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1. JP_Data

-Note: Large chunks of code have been hidden to improve readibility. -NOTE: Did some reading, this data is a particular type of MLM: “Cross-classified”. This means our level 1 units (i.e., RT observations) are clustered within two grouping variables (1 = Subject, 2 = Condition). Need to do a little more research on this. ## 1. Load Packages Used in Data Analysis -Code hidden

2. Data Read-In

-Code hidden

3. Data Pre-processing

Creating Trial Number Column

length(df$Session[df$Subject == 56001 & df$Session == 1]) #160 rows ("trials") per part.

## [1] 160

dim(df) #21,440 total rows ("trials")

## [1] 21440     8

21440/160 #160 rows repeated 134 times

## [1] 134

df$Trial <- rep(seq(1:160), 134) #Code creates a sequence of 1-160, per participant per session, and stores this in the "Trial" variable

Probe accuracy, incorrect trials, improbable reaction times

-The code below when run illustrates that there are no administrations #With <50% accuracy. Nothing excluded here.

df %>% 
  group_by(Subject, Session, Cue) %>% 
  summarise(Percent_Accurate = mean(Accuracy)) %>% 
  filter(Percent_Accurate < 0.50)

## `summarise()` has grouped output by 'Subject', 'Session'. You can override
## using the `.groups` argument.

## # A tibble: 0 × 4
## # Groups:   Subject, Session [0]
## # … with 4 variables: Subject <dbl>, Session <fct>, Cue <fct>,
## #   Percent_Accurate <dbl>

-The code below indicates the overall # of trials and the counts/percents for 1) incorrect trials to be excluded, and 2) improbable reaction time trials (i.e., <200ms OR >1500ms) to be excluded. The code immediately following then excludes this data.

df %>% 
  group_by(Session) %>% 
  summarise(Overall_Trials = n(), #Overall trial counts
            Count_Inc_Trial = sum(ifelse(Accuracy == 0,1,0)), #Incorrect trial count
            Per_Inc_Trial = 1 - mean(Accuracy), #Percent of trials that are incorrect
            Count_Inc_RT = sum(ifelse(RT < 200 | RT > 1500, 1, 0)), 
            #Count of trials whose reaction times are < 200 or > 1500
            Per_Inc_RT = 1 - mean(ifelse(RT < 200 | RT > 1500, 0, 1)))

## # A tibble: 6 × 6
##   Session Overall_Trials Count_Inc_Trial Per_Inc_Trial Count_Inc_RT Per_Inc_RT
##   <fct>            <int>           <dbl>         <dbl>        <dbl>      <dbl>
## 1 1                 4160              84        0.0202           79     0.0190
## 2 2                 4160             161        0.0387           80     0.0192
## 3 3                 3680              60        0.0163           70     0.0190
## 4 4                 3680              82        0.0223           62     0.0168
## 5 5                 2880              71        0.0247           69     0.0240
## 6 6                 2880              74        0.0257           90     0.0312

-Filtering out improbable reaction times

df <- df %>% 
  filter(Accuracy == 1) %>%  #Exclude incorrect trials
  filter(RT > 200 & RT < 1500) #Exclude RT's <200ms or >1500ms

-Maximum & Minimum RT after exclusions

max(df$RT)

## [1] 1499

min(df$RT)

## [1] 207

-Code for Median Absolute Deviation and SD calculations for Reaction Times hidden

-MAD & SD Calculation Results

df %>% 
  group_by(Session) %>% 
  summarise(Ovr_Trl_Aftr_Exc = n(), #Overall trial counts after excluding for above-noted criteria
            Count_MAD3_Over = sum(ifelse(RT > MAD_Exclude_Score, 1,0)), #Count of trials that had RT > 3MAD above individual median
            Per_MAD3_Over = mean(ifelse(RT > MAD_Exclude_Score, 1, 0)),
            Count_SD3_Over = sum(ifelse(RT > SD_Exclude_Score, 1,0)), #Count of trials that had RT > 3MAD above individual median
            Per_SD3_Over = mean(ifelse(RT > SD_Exclude_Score, 1, 0)))

## # A tibble: 6 × 6
##   Session Ovr_Trl_Aftr_Exc Count_MAD3_Over Per_MAD3_Over Count_SD3_Over Per_SD…¹
##   <fct>              <int>           <dbl>         <dbl>          <dbl>    <dbl>
## 1 1                   4005             205        0.0512             79   0.0197
## 2 2                   3931             138        0.0351             54   0.0137
## 3 3                   3551             115        0.0324             47   0.0132
## 4 4                   3538             119        0.0336             50   0.0141
## 5 5                   2746             114        0.0415             54   0.0197
## 6 6                   2725             128        0.0470             52   0.0191
## # … with abbreviated variable name ¹Per_SD3_Over

-Excluding Folks based on SD for Reaction Times

df <- df %>% 
  filter(RT < SD_Exclude_Score) #Exclude folks whose RT are >3SD above their individual means

3. Descriptive Statistics

df %>% select(RT, Condition, Session, Cue) %>%  group_by(Condition, Session, Cue) %>% 
  summarise(across(everything(), list(mean = mean, sd = sd, min = min, max = max)))

## `summarise()` has grouped output by 'Condition', 'Session'. You can override
## using the `.groups` argument.

## # A tibble: 36 × 7
## # Groups:   Condition, Session [18]
##    Condition Session Cue    RT_mean RT_sd RT_min RT_max
##    <chr>     <fct>   <fct>    <dbl> <dbl>  <dbl>  <dbl>
##  1 A         1       opioid    623.  210.    339   1447
##  2 A         1       pain      635.  213.    317   1440
##  3 A         2       opioid    580.  144.    207   1295
##  4 A         2       pain      576.  151.    367   1356
##  5 A         3       opioid    557.  125.    207   1167
##  6 A         3       pain      562.  133.    312   1316
##  7 A         4       opioid    551.  140.    342   1348
##  8 A         4       pain      552.  144.    328   1241
##  9 A         5       opioid    536.  141.    301   1217
## 10 A         5       pain      534.  137.    322   1219
## # … with 26 more rows

4. Attentional Bias (AB) Calculations

-Code for AB calculations hidden

-Attentional Bias Metrics Per Subject, Condition, Session, & Cue

By_Subject_Output

## # A tibble: 268 × 10
## # Groups:   Subject, Condition, Session [134]
##    Subject Condi…¹ Session Cue   Mean_…² Mean_…³ Mean_…⁴ Peak_…⁵ Peak_…⁶ Varia…⁷
##      <dbl> <chr>   <fct>   <fct>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1   56001 C       1       opio…   6.24     97.9   103.      442     203    82.1
##  2   56001 C       1       pain   33.2     139.     61.2     429     226    76.5
##  3   56001 C       2       opio…  12.3     114.    110.      402     296    87.9
##  4   56001 C       2       pain    0.776   100.    107.      380     287    76.7
##  5   56001 A       3       opio…  22.9     115.    113.      350     528   101. 
##  6   56001 A       3       pain    4.97     65.3    89.6     228     228    74.2
##  7   56001 A       4       opio… -39.8      82.1   106.      240     347    73.7
##  8   56001 A       4       pain    1.86     75.7   101.      326     486    80.2
##  9   56001 B       5       opio…  -0.504    88.8    65.7     251     178    64.5
## 10   56001 B       5       pain    9.94    110.     52.1     305     289    79.9
## # … with 258 more rows, and abbreviated variable names ¹Condition, ²Mean_Bias,
## #   ³Mean_Toward, ⁴Mean_Away, ⁵Peak_Toward, ⁶Peak_Away, ⁷Variability

By_Condition_Output

## # A tibble: 36 × 9
## # Groups:   Condition, Session [18]
##    Condition Session Cue    Mean_Bias Mean_Tow…¹ Mean_…² Peak_…³ Peak_…⁴ Varia…⁵
##    <chr>     <fct>   <fct>      <dbl>      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 A         1       opioid     -4.88      112.    117.     323.    317.    92.8
##  2 A         1       pain        1.90      117.    125.     400.    353.    98.4
##  3 B         1       opioid     -3.52       95.1    90.3    318.    313.    89.7
##  4 B         1       pain        6.39       84.6    90.5    292.    290.    81.8
##  5 C         1       opioid     -9.58      119.    128.     364.    367.   104. 
##  6 C         1       pain        5.10      130.    118.     396.    386.   107. 
##  7 A         2       opioid     20.0       102.     96.6    317.    329.    89.9
##  8 A         2       pain      -10.2        93.9   105.     354.    341.    89.9
##  9 B         2       opioid      3.00      106.    109.     406.    331.    86.2
## 10 B         2       pain       28.8       117.     96.1    364.    299.    94.3
## # … with 26 more rows, and abbreviated variable names ¹Mean_Toward, ²Mean_Away,
## #   ³Peak_Toward, ⁴Peak_Away, ⁵Variability

By_Session_Output

## # A tibble: 12 × 8
## # Groups:   Session [6]
##    Session Cue    Mean_Bias Mean_Toward Mean_Away Peak_Toward Peak_Away Variab…¹
##    <fct>   <fct>      <dbl>       <dbl>     <dbl>       <dbl>     <dbl>    <dbl>
##  1 1       opioid    -6.27         109.      113.        337.      335.     96.2
##  2 1       pain       4.50         113.      112.        366.      347.     96.9
##  3 2       opioid     1.82         108.      110.        357.      341.     93.2
##  4 2       pain       6.25         110.      112.        359.      342.     96.2
##  5 3       opioid    -0.233        111.      108.        373.      364.     94.1
##  6 3       pain      -4.52         105.      111.        338.      353.     94.5
##  7 4       opioid     4.76         111.      110.        377.      352.     93.3
##  8 4       pain       2.01         110.      106.        343.      362.     96.2
##  9 5       opioid    -3.19         106.      105.        324.      348.     92.1
## 10 5       pain     -13.3          103.      107.        326.      342.     90.4
## 11 6       opioid    -3.74         105.      110.        335.      329.     96.1
## 12 6       pain     -12.3          106.      116.        313.      348.     91.9
## # … with abbreviated variable name ¹Variability

By_Cue_Output

## # A tibble: 2 × 7
##   Cue    Mean_Bias Mean_Toward Mean_Away Peak_Toward Peak_Away Variability
##   <fct>      <dbl>       <dbl>     <dbl>       <dbl>     <dbl>       <dbl>
## 1 opioid     -1.01        108.      110.        352.      345.        94.2
## 2 pain       -1.76        108.      111.        343.      349.        94.7

5. ANOVA’s

-Code Hidden -Changing session variable to represent just pre-post

-Something to note: There are several subjects who appear to have no data for at least Condition/1 time-point:

Folks Missing 1 pre/post data point: #Condition A: 15 #Condition B: 02 #Condition C: 14

Folks Missing both pre/post data points #Condition A: 03, 11, 13, 14, 18, 22, 28 #Condition B: 03, 13, 17 #Condition C: 17,18,23

R seems to be dropping these folks from ANOVA analysis below. I’m trying to specify a way for them to be kept and not succeeding at the moment, but even if I can, that still is going to result with at least 13/28 getting dropped. A REALLY small sample of N=15 at best or N=12 at worst.

-Changing Data into Long Format

df_long <- df %>% select(Subject, Condition, Session, Cue, mean_bias:variability) %>% 
  pivot_longer(cols = c(mean_bias:variability), names_to = "Bias_Type", values_to = "RT") %>% 
  mutate(Subject = factor(Subject)) %>% 
  distinct()

dim(df_long) #Dataframe dimensions

## [1] 1608    6

# Number of RT's each participant contributes to the dataframe
table(df_long$Subject)

## 
## 56001 56002 56003 56004 56005 56006 56007 56008 56009 56010 56011 56012 56013 
##    72    60    36    72    72    72    72    72    72    72    48    72    24 
## 56014 56015 56016 56017 56018 56019 56020 56021 56022 56023 56025 56026 56027 
##    36    60    72    24    24    72    72    72    48    48    72    72    72 
## 56028 
##    48

data.frame(table(df_long$Subject)) %>% summarise(Min = min(Freq), Max = max(Freq))

##   Min Max
## 1  24  72

6. ANOVAs

6a. Mean Bias

-DV: Opioid Mean Bias

-DV: Pain Mean Bias

6b. Mean Away Bias

-DV: Opioid Mean Away Bias

-DV: Pain Mean Away Bias

6c. Mean Toward Bias

-DV: Opioid Mean Toward Bias

-DV: Pain Mean Toward Bias

6d. Peak Away Bias

-DV: Opioid Peak Away Bias

-DV: Pain Peak Away Bias

6e. Peak Toward Bias

-DV: Opioid Peak Toward Bias

-DV: Pain Peak Toward Bias

6f. Variability Bias

-DV: Opioid Variability Bias

-DV: Pain Variability Bias

7. Muiltilevel Models

7a. Mean Bias