Secondary analysis if single race/ethnicity in cohort

This part of the analysis is the same as before except we are not removing pregnancy complications from the dataset. Instead, we are including pregnancy complications and adjusting for them in the analysis. Please be sure to add these variables to your adjustmentvariables argument in the dataAnalysis function. We are also changing the analysisname to “secondary”.

Quick check to make sure the function runs in your cohort

Given the modeling approaches used, the dataAnalysis function requires a good deal of time to run. We recommend first checking whether the function runs on a relatively small subset of sites (i.e. 100 CpG loci). If you encounter any issues, please let us know. If not, proceed to the next step.

## if running in parallel, checking the number of available cores 
library(parallel)
detectCores() # should probably choose at least one less than the number available 

allvarsofinterest=c("BWT_Zscore","BirthLength_Zscore","HeadCircum_Zscore","wlr_Zscore")

## You can reduce this dataframe to whatever variables you have.
## For example, if you only have birthweight, you would specify:
## allvarsofinterest=c("BWT_Zscore")

for (i in 1:length(allvarsofinterest)){
  
  cat("Outcome:",allvarsofinterest[i],"\n")
  
  tempresults<-dataAnalysis(phenofinal=phenodataframe,
                  betafinal=Betasnooutliers[1:100,],
                  array="450K",
                  maxit=100,
                  robust=TRUE,
                  Omega=processedOut$Omega,
                  vartype="ExposureCont",
                  varofinterest=allvarsofinterest[i],
                  Table1vars=c("BWT","Gestage","Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","ComplicationsNew","Meanlog2oddsContamination"),
                  StratifyTable1=FALSE,
                  StratifyTable1var=NULL,
                  adjustmentvariables=c("Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","ComplicationsNew","Meanlog2oddsContamination"),
                  RunUnadjusted=TRUE,
                  RunAdjusted=TRUE,
                  RunCellTypeAdjusted=TRUE,
                  RunSexSpecific=TRUE,
                  RunCellTypeInteract=TRUE,
                  RestrictToSubset=FALSE,
                  RestrictionVar=NULL,
                  RestrictToIndicator=NULL,
                  number_cores=8,
                  runparallel=TRUE,
                  destinationfolder="H:\\UCLA\\PACE\\Birthweight-placenta",
                  savelog=TRUE,
                  cohort="HEBC",analysisdate="20220709",
                  analysisname="secondary")
  
  
}

Running the final models

Now running the models for all CpG loci

for (i in 1:length(allvarsofinterest)){
  
  cat("Outcome:",allvarsofinterest[i],"\n")
  
  tempresults<-dataAnalysis(phenofinal=phenodataframe,
                  betafinal=Betasnooutliers,
                  array="450K",
                  maxit=100,
                  robust=TRUE,
                  Omega=processedOut$Omega,
                  vartype="ExposureCont",
                  varofinterest=allvarsofinterest[i],
                  Table1vars=c("BWT","Gestage","Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","ComplicationsNew","Meanlog2oddsContamination"),
                  StratifyTable1=FALSE,
                  StratifyTable1var=NULL,
                  adjustmentvariables=c("Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","ComplicationsNew","Meanlog2oddsContamination"),
                  RunUnadjusted=TRUE,
                  RunAdjusted=TRUE,
                  RunCellTypeAdjusted=TRUE,
                  RunSexSpecific=TRUE,
                  RunCellTypeInteract=TRUE,
                  RestrictToSubset=FALSE,
                  RestrictionVar=NULL,
                  RestrictToIndicator=NULL,
                  number_cores=8,
                  runparallel=TRUE,
                  destinationfolder="H:\\UCLA\\PACE\\Birthweight-placenta",
                  savelog=TRUE,
                  cohort="HEBC",analysisdate="20220709",
                  analysisname="secondary")
  
  
}

Check model convergence

The function dataAnalysis includes an indicator of whether each site-specific model converged. If the models are not converging, you can increase the number of specified iterations for the robust regression models using the argument maxit in the dataAnalysis function. The current default number of iterations is 100; increasing this number will make the function slower.

baseoutputdirectory<-"H:/UCLA/PACE/Birthweight-placenta/HEBC_20220709_Output"

listchecking<-as.list(rep(NA,length(allvarsofinterest)))
names(listchecking)<-allvarsofinterest

for (i in 1:length(allvarsofinterest)){

  tempvarofinterest<-allvarsofinterest[i]

  cat("Outcome:",tempvarofinterest,"\n")
  tempdirectory<-paste(baseoutputdirectory,"/",tempvarofinterest,"_secondary",sep="")
  setwd(tempdirectory)
  tempfilename<-paste("HEBC_20220709_",tempvarofinterest,"_secondary_allanalyses.RData",sep="")
  load(tempfilename)
  if("CellInteraction" %in% names(alldataout)) alldataout$CellInteraction<-NULL
  if("warnings" %in% colnames(alldataout[[1]])) listchecking[[i]]<-lapply(alldataout,function(x) if(length(x)>1) table(x$warnings))

}

## Check them out
listchecking

Data upload

The PACEanalysis functions will create an “Output” folder in your specified destination directory, which will contain everything to be shared for the subsequent meta-analysis. To submit your results, zip this Output folder, and send it to Alexandra (ambinder@hawaii.edu).