/SAM_FGT

Analyses of Fear Generalization Task in SAM study

Primary LanguageRMIT LicenseMIT

SAM_FGT

Data Imputation and Analyses of Fear Generalization Task in SAM study, as described in:

  • Sep, M.S.C., Gorter, R., van Ast, V.A., Joëls, M., & Geuze, E. (2019) No Time-Dependent Effects of Psychosocial Stress on Fear Contextualization and Generalization: A Randomized-Controlled Study With Healthy Participants. Chronic Stress, 3, 247054701989654. https://doi.org/10.1177/2470547019896547

Step 1: Data

Datasets (available on Dataverse):

  • SAM_FGT.csv contains fear-potentiated startle responses (FPS) during the FGT task
  • SAM_FGT_Amperage.csv contains shock intensities used in the conditioning phase of the FGT, per participant.
  • SAM_questionnaires.sav contains questionnaire information (including fear contingency scores)
  • SAM_Codes_Task_Protocol_Versions.csv contains information on the task versions, per participant

Step 2: Descriptive Statistics

FGT_descritpvies.R (in the 'R' folder) loads:

  • SAM_FGT.csv and SAM_FGT_Amperage.csv for the description of shock intensities and missing values.
  • SAM_questionnaires.sav and SAM_Codes_Task_Protocol_Versions.csv to prepare data for sensitivity analyses with fear contingency scores (saved as participants.for.contingency.sensitivity.analyses.rda in the folder processed_data)

Step 3: Multiple Imputation (MI) of FPS

The FPS_imputation.R script (in the 'R' folder) loads SAM_FGT.csv and prepares the data for multiple imputation (the cleaned data is saved as INPUT_IMPUTATIE_FGT_Umag.rda in the folder processed_data).

Perform MI via the function Impute_FGT_EMG_SAM to:

  1. to impute individual trials (set sorttype to 'trials').
    • Note: trials will be imputed if more than 1/3 of the trials in a category is present (in other words if <2/3 missing), if less than 1/3 is present (in other words if >2/3 is missing; missing code 4) all the trials (for that category) will be set to missing
  2. to create imputed means (set sorttype to 'mean').
    • Note: means will be based on imputed trials if more than 2/3 of trials is present (in other words if <1/3 missing; missing code 1), or imputed directly if less than 2/3 of the trials is present (or in other words, if >1/3 missing; missing code 2 ).

The imputed datasets are saved in the folder processed_data as:

  1. OUPUT_IMPUTATIE_FGT_out_M50_MAXIT100_Umag_AllMeans.rda
  2. OUPUT_IMPUTATIE_FGT_out_M50_MAXIT100_Umag_Trials.rda

Note, the function Impute_FGT_EMG_SAM also saves the output of the script automatically with a generic name -OUPUT_IMPUTATIE_FGT_out_endofscript.rda- in the folder processed_data.

Step 4: MI quality checks & data transformations

  • FPS_mids_Quality.R (in the 'R' folder) creates a sorted mids object -Umag.mids.28.01.19.rda- from the imputed data in the folder processed_data, that is used for the analyses.

Step 5: Assumptions linear mixed effect models (LMM):

The assumptions for LMM analyses are checked within each imputed dataset via FPS_LMM_Assumptions_call.R (in the 'R' folder). Note, this script loads Umag.mids.28.01.19.rda and renders the script FPS_LMM_Assumptions_source.R to a rmarkdown file for each outcome measure (files will appear in 'R' folder).

Step 6: LMM analyses:

The LMM analyses are performed within each imputed dataset via FPS_LMM_Analyses_call.R (in the 'R' folder). This script loads Umag.mids.28.01.19.rda and sources:

  1. scripts with analyses functions:

    • FPS_LMM_Trialtype.Trial.Condition.r
    • FPS_LMM_trialtype.Condition.r
    • FPS_LMM_trial.Condition.R
    • FPS_LMM_LM_Condition.r

  2. a script to pool (and plot) LMM estimates: FPS_LMM_pool_EMM.r

  3. a script to transform mids objects: FGT_mids_transformations.R

  4. a script to export results: FPS_LMM_Results_to_Word.R

Step 7: Data Visualization:

  • Figures: FPS_LMM_Results_to_Plot.R