
e2_full_long <- e2_full_long[!duplicated(e2_full_long), ] #dim 1163 x 12
length(unique(e2_full_long[["PID"]])) #397 PID## [1] 397MARCH 2024 NEW E2 DATA CODE GLS MODEL
#took out highest grade because its not coded correctly
e2_model <- gls(pain ~ time + sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number, correlation = corAR1(form = ~ time | PID), control = list(singular.ok = FALSE), data=e2_full_long)
summary(e2_model)## Generalized least squares fit by REML
## Model: pain ~ time + sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number
## Data: e2_full_long
## AIC BIC logLik
## 5566.51 5642.223 -2768.255
##
## Correlation Structure: ARMA(1,0)
## Formula: ~time | PID
## Parameter estimate(s):
## Phi1
## 0.73062
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 1.9370128 0.8249753 2.347965 0.0190
## time -0.2372621 0.0453816 -5.228156 0.0000
## sqrt.conc 0.0097417 0.0403274 0.241564 0.8092
## ED_Age 0.0433499 0.0096850 4.475991 0.0000
## ED_HighestGradeAdvanced Degree -1.1040972 0.5188331 -2.128039 0.0335
## ED_HighestGradeHigh School or Less 0.0062920 0.2810954 0.022384 0.9821
## ED_HighestGradeBachelor's Degree -0.6553079 0.3530373 -1.856200 0.0637
## BMI 0.0268486 0.0147667 1.818177 0.0693
## ED_Pain 0.2596334 0.0491419 5.283337 0.0000
## RaceNon-Hispanic Black -0.5629988 0.3746560 -1.502709 0.1332
## RaceNon-Hispanic Other -0.4014009 0.5972709 -0.672058 0.5017
## RaceNon-Hispanic White -0.5940566 0.3903109 -1.522009 0.1283
## Plate.Number -0.0234922 0.0187327 -1.254078 0.2101
##
## Correlation:
## (Intr) time sqrt.c ED_Age ED_HGAD ED_SoL
## time -0.226
## sqrt.conc -0.301 0.001
## ED_Age -0.409 0.004 0.251
## ED_HighestGradeAdvanced Degree -0.219 0.002 -0.015 -0.098
## ED_HighestGradeHigh School or Less -0.245 0.000 0.025 0.105 0.237
## ED_HighestGradeBachelor's Degree -0.156 0.000 0.008 -0.069 0.244 0.339
## BMI -0.465 0.006 -0.101 -0.167 0.069 0.051
## ED_Pain -0.444 0.002 -0.060 0.046 0.136 -0.074
## RaceNon-Hispanic Black -0.310 0.000 -0.050 -0.064 0.221 0.042
## RaceNon-Hispanic Other -0.258 0.002 -0.048 0.027 0.118 -0.002
## RaceNon-Hispanic White -0.316 -0.002 -0.055 -0.089 0.131 0.059
## Plate.Number -0.285 0.000 0.004 -0.022 0.063 0.066
## ED_HGBD BMI ED_Pan RcN-HB RcN-HO RcN-HW
## time
## sqrt.conc
## ED_Age
## ED_HighestGradeAdvanced Degree
## ED_HighestGradeHigh School or Less
## ED_HighestGradeBachelor's Degree
## BMI 0.032
## ED_Pain 0.070 0.013
## RaceNon-Hispanic Black 0.079 -0.011 0.015
## RaceNon-Hispanic Other 0.048 0.022 0.096 0.475
## RaceNon-Hispanic White -0.045 -0.008 0.159 0.718 0.456
## Plate.Number 0.014 0.062 0.076 -0.038 -0.046 -0.108
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -2.096733348 -0.819667252 0.005922671 0.718887652 2.479985354
##
## Residual standard error: 3.005848
## Degrees of freedom: 1163 total; 1150 residualidea.. refactor pain to two categories: low to none and moderate to severe
e2_full_long <- e2_full_long %>%
mutate(bin_pain = cut(pain, breaks = c(-Inf, 4, 10), labels = c("Low to None", "Moderate to Severe"))) #dimensions 1163 x 13
ggplot(e2_full_long, aes(x = sqrt.conc, fill = bin_pain)) +
geom_histogram(binwidth = .2) +
facet_grid(time ~ .) +
labs(x = "Square Root of Estrogen Concentration", y = "Frequency", fill = "Pain Level")
lets look at the e2 values and re-level by QUINTILE
#visualize
summary(e2_full_long$sqrt.conc)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.325 3.867 4.906 5.841 6.844 20.137quantile(e2_full_long$sqrt.conc, probs = seq(0, 1, by = 0.2))## 0% 20% 40% 60% 80% 100%
## 1.325142 3.684359 4.494906 5.371406 7.504166 20.136844q1 <- quantile(e2_full_long$sqrt.conc, 0.2) #first quintile
q5 <- quantile(e2_full_long$sqrt.conc, 0.8) #fifth quintile
ggplot(data = e2_full_long, aes(x = sqrt.conc, y = ..density..)) +
geom_density(color = "deeppink3", fill = "deeppink3", alpha = .4, size=1.8) +
geom_histogram(fill = "skyblue", color = "gray20", alpha = 0.9, bins = 50) +
geom_vline(xintercept = q1, linetype = "dashed", color = "gray30", size = .5) +
geom_vline(xintercept = q5, linetype = "dashed", color = "gray30", size = .5) +
annotate("text", x = q1, y = 0.24, label = paste("20% Q =", round(q1, 2)), color = "gray20", hjust = 1.14, size = 4) +
annotate("text", x = q5, y = 0.24, label = paste("80% Q =", round(q5, 2)), color = "gray20", hjust = -0.1, size = 4) +
labs(x = "Estrogen Level (sqrt.conc)", y = "Density",
title = "Density Plot of Estrogen Levels")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#explore cut off points, pick some levels
quantile(e2_full_long$sqrt.conc, probs = seq(0, 1, by = 0.25))## 0% 25% 50% 75% 100%
## 1.325142 3.867493 4.905558 6.843826 20.136844quantile(e2_full_long$sqrt.conc, probs = seq(0, 1, by = 0.2))## 0% 20% 40% 60% 80% 100%
## 1.325142 3.684359 4.494906 5.371406 7.504166 20.136844length(e2_full_long[e2_full_long$sqrt.conc >= q5, "sqrt.conc"]) #234 values greater than or equal to 5th quintile## [1] 234length(e2_full_long[e2_full_long$sqrt.conc <= q1, "sqrt.conc"]) #234 values less than or equal to 1st quintile## [1] 234length(e2_full_long$sqrt.conc[e2_full_long$sqrt.conc > q1 & e2_full_long$sqrt.conc < q5]) #695 values between 1st and 5th quintile## [1] 695234+234+695 #1163## [1] 1163make new categories based on this exploration..
e2_full_long <- e2_full_long %>%
mutate(e2_bin = case_when(
sqrt.conc <= q1 ~ "low E2 Q1",
sqrt.conc > q1 & sqrt.conc < q5 ~ "mid E2 Q2-Q4",
sqrt.conc >= q5 ~ "high E2 Q5"
))
#new dimensions 1163 x 14summary table with new categories
e2_full_long %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**Full Table: Features by Estrogen Bin, ALL TIME POINTS**") %>%
#add_overall() %>%
add_p()%>%
bold_p()| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 2341 | low E2 Q1, N = 2341 | mid E2 Q2-Q4, N = 6951 | ||
| ED_Age | 32 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | <0.001 | |||
| Some College or Associate Degree | 86 (37%) | 88 (38%) | 292 (42%) | |
| Advanced Degree | 15 (6.4%) | 12 (5.1%) | 54 (7.8%) | |
| High School or Less | 95 (41%) | 67 (29%) | 254 (37%) | |
| Bachelor's Degree | 38 (16%) | 67 (29%) | 95 (14%) | |
| BMI | 31 (9) | 28 (6) | 30 (8) | 0.019 |
| Race | <0.001 | |||
| Hispanic | 29 (12%) | 40 (17%) | 98 (14%) | |
| Non-Hispanic Black | 98 (42%) | 80 (34%) | 336 (48%) | |
| Non-Hispanic Other | 24 (10%) | 15 (6.4%) | 30 (4.3%) | |
| Non-Hispanic White | 83 (35%) | 99 (42%) | 231 (33%) | |
| sqrt.conc | 10.86 (3.04) | 3.00 (0.55) | 5.11 (1.03) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (3) | <0.001 |
| bin_pain | 0.5 | |||
| Low to None | 114 (49%) | 119 (51%) | 368 (53%) | |
| Moderate to Severe | 120 (51%) | 115 (49%) | 327 (47%) | |
| pain | 4 (3) | 5 (3) | 4 (3) | 0.11 |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test | ||||
#summary table for MONTH 2
e2_full_long %>%
filter(time == 2) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**8WK Table: Features by Estrogen Bin, 8 WEEK TIME POINT**") %>%
#add_overall() %>%
add_p() %>%
bold_p()## There was an error in 'add_p()/add_difference()' for variable 'Race', p-value omitted:
## Error in stats::fisher.test(c("Non-Hispanic Black", "Non-Hispanic White", : FEXACT error 6. LDKEY=620 is too small for this problem,
## (ii := key2[itp=922] = 1176440, ldstp=18600)
## Try increasing the size of the workspace and possibly 'mult'| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 771 | low E2 Q1, N = 781 | mid E2 Q2-Q4, N = 2291 | ||
| ED_Age | 32 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | 0.085 | |||
| Some College or Associate Degree | 29 (38%) | 29 (37%) | 97 (42%) | |
| Advanced Degree | 5 (6.5%) | 4 (5.1%) | 18 (7.9%) | |
| High School or Less | 31 (40%) | 22 (28%) | 82 (36%) | |
| Bachelor's Degree | 12 (16%) | 23 (29%) | 32 (14%) | |
| BMI | 31 (9) | 28 (6) | 31 (8) | 0.2 |
| Race | ||||
| Hispanic | 9 (12%) | 13 (17%) | 33 (14%) | |
| Non-Hispanic Black | 33 (43%) | 26 (33%) | 111 (48%) | |
| Non-Hispanic Other | 8 (10%) | 5 (6.4%) | 10 (4.4%) | |
| Non-Hispanic White | 27 (35%) | 34 (44%) | 75 (33%) | |
| sqrt.conc | 10.87 (3.08) | 2.99 (0.57) | 5.12 (1.03) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (2) | 0.008 |
| bin_pain | 0.2 | |||
| Low to None | 28 (36%) | 37 (47%) | 109 (48%) | |
| Moderate to Severe | 49 (64%) | 41 (53%) | 120 (52%) | |
| pain | 5 (3) | 5 (3) | 5 (3) | 0.13 |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test | ||||
#summary table for MONTH 3
e2_full_long %>%
filter(time == 3) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**M3 Table: Features by Estrogen Bin, MONTH 3 TIME POINT**") %>%
#add_overall() %>%
add_p() %>%
bold_p()| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 771 | low E2 Q1, N = 761 | mid E2 Q2-Q4, N = 2291 | ||
| ED_Age | 33 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | 0.14 | |||
| Some College or Associate Degree | 28 (36%) | 29 (38%) | 95 (41%) | |
| Advanced Degree | 5 (6.5%) | 4 (5.3%) | 18 (7.9%) | |
| High School or Less | 32 (42%) | 22 (29%) | 85 (37%) | |
| Bachelor's Degree | 12 (16%) | 21 (28%) | 31 (14%) | |
| BMI | 31 (10) | 28 (6) | 30 (9) | 0.3 |
| Race | 0.3 | |||
| Hispanic | 10 (13%) | 13 (17%) | 32 (14%) | |
| Non-Hispanic Black | 32 (42%) | 27 (36%) | 110 (48%) | |
| Non-Hispanic Other | 8 (10%) | 5 (6.6%) | 10 (4.4%) | |
| Non-Hispanic White | 27 (35%) | 31 (41%) | 77 (34%) | |
| sqrt.conc | 10.85 (3.01) | 3.01 (0.54) | 5.11 (1.03) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (3) | 0.029 |
| bin_pain | 0.8 | |||
| Low to None | 39 (51%) | 37 (49%) | 120 (52%) | |
| Moderate to Severe | 38 (49%) | 39 (51%) | 109 (48%) | |
| pain | 4 (4) | 5 (3) | 4 (3) | 0.4 |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | ||||
#summary table for MONTH 6
e2_full_long %>%
filter(time == 6) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**M6 Table: Features by Estrogen Bin, MONTH 6 TIME POINT**") %>%
#add_overall() %>%
add_p() %>%
bold_p()## There was an error in 'add_p()/add_difference()' for variable 'Race', p-value omitted:
## Error in stats::fisher.test(c("Non-Hispanic Black", "Non-Hispanic White", : FEXACT error 6. LDKEY=620 is too small for this problem,
## (ii := key2[itp=638] = 1487357, ldstp=18600)
## Try increasing the size of the workspace and possibly 'mult'| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 801 | low E2 Q1, N = 801 | mid E2 Q2-Q4, N = 2371 | ||
| ED_Age | 32 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | 0.092 | |||
| Some College or Associate Degree | 29 (36%) | 30 (38%) | 100 (42%) | |
| Advanced Degree | 5 (6.3%) | 4 (5.0%) | 18 (7.6%) | |
| High School or Less | 32 (40%) | 23 (29%) | 87 (37%) | |
| Bachelor's Degree | 14 (18%) | 23 (29%) | 32 (14%) | |
| BMI | 31 (9) | 28 (6) | 30 (8) | 0.3 |
| Race | ||||
| Hispanic | 10 (13%) | 14 (18%) | 33 (14%) | |
| Non-Hispanic Black | 33 (41%) | 27 (34%) | 115 (49%) | |
| Non-Hispanic Other | 8 (10%) | 5 (6.3%) | 10 (4.2%) | |
| Non-Hispanic White | 29 (36%) | 34 (43%) | 79 (33%) | |
| sqrt.conc | 10.84 (3.06) | 2.99 (0.56) | 5.11 (1.02) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (3) | 0.024 |
| bin_pain | >0.9 | |||
| Low to None | 47 (59%) | 45 (56%) | 139 (59%) | |
| Moderate to Severe | 33 (41%) | 35 (44%) | 98 (41%) | |
| pain | 4 (3) | 4 (3) | 4 (3) | 0.4 |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test | ||||
i don’t know. it’s not signficant when stratified by time point. I think this is because it is auto-correlating when all time points are combined because you have three of each case
visuals
ggplot(e2_full_long, aes(x = pain, fill = e2_bin)) +
geom_density(alpha = 0.5) +
labs(x = "Pain Across All Time Points", y = "Density", title = "Pain All Time Points, by Estrogen Bin") +
scale_fill_manual(values = c("low E2 Q1" = "blue", "mid E2 Q2-Q4" = "green", "high E2 Q5" = "red")) 
#high and mid E2 peak at 0 pain. Everything mostly falls off at pain 10.
#BELOW: PAIN AT 2 MONTHS. Mostly mid level pain everyone
ggplot(data = e2_full_long %>% filter(time == 2), aes(x = pain, fill = e2_bin)) +
geom_density(alpha = 0.5) +
labs(x = "Pain at 2 MONTHS", y = "Density", title = "Pain at 2 MONTHS, by Estrogen Category") +
scale_fill_manual(values = c("low E2 Q1" = "blue", "mid E2 Q2-Q4" = "green", "high E2 Q5" = "red")) 
#BELOW: PAIN AT 3 MONTHS, mostly mid level pain, high and mid e2 start to bounce back down to zero
ggplot(data = e2_full_long %>% filter(time == 3), aes(x = pain, fill = e2_bin)) +
geom_density(alpha = 0.5) +
labs(x = "Pain at 3 MONTHS", y = "Density", title = "Pain at 3 MONTHS, by Estrogen Category") +
scale_fill_manual(values = c("low E2 Q1" = "blue", "mid E2 Q2-Q4" = "green", "high E2 Q5" = "red")) 
#BELOW: PAIN AT 6 MONTHS. Mid and high e2 have a strong peak at 0 pain, and high e2 has another strong bump at ~6 pain. Low e2 is mildly right skewed towards 10.
ggplot(data = e2_full_long %>% filter(time == 6), aes(x = pain, fill = e2_bin)) +
geom_density(alpha = 0.5) +
labs(x = "Pain at 6 MONTHS", y = "Density", title = "Pain at 6 MONTHS, by Estrogen Category") +
scale_fill_manual(values = c("low E2 Q1" = "blue", "mid E2 Q2-Q4" = "green", "high E2 Q5" = "red")) 
We see that low E2 has fewer 0s and more 10s at 6 months
#high E2 across each time point
ggplot(e2_full_long %>% filter(e2_bin == "high E2 Q5"), aes(x = pain, fill = as.factor(time))) +
geom_histogram(binwidth = 1, position = "dodge", alpha = 0.7, color="black") +
labs(title = "High E2 x Pain",
x = "Pain", y = "Frequency")
#mid E2 across each time point
ggplot(e2_full_long %>% filter(e2_bin == "mid E2 Q2-Q4"), aes(x = pain, fill = as.factor(time))) +
geom_histogram(binwidth = 1, position = "dodge", alpha = 0.7, color="black") +
labs(title = "Mid E2 x Pain",
x = "Pain", y = "Frequency")
#low E2 across each time point
ggplot(e2_full_long %>% filter(e2_bin == "low E2 Q1"), aes(x = pain, fill = as.factor(time))) +
geom_histogram(binwidth = 1, position = "dodge", alpha = 0.7, color="black") +
labs(title = "Low E2 x Pain",
x = "Pain", y = "Frequency")
#High and Low E2 level at month 2 - way more 0s for high E2 and more 10s for low E2
ggplot(e2_full_long %>% filter(time == 2, e2_bin %in% c("high E2 Q5", "low E2 Q1")), aes(x = pain, fill = e2_bin)) +
geom_histogram(binwidth = .6, position = "dodge", color="black") +
scale_fill_manual(values = c("high E2 Q5" = "paleturquoise3", "low E2 Q1" = "lightcoral")) +
labs(title = "Pain at Month 2 by High and Low E2",
x = "Pain Score",
y = "Frequency")
#High and Low E2 level at month 3 - - way more 0s for high E2 and more 10s for low E2
ggplot(e2_full_long %>% filter(time == 3, e2_bin %in% c("high E2 Q5", "low E2 Q1")), aes(x = pain, fill = e2_bin)) +
geom_histogram(binwidth = .6, position = "dodge", color="black") +
scale_fill_manual(values = c("high E2 Q5" = "paleturquoise3", "low E2 Q1" = "lightcoral")) +
labs(title = "Pain at Month 3 by High and Low E2",
x = "Pain Score",
y = "Frequency")
#High and Low E2 level at month 6 - - way more 0s for high E2 and more 10s for low E2
ggplot(e2_full_long %>% filter(time == 6, e2_bin %in% c("high E2 Q5", "low E2 Q1")), aes(x = pain, fill = e2_bin)) +
geom_histogram(binwidth = .6, position = "dodge", color="black") +
scale_fill_manual(values = c("high E2 Q5" = "paleturquoise3", "low E2 Q1" = "lightcoral")) +
labs(title = "Pain at Month 6 by High and Low E2",
x = "Pain Score",
y = "Frequency")
chi square ?
table(e2_full_long$e2_bin, as.factor(e2_full_long$pain))##
## 0 1 2 3 4 5 6 7 8 9 10
## high E2 Q5 57 10 11 15 21 19 27 27 21 7 19
## low E2 Q1 31 22 13 27 26 27 22 17 22 3 24
## mid E2 Q2-Q4 134 60 45 67 62 93 80 48 44 29 33table(e2_full_long$e2_bin, e2_full_long$bin_pain)##
## Low to None Moderate to Severe
## high E2 Q5 114 120
## low E2 Q1 119 115
## mid E2 Q2-Q4 368 327chisq.test(e2_full_long$e2_bin, as.factor(e2_full_long$pain))##
## Pearson's Chi-squared test
##
## data: e2_full_long$e2_bin and as.factor(e2_full_long$pain)
## X-squared = 44.554, df = 20, p-value = 0.001268chisq.test(e2_full_long$e2_bin, e2_full_long$bin_pain) #p-value = 0.1896##
## Pearson's Chi-squared test
##
## data: e2_full_long$e2_bin and e2_full_long$bin_pain
## X-squared = 1.3346, df = 2, p-value = 0.5131kruskal.test(pain ~ e2_bin, data = e2_full_long) # p-value = 0.00459##
## Kruskal-Wallis rank sum test
##
## data: pain by e2_bin
## Kruskal-Wallis chi-squared = 4.4146, df = 2, p-value = 0.11make new levels for pain
e2_full_long <- e2_full_long %>%
mutate(bin_pain2 = case_when(
pain < 2 ~ "0 or 1 pain",
pain > 1 & pain < 9 ~ "2 to 8 pain",
pain > 8 ~ "9 or 10 pain"
))
#new dimensions 1163 x 15
e2_full_long <- e2_full_long %>%
mutate(bin_pain3 = case_when(
pain < 1 ~ "0 pain",
pain > 0 & pain < 10 ~ "1 to 9 pain",
pain > 9 ~ "10 pain"
))
#new dimensions 1163 x 16summary table
#each time point
e2_full_long %>%
#filter(time == 2) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin, bin_pain2, bin_pain3) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**Combined Table: Features by Estrogen Bin, ALL TIME POINTS**") %>%
#add_overall() %>%
add_p() %>%
bold_p()| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 2341 | low E2 Q1, N = 2341 | mid E2 Q2-Q4, N = 6951 | ||
| ED_Age | 32 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | <0.001 | |||
| Some College or Associate Degree | 86 (37%) | 88 (38%) | 292 (42%) | |
| Advanced Degree | 15 (6.4%) | 12 (5.1%) | 54 (7.8%) | |
| High School or Less | 95 (41%) | 67 (29%) | 254 (37%) | |
| Bachelor's Degree | 38 (16%) | 67 (29%) | 95 (14%) | |
| BMI | 31 (9) | 28 (6) | 30 (8) | 0.019 |
| Race | <0.001 | |||
| Hispanic | 29 (12%) | 40 (17%) | 98 (14%) | |
| Non-Hispanic Black | 98 (42%) | 80 (34%) | 336 (48%) | |
| Non-Hispanic Other | 24 (10%) | 15 (6.4%) | 30 (4.3%) | |
| Non-Hispanic White | 83 (35%) | 99 (42%) | 231 (33%) | |
| sqrt.conc | 10.86 (3.04) | 3.00 (0.55) | 5.11 (1.03) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (3) | <0.001 |
| bin_pain | 0.5 | |||
| Low to None | 114 (49%) | 119 (51%) | 368 (53%) | |
| Moderate to Severe | 120 (51%) | 115 (49%) | 327 (47%) | |
| pain | 4 (3) | 5 (3) | 4 (3) | 0.11 |
| bin_pain2 | 0.4 | |||
| 0 or 1 pain | 67 (29%) | 53 (23%) | 194 (28%) | |
| 2 to 8 pain | 141 (60%) | 154 (66%) | 439 (63%) | |
| 9 or 10 pain | 26 (11%) | 27 (12%) | 62 (8.9%) | |
| bin_pain3 | <0.001 | |||
| 0 pain | 57 (24%) | 31 (13%) | 134 (19%) | |
| 1 to 9 pain | 158 (68%) | 179 (76%) | 528 (76%) | |
| 10 pain | 19 (8.1%) | 24 (10%) | 33 (4.7%) | |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test | ||||
#bin pain 2 and bin pain 3 significant (p<.001)
#month 2
e2_full_long %>%
filter(time == 2) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin, bin_pain2, bin_pain3) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**WK8 Table: Features by Estrogen Bin, 8 weeks**") %>%
#add_overall() %>%
add_p(test.args = c(bin_pain2,bin_pain3) ~ list(workspace=2e9)) %>%
bold_p()## There was an error in 'add_p()/add_difference()' for variable 'Race', p-value omitted:
## Error in stats::fisher.test(c("Non-Hispanic Black", "Non-Hispanic White", : FEXACT error 6. LDKEY=620 is too small for this problem,
## (ii := key2[itp=922] = 1176440, ldstp=18600)
## Try increasing the size of the workspace and possibly 'mult'## Note for variable 'bin_pain2': Argument(s) 'test.args' do not apply and were
## ignored. See `?tests` for details.
## Note for variable 'bin_pain3': Argument(s) 'test.args' do not apply and were
## ignored. See `?tests` for details.| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 771 | low E2 Q1, N = 781 | mid E2 Q2-Q4, N = 2291 | ||
| ED_Age | 32 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | 0.085 | |||
| Some College or Associate Degree | 29 (38%) | 29 (37%) | 97 (42%) | |
| Advanced Degree | 5 (6.5%) | 4 (5.1%) | 18 (7.9%) | |
| High School or Less | 31 (40%) | 22 (28%) | 82 (36%) | |
| Bachelor's Degree | 12 (16%) | 23 (29%) | 32 (14%) | |
| BMI | 31 (9) | 28 (6) | 31 (8) | 0.2 |
| Race | ||||
| Hispanic | 9 (12%) | 13 (17%) | 33 (14%) | |
| Non-Hispanic Black | 33 (43%) | 26 (33%) | 111 (48%) | |
| Non-Hispanic Other | 8 (10%) | 5 (6.4%) | 10 (4.4%) | |
| Non-Hispanic White | 27 (35%) | 34 (44%) | 75 (33%) | |
| sqrt.conc | 10.87 (3.08) | 2.99 (0.57) | 5.12 (1.03) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (2) | 0.008 |
| bin_pain | 0.2 | |||
| Low to None | 28 (36%) | 37 (47%) | 109 (48%) | |
| Moderate to Severe | 49 (64%) | 41 (53%) | 120 (52%) | |
| pain | 5 (3) | 5 (3) | 5 (3) | 0.13 |
| bin_pain2 | 0.8 | |||
| 0 or 1 pain | 13 (17%) | 12 (15%) | 47 (21%) | |
| 2 to 8 pain | 54 (70%) | 58 (74%) | 155 (68%) | |
| 9 or 10 pain | 10 (13%) | 8 (10%) | 27 (12%) | |
| bin_pain3 | 0.6 | |||
| 0 pain | 11 (14%) | 7 (9.0%) | 30 (13%) | |
| 1 to 9 pain | 58 (75%) | 64 (82%) | 185 (81%) | |
| 10 pain | 8 (10%) | 7 (9.0%) | 14 (6.1%) | |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test | ||||
#no sig in pain
#month 3
e2_full_long %>%
filter(time == 3) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin, bin_pain2, bin_pain3) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**M3 Table: Features by Estrogen Bin, 3 months**") %>%
#add_overall() %>%
add_p(test.args = c(bin_pain2,bin_pain3) ~ list(workspace=2e9)) %>%
bold_p()## Note for variable 'bin_pain2': Argument(s) 'test.args' do not apply and were
## ignored. See `?tests` for details.| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 771 | low E2 Q1, N = 761 | mid E2 Q2-Q4, N = 2291 | ||
| ED_Age | 33 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | 0.14 | |||
| Some College or Associate Degree | 28 (36%) | 29 (38%) | 95 (41%) | |
| Advanced Degree | 5 (6.5%) | 4 (5.3%) | 18 (7.9%) | |
| High School or Less | 32 (42%) | 22 (29%) | 85 (37%) | |
| Bachelor's Degree | 12 (16%) | 21 (28%) | 31 (14%) | |
| BMI | 31 (10) | 28 (6) | 30 (9) | 0.3 |
| Race | 0.3 | |||
| Hispanic | 10 (13%) | 13 (17%) | 32 (14%) | |
| Non-Hispanic Black | 32 (42%) | 27 (36%) | 110 (48%) | |
| Non-Hispanic Other | 8 (10%) | 5 (6.6%) | 10 (4.4%) | |
| Non-Hispanic White | 27 (35%) | 31 (41%) | 77 (34%) | |
| sqrt.conc | 10.85 (3.01) | 3.01 (0.54) | 5.11 (1.03) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (3) | 0.029 |
| bin_pain | 0.8 | |||
| Low to None | 39 (51%) | 37 (49%) | 120 (52%) | |
| Moderate to Severe | 38 (49%) | 39 (51%) | 109 (48%) | |
| pain | 4 (4) | 5 (3) | 4 (3) | 0.4 |
| bin_pain2 | 0.3 | |||
| 0 or 1 pain | 25 (32%) | 16 (21%) | 64 (28%) | |
| 2 to 8 pain | 42 (55%) | 52 (68%) | 147 (64%) | |
| 9 or 10 pain | 10 (13%) | 8 (11%) | 18 (7.9%) | |
| bin_pain3 | 0.054 | |||
| 0 pain | 20 (26%) | 10 (13%) | 42 (18%) | |
| 1 to 9 pain | 50 (65%) | 59 (78%) | 178 (78%) | |
| 10 pain | 7 (9.1%) | 7 (9.2%) | 9 (3.9%) | |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | ||||
#no sig in pain
#month 6
e2_full_long %>%
filter(time == 6) %>%
select(ED_Age, ED_HighestGrade, BMI, Race, sqrt.conc, ED_Pain, bin_pain, pain, e2_bin, bin_pain2, bin_pain3) %>%
tbl_summary(by = e2_bin,
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Relative Estrogen Bin**") %>%
modify_caption("**M6 Table: Features by Estrogen Bin, 6 months**") %>%
#add_overall() %>%
add_p(test.args = c(bin_pain2,bin_pain3) ~ list(workspace=2e9)) %>%
bold_p()## There was an error in 'add_p()/add_difference()' for variable 'Race', p-value omitted:
## Error in stats::fisher.test(c("Non-Hispanic Black", "Non-Hispanic White", : FEXACT error 6. LDKEY=620 is too small for this problem,
## (ii := key2[itp=638] = 1487357, ldstp=18600)
## Try increasing the size of the workspace and possibly 'mult'
## Note for variable 'bin_pain2': Argument(s) 'test.args' do not apply and were
## ignored. See `?tests` for details.| Characteristic | Relative Estrogen Bin | p-value2 | ||
|---|---|---|---|---|
| high E2 Q5, N = 801 | low E2 Q1, N = 801 | mid E2 Q2-Q4, N = 2371 | ||
| ED_Age | 32 (10) | 44 (13) | 37 (14) | <0.001 |
| ED_HighestGrade | 0.092 | |||
| Some College or Associate Degree | 29 (36%) | 30 (38%) | 100 (42%) | |
| Advanced Degree | 5 (6.3%) | 4 (5.0%) | 18 (7.6%) | |
| High School or Less | 32 (40%) | 23 (29%) | 87 (37%) | |
| Bachelor's Degree | 14 (18%) | 23 (29%) | 32 (14%) | |
| BMI | 31 (9) | 28 (6) | 30 (8) | 0.3 |
| Race | ||||
| Hispanic | 10 (13%) | 14 (18%) | 33 (14%) | |
| Non-Hispanic Black | 33 (41%) | 27 (34%) | 115 (49%) | |
| Non-Hispanic Other | 8 (10%) | 5 (6.3%) | 10 (4.2%) | |
| Non-Hispanic White | 29 (36%) | 34 (43%) | 79 (33%) | |
| sqrt.conc | 10.84 (3.06) | 2.99 (0.56) | 5.11 (1.02) | <0.001 |
| ED_Pain | 7 (3) | 6 (3) | 6 (3) | 0.024 |
| bin_pain | >0.9 | |||
| Low to None | 47 (59%) | 45 (56%) | 139 (59%) | |
| Moderate to Severe | 33 (41%) | 35 (44%) | 98 (41%) | |
| pain | 4 (3) | 4 (3) | 4 (3) | 0.4 |
| bin_pain2 | 0.5 | |||
| 0 or 1 pain | 29 (36%) | 25 (31%) | 83 (35%) | |
| 2 to 8 pain | 45 (56%) | 44 (55%) | 137 (58%) | |
| 9 or 10 pain | 6 (7.5%) | 11 (14%) | 17 (7.2%) | |
| bin_pain3 | 0.036 | |||
| 0 pain | 26 (33%) | 14 (18%) | 62 (26%) | |
| 1 to 9 pain | 50 (63%) | 56 (70%) | 165 (70%) | |
| 10 pain | 4 (5.0%) | 10 (13%) | 10 (4.2%) | |
| 1 Mean (SD); n (%) | ||||
| 2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | ||||
#bin pain 3 sig (p=0.036)stats for new categories
table(e2_full_long$e2_bin, e2_full_long$bin_pain3)##
## 0 pain 1 to 9 pain 10 pain
## high E2 Q5 57 158 19
## low E2 Q1 31 179 24
## mid E2 Q2-Q4 134 528 33round(prop.table(table(e2_full_long$e2_bin, e2_full_long$bin_pain3), margin = 1) * 100,2)##
## 0 pain 1 to 9 pain 10 pain
## high E2 Q5 24.36 67.52 8.12
## low E2 Q1 13.25 76.50 10.26
## mid E2 Q2-Q4 19.28 75.97 4.75chisq.test(e2_full_long[e2_full_long$time == 2, "e2_bin"], e2_full_long[e2_full_long$time == 2, "bin_pain3"]) # p-value = 0.56##
## Pearson's Chi-squared test
##
## data: e2_full_long[e2_full_long$time == 2, "e2_bin"] and e2_full_long[e2_full_long$time == 2, "bin_pain3"]
## X-squared = 2.9646, df = 4, p-value = 0.5638chisq.test(e2_full_long[e2_full_long$time == 3, "e2_bin"], e2_full_long[e2_full_long$time == 3, "bin_pain3"]) #p-value = 0.064## Warning in chisq.test(e2_full_long[e2_full_long$time == 3, "e2_bin"],
## e2_full_long[e2_full_long$time == : Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: e2_full_long[e2_full_long$time == 3, "e2_bin"] and e2_full_long[e2_full_long$time == 3, "bin_pain3"]
## X-squared = 8.8979, df = 4, p-value = 0.0637chisq.test(e2_full_long[e2_full_long$time == 6, "e2_bin"], e2_full_long[e2_full_long$time == 6, "bin_pain3"]) #p-value = 0.02649## Warning in chisq.test(e2_full_long[e2_full_long$time == 6, "e2_bin"],
## e2_full_long[e2_full_long$time == : Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: e2_full_long[e2_full_long$time == 6, "e2_bin"] and e2_full_long[e2_full_long$time == 6, "bin_pain3"]
## X-squared = 11.007, df = 4, p-value = 0.02649redo model from above
MARCH 2024 NEW E2 DATA CODE GLS MODEL
e2_model <- gls(pain ~ time + sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin, correlation = corAR1(form = ~ time | PID), control = list(singular.ok = FALSE), data=e2_full_long)
summary(e2_model)## Generalized least squares fit by REML
## Model: pain ~ time + sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin
## Data: e2_full_long
## AIC BIC logLik
## 5568.24 5654.018 -2767.12
##
## Correlation Structure: ARMA(1,0)
## Formula: ~time | PID
## Parameter estimate(s):
## Phi1
## 0.730651
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) 2.1746068 1.2202312 1.782127 0.0750
## time -0.2371992 0.0453832 -5.226584 0.0000
## sqrt.conc 0.0018124 0.0761957 0.023786 0.9810
## ED_Age 0.0412959 0.0098138 4.207956 0.0000
## ED_HighestGradeAdvanced Degree -1.0719852 0.5218861 -2.054060 0.0402
## ED_HighestGradeHigh School or Less 0.0023889 0.2818121 0.008477 0.9932
## ED_HighestGradeBachelor's Degree -0.7106412 0.3553445 -1.999865 0.0458
## BMI 0.0290101 0.0148775 1.949933 0.0514
## ED_Pain 0.2548956 0.0496970 5.128991 0.0000
## RaceNon-Hispanic Black -0.5212492 0.3760168 -1.386239 0.1659
## RaceNon-Hispanic Other -0.4379841 0.6004024 -0.729484 0.4659
## RaceNon-Hispanic White -0.5840843 0.3911167 -1.493376 0.1356
## Plate.Number -0.0251045 0.0188112 -1.334551 0.1823
## e2_binlow E2 Q1 0.1232773 0.7186841 0.171532 0.8638
## e2_binmid E2 Q2-Q4 -0.2817202 0.5423023 -0.519489 0.6035
##
## Correlation:
## (Intr) time sqrt.c ED_Age ED_HGAD ED_SoL
## time -0.156
## sqrt.conc -0.729 0.004
## ED_Age -0.254 0.003 0.096
## ED_HighestGradeAdvanced Degree -0.212 0.003 0.071 -0.109
## ED_HighestGradeHigh School or Less -0.217 0.001 0.073 0.101 0.241
## ED_HighestGradeBachelor's Degree -0.129 0.000 0.023 -0.052 0.237 0.338
## BMI -0.341 0.006 -0.010 -0.183 0.079 0.054
## ED_Pain -0.402 0.002 0.085 0.048 0.144 -0.063
## RaceNon-Hispanic Black -0.216 0.000 -0.011 -0.076 0.225 0.043
## RaceNon-Hispanic Other -0.246 0.002 0.055 0.028 0.123 0.005
## RaceNon-Hispanic White -0.254 -0.001 0.020 -0.093 0.137 0.063
## Plate.Number -0.153 -0.001 -0.049 -0.009 0.053 0.062
## e2_binlow E2 Q1 -0.700 0.005 0.829 -0.074 0.102 0.068
## e2_binmid E2 Q2-Q4 -0.723 0.004 0.812 0.005 0.074 0.068
## ED_HGBD BMI ED_Pan RcN-HB RcN-HO RcN-HW
## time
## sqrt.conc
## ED_Age
## ED_HighestGradeAdvanced Degree
## ED_HighestGradeHigh School or Less
## ED_HighestGradeBachelor's Degree
## BMI 0.020
## ED_Pain 0.078 0.013
## RaceNon-Hispanic Black 0.070 -0.001 0.013
## RaceNon-Hispanic Other 0.054 0.023 0.110 0.469
## RaceNon-Hispanic White -0.046 -0.003 0.164 0.717 0.457
## Plate.Number 0.020 0.051 0.070 -0.044 -0.049 -0.113
## e2_binlow E2 Q1 -0.002 0.073 0.122 0.035 0.085 0.062
## e2_binmid E2 Q2-Q4 0.053 0.017 0.148 -0.007 0.101 0.049
## Plt.Nm e2_E2Q1
## time
## sqrt.conc
## ED_Age
## ED_HighestGradeAdvanced Degree
## ED_HighestGradeHigh School or Less
## ED_HighestGradeBachelor's Degree
## BMI
## ED_Pain
## RaceNon-Hispanic Black
## RaceNon-Hispanic Other
## RaceNon-Hispanic White
## Plate.Number
## e2_binlow E2 Q1 -0.073
## e2_binmid E2 Q2-Q4 -0.038 0.873
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -2.102632201 -0.800003712 0.006275637 0.709551986 2.525482869
##
## Residual standard error: 3.006026
## Degrees of freedom: 1163 total; 1148 residual#recode the factors so the binned pain is the outcome of interest
e2_full_long$bin_pain4 <- recode_factor(e2_full_long$bin_pain3,
`0 pain` = "1",
`1 to 9 pain` = "2",
`10 pain` = "3") #new dimensions 1163 x 17#multinomial logit regression
e2_model <- multinom(bin_pain4 ~ time + sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin, correlation = corAR1(form = ~ time | PID), control = list(singular.ok = FALSE), data=e2_full_long) #e2_bin low E2 p = 2.398452e-03 = 0.002398452 ## # weights: 48 (30 variable)
## initial value 1277.686092
## iter 10 value 977.801520
## iter 20 value 815.371103
## iter 30 value 769.544655
## iter 40 value 766.446223
## final value 766.445652
## converged#library(kableExtra)
tidy(e2_model, conf.int = TRUE) %>%
kable() %>% kable_styling("basic", full_width = FALSE)| y.level | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| 2 | (Intercept) | 0.1703722 | 0.7650969 | 0.2226806 | 0.8237841 | -1.3291901 | 1.6699346 |
| 2 | time | -0.1979691 | 0.0442244 | -4.4764671 | 0.0000076 | -0.2846473 | -0.1112909 |
| 2 | sqrt.conc | 0.0363906 | 0.0458435 | 0.7938017 | 0.4273109 | -0.0534609 | 0.1262422 |
| 2 | ED_Age | 0.0251792 | 0.0066958 | 3.7604728 | 0.0001696 | 0.0120558 | 0.0383027 |
| 2 | ED_HighestGradeAdvanced Degree | 0.1104983 | 0.3435053 | 0.3216786 | 0.7476962 | -0.5627598 | 0.7837564 |
| 2 | ED_HighestGradeHigh School or Less | -0.0807259 | 0.1795967 | -0.4494841 | 0.6530825 | -0.4327289 | 0.2712772 |
| 2 | ED_HighestGradeBachelor’s Degree | 0.2136520 | 0.2403628 | 0.8888728 | 0.3740715 | -0.2574505 | 0.6847544 |
| 2 | BMI | 0.0117216 | 0.0098434 | 1.1908138 | 0.2337267 | -0.0075710 | 0.0310143 |
| 2 | ED_Pain | 0.0990731 | 0.0319298 | 3.1028440 | 0.0019167 | 0.0364919 | 0.1616543 |
| 2 | RaceNon-Hispanic Black | -0.5567944 | 0.2671136 | -2.0844852 | 0.0371161 | -1.0803275 | -0.0332613 |
| 2 | RaceNon-Hispanic Other | -0.2695170 | 0.3910681 | -0.6891817 | 0.4907089 | -1.0359964 | 0.4969624 |
| 2 | RaceNon-Hispanic White | -0.3525913 | 0.2768899 | -1.2733990 | 0.2028765 | -0.8952854 | 0.1901029 |
| 2 | Plate.Number | -0.0221147 | 0.0119525 | -1.8502121 | 0.0642830 | -0.0455412 | 0.0013118 |
| 2 | e2_binlow E2 Q1 | 0.8999094 | 0.4502753 | 1.9985760 | 0.0456542 | 0.0173861 | 1.7824327 |
| 2 | e2_binmid E2 Q2-Q4 | 0.5857043 | 0.3309789 | 1.7696120 | 0.0767918 | -0.0630025 | 1.2344110 |
| 3 | (Intercept) | -2.8005863 | 1.5224774 | -1.8394928 | 0.0658427 | -5.7845872 | 0.1834146 |
| 3 | time | -0.2119677 | 0.0816147 | -2.5971748 | 0.0093994 | -0.3719296 | -0.0520058 |
| 3 | sqrt.conc | 0.0245124 | 0.0913903 | 0.2682167 | 0.7885325 | -0.1546092 | 0.2036340 |
| 3 | ED_Age | 0.0552166 | 0.0119522 | 4.6197746 | 0.0000038 | 0.0317907 | 0.0786425 |
| 3 | ED_HighestGradeAdvanced Degree | -1.6332431 | 1.0815625 | -1.5100774 | 0.1310237 | -3.7530667 | 0.4865804 |
| 3 | ED_HighestGradeHigh School or Less | -0.0021270 | 0.3043487 | -0.0069887 | 0.9944238 | -0.5986395 | 0.5943855 |
| 3 | ED_HighestGradeBachelor’s Degree | -0.7036320 | 0.5114034 | -1.3758846 | 0.1688574 | -1.7059642 | 0.2987002 |
| 3 | BMI | -0.0224145 | 0.0195472 | -1.1466910 | 0.2515094 | -0.0607263 | 0.0158972 |
| 3 | ED_Pain | 0.2941742 | 0.0642418 | 4.5791721 | 0.0000047 | 0.1682626 | 0.4200858 |
| 3 | RaceNon-Hispanic Black | -0.5559079 | 0.4183799 | -1.3287157 | 0.1839418 | -1.3759174 | 0.2641016 |
| 3 | RaceNon-Hispanic Other | -0.5137372 | 0.6962793 | -0.7378320 | 0.4606165 | -1.8784196 | 0.8509453 |
| 3 | RaceNon-Hispanic White | -0.7766032 | 0.4761271 | -1.6310840 | 0.1028726 | -1.7097951 | 0.1565887 |
| 3 | Plate.Number | -0.0439037 | 0.0221902 | -1.9785238 | 0.0478696 | -0.0873956 | -0.0004118 |
| 3 | e2_binlow E2 Q1 | 0.8055860 | 0.8141799 | 0.9894447 | 0.3224456 | -0.7901773 | 2.4013493 |
| 3 | e2_binmid E2 Q2-Q4 | -0.1328456 | 0.6102937 | -0.2176749 | 0.8276825 | -1.3289994 | 1.0633082 |
#e2_bin low E2 on category 3 (10 pain) p = 2.398452e-03 = 0.002398452
#this means that having a LOW E2 category is significantly associated with having level 10 PAIN rather than Level 0 Pain
# Fit the multinomial logistic regression model for cases where e2_full_long$time == 2 months
model_m2 <- multinom(bin_pain4 ~ sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin,
subset = time == 2, control = list(singular.ok = FALSE), data = e2_full_long)## # weights: 45 (28 variable)
## initial value 421.867119
## iter 10 value 247.599606
## iter 20 value 221.928655
## iter 30 value 221.001253
## iter 40 value 220.973076
## final value 220.972555
## convergedtidy(model_m2, conf.int = TRUE) %>%
kable() %>% kable_styling("basic", full_width = FALSE) #low e2 significant vs level 10 pain | y.level | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| 2 | (Intercept) | 0.1311589 | 1.5836329 | 8.282160e-02 | 0.9339934 | -2.9727044 | 3.2350223 |
| 2 | sqrt.conc | -0.0163413 | 0.0943060 | -1.732790e-01 | 0.8624321 | -0.2011776 | 0.1684951 |
| 2 | ED_Age | 0.0340383 | 0.0143767 | 2.367602e+00 | 0.0179038 | 0.0058605 | 0.0622162 |
| 2 | ED_HighestGradeAdvanced Degree | 0.1388173 | 0.7293180 | 1.903385e-01 | 0.8490439 | -1.2906196 | 1.5682542 |
| 2 | ED_HighestGradeHigh School or Less | -0.4155563 | 0.3747485 | -1.108894e+00 | 0.2674759 | -1.1500499 | 0.3189372 |
| 2 | ED_HighestGradeBachelor’s Degree | 0.1282390 | 0.5034145 | 2.547383e-01 | 0.7989252 | -0.8584354 | 1.1149134 |
| 2 | BMI | -0.0002916 | 0.0199227 | -1.463560e-02 | 0.9883229 | -0.0393394 | 0.0387563 |
| 2 | ED_Pain | 0.2308309 | 0.0648503 | 3.559444e+00 | 0.0003716 | 0.1037267 | 0.3579350 |
| 2 | RaceNon-Hispanic Black | -0.9827641 | 0.6584997 | -1.492429e+00 | 0.1355867 | -2.2733998 | 0.3078716 |
| 2 | RaceNon-Hispanic Other | -0.8215715 | 0.8566076 | -9.590990e-01 | 0.3375089 | -2.5004916 | 0.8573485 |
| 2 | RaceNon-Hispanic White | -0.9507816 | 0.6677560 | -1.423846e+00 | 0.1544911 | -2.2595592 | 0.3579961 |
| 2 | Plate.Number | 0.0167237 | 0.0253585 | 6.594900e-01 | 0.5095812 | -0.0329781 | 0.0664255 |
| 2 | e2_binlow E2 Q1 | 0.2763326 | 0.9628684 | 2.869890e-01 | 0.7741208 | -1.6108547 | 2.1635199 |
| 2 | e2_binmid E2 Q2-Q4 | 0.0820186 | 0.7059307 | 1.161850e-01 | 0.9075059 | -1.3015802 | 1.4656173 |
| 3 | (Intercept) | -2.4242005 | 2.5482795 | -9.513087e-01 | 0.3414477 | -7.4187365 | 2.5703355 |
| 3 | sqrt.conc | -0.0022809 | 0.1469001 | -1.552690e-02 | 0.9876118 | -0.2901999 | 0.2856381 |
| 3 | ED_Age | 0.0524826 | 0.0212763 | 2.466720e+00 | 0.0136357 | 0.0107819 | 0.0941834 |
| 3 | ED_HighestGradeAdvanced Degree | -14.3731028 | 0.0000030 | -4.750017e+06 | 0.0000000 | -14.3731087 | -14.3730969 |
| 3 | ED_HighestGradeHigh School or Less | -0.5352624 | 0.5506261 | -9.720977e-01 | 0.3310019 | -1.6144697 | 0.5439450 |
| 3 | ED_HighestGradeBachelor’s Degree | -0.5856722 | 0.8206092 | -7.137041e-01 | 0.4754101 | -2.1940366 | 1.0226922 |
| 3 | BMI | -0.0444724 | 0.0346465 | -1.283603e+00 | 0.1992808 | -0.1123784 | 0.0234336 |
| 3 | ED_Pain | 0.4269331 | 0.1148537 | 3.717191e+00 | 0.0002015 | 0.2018240 | 0.6520422 |
| 3 | RaceNon-Hispanic Black | -1.1111457 | 0.8408800 | -1.321408e+00 | 0.1863653 | -2.7592403 | 0.5369489 |
| 3 | RaceNon-Hispanic Other | -1.6529745 | 1.3994927 | -1.181124e+00 | 0.2375534 | -4.3959297 | 1.0899807 |
| 3 | RaceNon-Hispanic White | -1.1583642 | 0.9015747 | -1.284823e+00 | 0.1988541 | -2.9254181 | 0.6086898 |
| 3 | Plate.Number | 0.0164285 | 0.0386844 | 4.246803e-01 | 0.6710698 | -0.0593916 | 0.0922486 |
| 3 | e2_binlow E2 Q1 | 0.0559009 | 1.4232599 | 3.927670e-02 | 0.9686698 | -2.7336372 | 2.8454391 |
| 3 | e2_binmid E2 Q2-Q4 | -0.3858054 | 1.0600603 | -3.639466e-01 | 0.7158979 | -2.4634854 | 1.6918747 |
#Fit the model for time point 3 months
model_m3 <- multinom(bin_pain4 ~ sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin,
subset = time == 3, control = list(singular.ok = FALSE), data = e2_full_long)## # weights: 45 (28 variable)
## initial value 419.669894
## iter 10 value 266.160078
## iter 20 value 245.906643
## iter 30 value 245.107442
## final value 245.104152
## convergedtidy(model_m3, conf.int = TRUE) %>%
kable() %>% kable_styling("basic", full_width = FALSE) #no significance| y.level | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| 2 | (Intercept) | -0.9701946 | 1.3159434 | -0.7372616 | 0.4609633 | -3.5493962 | 1.6090071 |
| 2 | sqrt.conc | 0.0535287 | 0.0807280 | 0.6630748 | 0.5072826 | -0.1046953 | 0.2117527 |
| 2 | ED_Age | 0.0269585 | 0.0117025 | 2.3036503 | 0.0212423 | 0.0040220 | 0.0498951 |
| 2 | ED_HighestGradeAdvanced Degree | 0.2718833 | 0.6243135 | 0.4354915 | 0.6632057 | -0.9517488 | 1.4955153 |
| 2 | ED_HighestGradeHigh School or Less | -0.0117981 | 0.3088062 | -0.0382054 | 0.9695239 | -0.6170471 | 0.5934510 |
| 2 | ED_HighestGradeBachelor’s Degree | 0.3832827 | 0.4352584 | 0.8805866 | 0.3785416 | -0.4698080 | 1.2363734 |
| 2 | BMI | 0.0212149 | 0.0175535 | 1.2085828 | 0.2268232 | -0.0131894 | 0.0556191 |
| 2 | ED_Pain | 0.0620877 | 0.0557494 | 1.1136930 | 0.2654109 | -0.0471791 | 0.1713544 |
| 2 | RaceNon-Hispanic Black | -0.8017810 | 0.4927088 | -1.6272919 | 0.1036751 | -1.7674724 | 0.1639105 |
| 2 | RaceNon-Hispanic Other | -0.1361759 | 0.7287832 | -0.1868538 | 0.8517753 | -1.5645648 | 1.2922130 |
| 2 | RaceNon-Hispanic White | -0.6849750 | 0.5097263 | -1.3438096 | 0.1790100 | -1.6840202 | 0.3140701 |
| 2 | Plate.Number | -0.0090328 | 0.0207976 | -0.4343192 | 0.6640567 | -0.0497954 | 0.0317298 |
| 2 | e2_binlow E2 Q1 | 1.1126520 | 0.7832563 | 1.4205465 | 0.1554486 | -0.4225021 | 2.6478060 |
| 2 | e2_binmid E2 Q2-Q4 | 0.8586772 | 0.5744442 | 1.4947965 | 0.1349676 | -0.2672128 | 1.9845671 |
| 3 | (Intercept) | -4.5286568 | 2.7704432 | -1.6346326 | 0.1021261 | -9.9586257 | 0.9013121 |
| 3 | sqrt.conc | 0.0530163 | 0.1690602 | 0.3135939 | 0.7538295 | -0.2783357 | 0.3843682 |
| 3 | ED_Age | 0.0514629 | 0.0220781 | 2.3309514 | 0.0197559 | 0.0081907 | 0.0947351 |
| 3 | ED_HighestGradeAdvanced Degree | 0.0570339 | 1.2663068 | 0.0450396 | 0.9640758 | -2.4248819 | 2.5389497 |
| 3 | ED_HighestGradeHigh School or Less | 0.4375417 | 0.5663424 | 0.7725744 | 0.4397743 | -0.6724691 | 1.5475525 |
| 3 | ED_HighestGradeBachelor’s Degree | -0.1149097 | 0.9232596 | -0.1244609 | 0.9009503 | -1.9244652 | 1.6946458 |
| 3 | BMI | -0.0021299 | 0.0339560 | -0.0627244 | 0.9499860 | -0.0686823 | 0.0644226 |
| 3 | ED_Pain | 0.3462676 | 0.1243026 | 2.7856822 | 0.0053415 | 0.1026389 | 0.5898962 |
| 3 | RaceNon-Hispanic Black | -1.0872894 | 0.7433741 | -1.4626408 | 0.1435657 | -2.5442759 | 0.3696972 |
| 3 | RaceNon-Hispanic Other | -0.7309113 | 1.3363611 | -0.5469415 | 0.5844189 | -3.3501309 | 1.8883082 |
| 3 | RaceNon-Hispanic White | -0.8465528 | 0.8185463 | -1.0342150 | 0.3010357 | -2.4508740 | 0.7577684 |
| 3 | Plate.Number | -0.0820752 | 0.0446913 | -1.8364919 | 0.0662849 | -0.1696686 | 0.0055181 |
| 3 | e2_binlow E2 Q1 | 1.0377023 | 1.4619024 | 0.7098301 | 0.4778095 | -1.8275737 | 3.9029783 |
| 3 | e2_binmid E2 Q2-Q4 | -0.1145481 | 1.0823714 | -0.1058307 | 0.9157167 | -2.2359572 | 2.0068609 |
#Fit the model for time point 6 months
model_m6 <- multinom(bin_pain4 ~ sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin,
subset = time == 6, control = list(singular.ok = FALSE), data = e2_full_long)## # weights: 45 (28 variable)
## initial value 436.149079
## iter 10 value 306.681378
## iter 20 value 282.827630
## iter 30 value 281.153243
## iter 40 value 281.105845
## final value 281.104843
## convergedtidy(model_m6, conf.int = TRUE) %>%
kable() %>% kable_styling("basic", full_width = FALSE) #plate number is significant????, no e2 significance| y.level | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| 2 | (Intercept) | -0.6797556 | 1.1453026 | -5.935161e-01 | 0.5528358 | -2.9245075 | 1.5649963 |
| 2 | sqrt.conc | 0.0580868 | 0.0713283 | 8.143595e-01 | 0.4154390 | -0.0817140 | 0.1978876 |
| 2 | ED_Age | 0.0199008 | 0.0101594 | 1.958861e+00 | 0.0501290 | -0.0000112 | 0.0398128 |
| 2 | ED_HighestGradeAdvanced Degree | -0.0242110 | 0.5138717 | -4.711480e-02 | 0.9624217 | -1.0313811 | 0.9829592 |
| 2 | ED_HighestGradeHigh School or Less | 0.0685121 | 0.2803377 | 2.443913e-01 | 0.8069278 | -0.4809397 | 0.6179639 |
| 2 | ED_HighestGradeBachelor’s Degree | 0.1315121 | 0.3593481 | 3.659740e-01 | 0.7143845 | -0.5727973 | 0.8358214 |
| 2 | BMI | 0.0115781 | 0.0151579 | 7.638301e-01 | 0.4449685 | -0.0181309 | 0.0412870 |
| 2 | ED_Pain | 0.0503091 | 0.0500570 | 1.005036e+00 | 0.3148794 | -0.0478008 | 0.1484190 |
| 2 | RaceNon-Hispanic Black | -0.2536683 | 0.3806172 | -6.664657e-01 | 0.5051135 | -0.9996643 | 0.4923277 |
| 2 | RaceNon-Hispanic Other | -0.1679640 | 0.5848960 | -2.871690e-01 | 0.7739830 | -1.3143391 | 0.9784111 |
| 2 | RaceNon-Hispanic White | 0.1132893 | 0.3982906 | 2.844389e-01 | 0.7760741 | -0.6673458 | 0.8939245 |
| 2 | Plate.Number | -0.0546909 | 0.0185820 | -2.943225e+00 | 0.0032481 | -0.0911109 | -0.0182709 |
| 2 | e2_binlow E2 Q1 | 1.1190994 | 0.6923761 | 1.616317e+00 | 0.1060258 | -0.2379329 | 2.4761317 |
| 2 | e2_binmid E2 Q2-Q4 | 0.6650044 | 0.5114145 | 1.300324e+00 | 0.1934900 | -0.3373496 | 1.6673584 |
| 3 | (Intercept) | -4.2024389 | 2.8039702 | -1.498746e+00 | 0.1339396 | -9.6981194 | 1.2932416 |
| 3 | sqrt.conc | -0.0018952 | 0.1885286 | -1.005250e-02 | 0.9919794 | -0.3714044 | 0.3676140 |
| 3 | ED_Age | 0.0739300 | 0.0215254 | 3.434539e+00 | 0.0005936 | 0.0317409 | 0.1161191 |
| 3 | ED_HighestGradeAdvanced Degree | -13.6569224 | 0.0000054 | -2.509571e+06 | 0.0000000 | -13.6569331 | -13.6569118 |
| 3 | ED_HighestGradeHigh School or Less | 0.0792444 | 0.5192663 | 1.526085e-01 | 0.8787070 | -0.9384988 | 1.0969877 |
| 3 | ED_HighestGradeBachelor’s Degree | -1.5352518 | 1.1238222 | -1.366099e+00 | 0.1719080 | -3.7379027 | 0.6673992 |
| 3 | BMI | -0.0253108 | 0.0350263 | -7.226232e-01 | 0.4699114 | -0.0939611 | 0.0433395 |
| 3 | ED_Pain | 0.1899685 | 0.1041265 | 1.824400e+00 | 0.0680916 | -0.0141158 | 0.3940528 |
| 3 | RaceNon-Hispanic Black | 0.1784226 | 0.7516905 | 2.373618e-01 | 0.8123761 | -1.2948637 | 1.6517090 |
| 3 | RaceNon-Hispanic Other | 0.5406662 | 1.0958821 | 4.933616e-01 | 0.6217571 | -1.6072233 | 2.6885557 |
| 3 | RaceNon-Hispanic White | -0.9287545 | 0.9489130 | -9.787562e-01 | 0.3277005 | -2.7885899 | 0.9310809 |
| 3 | Plate.Number | -0.0642080 | 0.0381791 | -1.681761e+00 | 0.0926153 | -0.1390376 | 0.0106215 |
| 3 | e2_binlow E2 Q1 | 1.1705709 | 1.5664553 | 7.472737e-01 | 0.4548984 | -1.8996252 | 4.2407669 |
| 3 | e2_binmid E2 Q2-Q4 | -0.0904802 | 1.1792451 | -7.672720e-02 | 0.9388406 | -2.4017582 | 2.2207978 |
#m <- glmer(bin_pain4 ~ time + sqrt.conc + ED_Age + ED_HighestGrade + BMI + ED_Pain + Race + Plate.Number + e2_bin + (1 | PID), data = e2_full_long, family = binomial, control = glmerControl(optimizer = "bobyqa"), nAGQ = 10)miRNA original code for miRNA
#E2_women_wide_all<- E2_women_wide %>%
# inner_join(ID_miRNA,by="PID")
#E2_women_long_all<- E2_women_long %>%
# inner_join(ID_miRNA,by="PID") NEW MARCH 2024 CODE FOR miRNA
none of the PIDs match up between the new estrogen data and the miRNA data
#unique(e2_full_long$PID)
#ID_miRNA$PID
#typeof(e2_full_long$PID)
#typeof(ID_miRNA$PID)
e2_wide_all<- e2_full_wide %>%
inner_join(ID_miRNA,by="PID")
E2_long_all<- e2_full_long %>%
inner_join(ID_miRNA,by="PID")