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BreakthroughFactorsAnalysis.R
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# Last updated: 09-20-2021
# Author: Cong Liu
# checked version: Yes
# source("./cohortCharacterizationAndRefine.R")
# get matched samples based on nearest neighbors.
breakthroughCovidCov = breakthroughCovidRefined %>%
left_join(breakthroughCovidFeatures$visit,by = "person_id",copy = TRUE) %>%
left_join(breakthroughCovidFeatures$obDays,by = "person_id",copy = TRUE) %>%
dplyr::select(person_id,latest_dose_date,earliest_dose_date,observation_days,count_of_visits,index_date) %>%
mutate(sample = "breakthroughCovid") %>%
replace_na(list(observation_days = 0, count_of_visits = 0))
nonBreakthroughPcrCovidCov = nonBreakthroughPcrCovidRefined %>%
left_join(nonBreakthroughPcrCovidFeatures$visit,by = "person_id",copy = TRUE) %>%
left_join(nonBreakthroughPcrCovidFeatures$obDays,by = "person_id",copy = TRUE) %>%
dplyr::select(person_id,latest_dose_date,earliest_dose_date,observation_days,count_of_visits,index_date) %>%
replace_na(list(observation_days = 0, count_of_visits = 0)) %>%
mutate(sample = "nonBreakthroughPcrCovid")
immuno = rbind(breakthroughCovidFeatures$immuno,nonBreakthroughPcrCovidFeatures$immuno)
forMatchData = rbind(breakthroughCovidCov,nonBreakthroughPcrCovidCov) %>%
mutate(group = as.logical(sample == 'breakthroughCovid')) %>%
mutate(ldd_category= cut(x = latest_dose_date, "months")) %>%
mutate(status = as.numeric(group)) %>%
mutate(time = if_else(condition = (status == 1),
true = (as.integer(difftime(units = "days",index_date,latest_dose_date) - 14)) + 1,
false = (as.integer(difftime(units = "days",index_date,latest_dose_date) - 14)) + 1
)
)
# overall incidence rate
overallIR = 1000*epi.conf(matrix(apply(forMatchData %>% dplyr::select(status,time),2,sum),nrow = 1),ctype = 'inc.rate',method = 'exact',N = 1000)
overallIR
# set.seed(5)
# # take a minute
# matchIt = matchit(group ~ ldd_category + observation_days + count_of_visits, data = forMatchData, method="nearest", ratio=10)
# plot(summary(matchIt))
# matchItData = match.data(matchIt)[1:ncol(forMatchData)]
# totalBtCount = table(matchItData$group)
# matchItData = matchItData %>%
# mutate(status = as.numeric(group)) %>%
# mutate(time = as.integer(difftime(units = "days",index_date,latest_dose_date) - 14))
#
# # prepare outcome
# # breakthroughCovid %>% mutate(time_to_event = difftime(units = "day",evidence_date,latest_dose_date)) %>%
# # dplyr::select(person_id,time_to_event)
# # add var of interest for cox regression analysis.
# # control -- check visit
# visitForChisqTest = matchItData %>%
# mutate(visit_category= cut_number(x = count_of_visits, n = 5))
# oddsRatioTest(table(visitForChisqTest$group, visitForChisqTest$visit_category))
#
# # control -- check observation_days
# obDaysForChisqTest = matchItData %>%
# mutate(obDays_category= cut_number(x = observation_days, n = 5))
# oddsRatioTest(table(obDaysForChisqTest$group, obDaysForChisqTest$obDays_category))
#
# # control -- check observation_days
# lddForChisqTest = matchItData %>%
# mutate(ldd_category= cut(x = latest_dose_date, "months"))
# oddsRatioTest(table(lddForChisqTest$group, lddForChisqTest$ldd_category))
res=NULL
# brand
brand = rbind(breakthroughCovidFeatures$brand,nonBreakthroughPcrCovidFeatures$brand)
# brandForTest = matchItData %>% left_join(brand,by="person_id",copy = TRUE)
# oddsRatioTest(table(brandForChisqTest$group, brandForChisqTest$vaccine_brand))
# brandForPoissonTest = matchItData %>% left_join(brand,by="person_id",copy = TRUE)
# brandForCoxTest = matchItData %>%
# left_join(brand,by="person_id",copy = TRUE)
brandForTest = forMatchData %>%
left_join(brand,by="person_id",copy = TRUE)
brandResults = univarTest(brandForTest,"vaccine_brand",
adj=c("ldd_category","count_of_visits","observation_days"))
for(r in c("moderna","pfizer")){
res = rbind(res,cbind(r,1000*epi.conf(matrix(apply(brandForTest %>% filter(vaccine_brand == r)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)))
}
# demo
demo = rbind(breakthroughCovidFeatures$demo,nonBreakthroughPcrCovidFeatures$demo)
# matchItData vs. forMatchData
demoForTest = forMatchData %>% left_join(demo,by = "person_id") %>%
mutate(age_at_event = as.integer(difftime(units = "days",latest_dose_date,DOB)/365.24)) %>%
mutate(age_category_at_event = cut(age_at_event, breaks=c(0, 65,Inf), include.lowest=TRUE) ) %>%
mutate(race_category = case_when((race == "White") ~ "White",
(race == "Black or African American") ~ "Black",
(race == "Asian") ~ "Asian",
TRUE ~ "Other/Unknown")) %>%
mutate(ethnicity_category = case_when((ethnicity=='Hispanic or Latino')~"Hispanic",
ethnicity=='Not Hispanic or Latino'~"Not Hispanic",
TRUE~"Other/Unknown")) %>% distinct()
covariates = c("age_category_at_event", "race_category", "ethnicity_category", "gender")
demoResults = do.call("rbind", sapply(covariates,
function(x) univarTest(demoForTest,x,adj=c("ldd_category","count_of_visits","observation_days"))))
for(r in c("[0,65]","(65,Inf]")){
res = rbind(res,cbind(r,
1000*epi.conf(matrix(apply(demoForTest %>% filter(age_category_at_event == r)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)
))
}
for(r in c("Asian","Black","White","Other/Unknown")){
res = rbind(res,cbind(r,
1000*epi.conf(matrix(apply(demoForTest %>% filter(race_category == r)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)
))
}
for(r in c("Hispanic","Not Hispanic","Other/Unknown")){
res = rbind(res,cbind(r,
1000*epi.conf(matrix(apply(demoForTest %>% filter(ethnicity_category == r)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)
))
}
for(r in c("FEMALE","MALE")){
res = rbind(res,cbind(r,
1000*epi.conf(matrix(apply(demoForTest %>% filter(gender == r)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)
))
}
# oddsRatioTest(table(demoForChisqTest$group, demoForChisqTest$gender))
# oddsRatioTest(table(demoForChisqTest$group, demoForChisqTest$race_category))
# oddsRatioTest(table(demoForChisqTest$group, demoForChisqTest$ethnicity_category))
# oddsRatioTest(table(demoForChisqTest$group, demoForChisqTest$age_category_at_event))
# immuno supressed test
immuno = rbind(breakthroughCovidFeatures$immuno,nonBreakthroughPcrCovidFeatures$immuno)
orderedLevelsCategory = c("no", "tumor", "ckd", "hiv",
"immuno_suppress_drug", "immuno_deficiency","transplant")
isImmunoLevelsCategory = c("Not Immuno Comprised", "Immuno Comprised")
immunoForTest = forMatchData %>% left_join(immuno,by = "person_id") %>%
dplyr::select(person_id,group,category,time,status,ldd_category,count_of_visits,observation_days) %>%
replace(., is.na(.), "no") %>%
mutate(isImmuo = case_when((category=='no')~'Not Immuno Comprised',TRUE~'Immuno Comprised')) %>%
mutate(isImmuo = factor(isImmuo,levels = isImmunoLevelsCategory)) %>%
mutate(category = factor(category,levels = orderedLevelsCategory)) %>%
distinct_all() %>%
dcast(person_id+group+time+status+ldd_category+count_of_visits+observation_days+isImmuo~category,fun = length)
covariates = c("isImmuo", "tumor","ckd","hiv","immuno_suppress_drug","immuno_deficiency","transplant")
for(r in c("Not Immuno Comprised","Immuno Comprised")){
res = rbind(res,cbind(r,
1000*epi.conf(matrix(apply(immunoForTest %>% filter(isImmuo == r)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)
))
}
for(r in covariates){
res = rbind(res,cbind(r,
1000*epi.conf(matrix(apply(immunoForTest %>% filter(!!as.symbol(r) == 1)
%>% dplyr::select(status,time),2,sum),nrow = 1),
ctype = 'inc.rate',method = 'exact',N = 1000)
))
}
immunoResults = lapply(covariates,
function(x) univarTest(immunoForTest,x,
adj=c("ldd_category","count_of_visits","observation_days"))
)
table2Col1 = res
demoRows = c(2,13,14,15,26,27,38,39)
brandRows = c(2)
table2Col2 = rbind(demoResults[demoRows,],(brandResults %>% as.data.frame())[brandRows,])
for(i in c(1:length(immunoResults))){
table2Col2 = rbind(table2Col2,(immunoResults[[i]] %>% as.data.frame())[2,])
}
table2Col2
# adj for covariates
immunoResultsAdj = lapply(covariates,
function(x) univarTest(immunoForTest %>% left_join(demoForTest),x,
adj=c("age_at_event","ldd_category","count_of_visits","observation_days")
))
brandResultsAdj = as.data.frame(univarTest(brandForTest %>% left_join(immunoForTest %>% dplyr::select(person_id,isImmuo) %>% distinct())
%>% left_join(demoForTest),"vaccine_brand",
adj=c("age_at_event","ldd_category","count_of_visits","observation_days","isImmuo")))
brandForTest %>% ggplot(aes(y=time,x=as.factor(status))) + geom_boxplot(aes(colour = vaccine_brand))
immunobrand = brandForTest %>% left_join(immunoForTest %>% dplyr::select(person_id,isImmuo) %>% distinct())
brandRows = c(2)
table2Col3 = rbind((brandResultsAdj %>% as.data.frame())[brandRows,])
for(i in c(1:length(immunoResultsAdj))){
table2Col3 = rbind(table2Col3,(immunoResultsAdj[[i]] %>% as.data.frame())[2,])
}
table2Col3
immunobrand %>% mutate(g = case_when(status == 1~"Vax Positive", T~"Vax Negative")) %>%
mutate(x = case_when(isImmuo=='Immuno Comprised'~'Immunocompromised',T~'Not Immunocompromised')) %>%
group_by(vaccine_brand,g,x) %>% summarise(N = n()) %>%
ggplot(aes(x, N, fill = vaccine_brand)) +
geom_bar(stat="identity", position = "dodge") + facet_wrap(~as.factor(g),nrow = 2,scales = "free_y")
# do.call("rbind", list(
# brandResults,
# brandResultsAdj,
# demoResults,
# immunoResults,
# immunoResultsAdj)) %>% write.csv("./breakthroughAnalysisRiskFactors.csv",sep = ",",row.names = T)
# forMultiTest = demoForTest %>% left_join(brandForCoxTest) %>% left_join(immunoForTest)
# multivarTest(forMultiTest,c("age_category_at_event", "race_category", "ethnicity_category", "gender","vaccine_brand","isImmuo", "category"))
# oddsRatioTest(table(immunoForChisqTest$group, immunoForChisqTest$isImmuo))
#
# for(i in immunoForChisqTest$category %>% unique()){
# immunoForChisqTestSub = immunoForChisqTest %>% filter(category %in% c('no',i))
# oddsRatioTest(table(immunoForChisqTestSub$group, immunoForChisqTestSub$category))
# }
# condition and drug
condition = rbind(breakthroughCovidFeatures$condition,nonBreakthroughPcrCovidFeatures$condition)
colnames(condition)[2] = "concept_id"
drug = rbind(breakthroughCovidFeatures$drug,nonBreakthroughPcrCovidFeatures$drug)
colnames(drug)[2] = "concept_id"
# concept = rbind(condition,drug)
conditionConceptRes = univariateTestForAllconcept(forMatchData,demoForTest = demoForTest,concept = condition)
drugConceptRes = univariateTestForAllconcept(forMatchData,demoForTest = demoForTest,concept = drug)
table3 = resultsDisplay(conceptRes = conditionConceptRes,con=con)
table3 %>% fwrite(file = './breakthroughAnalysisRiskFactorsAllCondition90daysPriorFullList.csv')
table4 = resultsDisplay(conceptRes = drugConceptRes,con=con)
table4 %>% fwrite(file = './breakthroughAnalysisRiskFactorsAllDrug90daysPriorFullList.csv')
# test.
# conceptRes = t(sapply(covariates[1:10],function(x) univarTest(matchItDataConceptWide,x)))
# matchItDataConcept = matchItDataConcept[,.(conceptNBtCount = .N - sum(group), conceptBtCount = sum(group)),by = concept_id]
# matchItDataConcept[,AllNBtCount := totalBtCount['FALSE']]
# matchItDataConcept[,AllBtCount := totalBtCount['TRUE']]
# # matchItDataCondition = matchItDataConcept[(conceptNBtCount + conceptBtCount) > 10]
# matchItDataOrResults = matchItDataConcept[,dataTableGroupOddsRatioTest(conceptNBtCount,conceptBtCount,AllNBtCount,AllBtCount),by = concept_id]
# all test stratified by age, gender and brand
# stratifyImmunoForChisqTest = immunoForChisqTest %>% left_join(demoForChisqTest) %>%
# left_join(brand) %>%left_join(immuno)
# for(i in unique(stratifyImmunoForChisqTest$vaccine_brand)){
# print(i)
# forChisqTest = stratifyImmunoForChisqTest %>% filter(vaccine_brand %in% i)
# oddsRatioTest(table(forChisqTest$group, forChisqTest$isImmuo))
# }
# for(i in unique(stratifyImmunoForChisqTest$age_category_at_event)){
# print(i)
# forChisqTest = stratifyImmunoForChisqTest %>% filter(age_category_at_event %in% i)
# oddsRatioTest(table(forChisqTest$group, forChisqTest$isImmuo))
# }
# for(i in unique(stratifyImmunoForChisqTest$gender)){
# print(i)
# forChisqTest = stratifyImmunoForChisqTest %>% filter(gender %in% i)
# oddsRatioTest(table(forChisqTest$group, forChisqTest$isImmuo))
# }