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functions-process-results.R
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functions-process-results.R
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# Reference: Julie Dudášová, Zdeněk Valenta, Jeffrey R. Sachs (2024).
# "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression"
# This program is released under the GNU GPL3 license. Copyright © 2024 Merck & Co., Inc., Rahway, NJ, USA and its affiliates. All rights reserved.
### Supplementary functions
### Summarizing simulation results ----
analyzeSimulation <- function(SimulationData){
Results <- data.frame(seed = SimulationData$results$resultTrial$seed,
NDiseased.all = SimulationData$results$resultTrial$NDiseased.all,
NDiseased.groupA = SimulationData$results$resultTrial$NDiseased.groupA,
NDiseased.groupB = SimulationData$results$resultTrial$NDiseased.groupB,
NNondiseased.all = SimulationData$results$resultTrial$NNondiseased.all,
NNondiseased.groupA = SimulationData$results$resultTrial$NNondiseased.groupA,
NNondiseased.groupB = SimulationData$results$resultTrial$NNondiseased.groupB,
trueVE.all = SimulationData$results$resultTrial$trueEfficacy.all,
trueVE.groupA = SimulationData$results$resultTrial$trueEfficacy.groupA,
trueVE.groupB = SimulationData$results$resultTrial$trueEfficacy.groupB,
#
casecountVE.all = SimulationData$results$resultCaseCount$VE.all,
casecountCI.LB.all = SimulationData$results$resultCaseCount$VECI.all.lowerBound,
casecountCI.UB.all = SimulationData$results$resultCaseCount$VECI.all.upperBound,
casecountVE.groupA = SimulationData$results$resultCaseCount$VE.groupA,
casecountCI.LB.groupA = SimulationData$results$resultCaseCount$VECI.groupA.lowerBound,
casecountCI.UB.groupA = SimulationData$results$resultCaseCount$VECI.groupA.upperBound,
casecountVE.groupB = SimulationData$results$resultCaseCount$VE.groupB,
casecountCI.LB.groupB = SimulationData$results$resultCaseCount$VECI.groupB.lowerBound,
casecountCI.UB.groupB = SimulationData$results$resultCaseCount$VECI.groupB.upperBound,
#
logisticVE.all.add = SimulationData$results$resultLogistic$VE.all.add/100,
logisticCI.LB.all.add = SimulationData$results$resultLogistic$VECI.all.add.LB/100,
logisticCI.UB.all.add = SimulationData$results$resultLogistic$VECI.all.add.UB/100,
logisticVE.all.multi = SimulationData$results$resultLogistic$VE.all.multi/100,
logisticCI.LB.all.multi = SimulationData$results$resultLogistic$VECI.all.multi.LB/100,
logisticCI.UB.all.multi = SimulationData$results$resultLogistic$VECI.all.multi.UB/100,
logisticVE.groupA.add = SimulationData$results$resultLogistic$VE.groupA.add/100,
logisticCI.LB.groupA.add = SimulationData$results$resultLogistic$VECI.groupA.add.LB/100,
logisticCI.UB.groupA.add = SimulationData$results$resultLogistic$VECI.groupA.add.UB/100,
logisticVE.groupA.multi = SimulationData$results$resultLogistic$VE.groupA.multi/100,
logisticCI.LB.groupA.multi = SimulationData$results$resultLogistic$VECI.groupA.multi.LB/100,
logisticCI.UB.groupA.multi = SimulationData$results$resultLogistic$VECI.groupA.multi.UB/100,
logisticVE.groupB.add = SimulationData$results$resultLogistic$VE.groupB.add/100,
logisticCI.LB.groupB.add = SimulationData$results$resultLogistic$VECI.groupB.add.LB/100,
logisticCI.UB.groupB.add = SimulationData$results$resultLogistic$VECI.groupB.add.UB/100,
logisticVE.groupB.multi = SimulationData$results$resultLogistic$VE.groupB.multi/100,
logisticCI.LB.groupB.multi = SimulationData$results$resultLogistic$VECI.groupB.multi.LB/100,
logisticCI.UB.groupB.multi = SimulationData$results$resultLogistic$VECI.groupB.multi.UB/100,
logisticP.VS.add = SimulationData$results$resultLogistic$pval.vaccStatus.add,
logisticP.A.add = SimulationData$results$resultLogistic$pval.covariateBinary.add,
logisticP.VS.multi = SimulationData$results$resultLogistic$pval.vaccStatus.multi,
logisticP.A.multi = SimulationData$results$resultLogistic$pval.covariateBinary.multi,
logisticP.inter.multi = SimulationData$results$resultLogistic$pval.interaction.multi,
logisticAIC.add = SimulationData$results$resultLogistic$aic.aic.add,
logisticAIC.multi = SimulationData$results$resultLogistic$aic.aic.multi,
#
logisticCoPVE.all.add = SimulationData$results$resultLogisticCoP$VE.all.add/100,
logisticCoPCI.LB.all.add = SimulationData$results$resultLogisticCoP$VECI.all.add.LB/100,
logisticCoPCI.UB.all.add = SimulationData$results$resultLogisticCoP$VECI.all.add.UB/100,
logisticCoPVE.all.multi = SimulationData$results$resultLogisticCoP$VE.all.multi/100,
logisticCoPCI.LB.all.multi = SimulationData$results$resultLogisticCoP$VECI.all.multi.LB/100,
logisticCoPCI.UB.all.multi = SimulationData$results$resultLogisticCoP$VECI.all.multi.UB/100,
logisticCoPVE.groupA.add = SimulationData$results$resultLogisticCoP$VE.groupA.add/100,
logisticCoPCI.LB.groupA.add = SimulationData$results$resultLogisticCoP$VECI.groupA.add.LB/100,
logisticCoPCI.UB.groupA.add = SimulationData$results$resultLogisticCoP$VECI.groupA.add.UB/100,
logisticCoPVE.groupA.multi = SimulationData$results$resultLogisticCoP$VE.groupA.multi/100,
logisticCoPCI.LB.groupA.multi = SimulationData$results$resultLogisticCoP$VECI.groupA.multi.LB/100,
logisticCoPCI.UB.groupA.multi = SimulationData$results$resultLogisticCoP$VECI.groupA.multi.UB/100,
logisticCoPVE.groupB.add = SimulationData$results$resultLogisticCoP$VE.groupB.add/100,
logisticCoPCI.LB.groupB.add = SimulationData$results$resultLogisticCoP$VECI.groupB.add.LB/100,
logisticCoPCI.UB.groupB.add = SimulationData$results$resultLogisticCoP$VECI.groupB.add.UB/100,
logisticCoPVE.groupB.multi = SimulationData$results$resultLogisticCoP$VE.groupB.multi/100,
logisticCoPCI.LB.groupB.multi = SimulationData$results$resultLogisticCoP$VECI.groupB.multi.LB/100,
logisticCoPCI.UB.groupB.multi = SimulationData$results$resultLogisticCoP$VECI.groupB.multi.UB/100,
logisticCoPP.T.add = SimulationData$results$resultLogisticCoP$pval.titer.add,
logisticCoPP.A.add = SimulationData$results$resultLogisticCoP$pval.covariateBinary.add,
logisticCoPP.T.multi = SimulationData$results$resultLogisticCoP$pval.titer.multi,
logisticCoPP.A.multi = SimulationData$results$resultLogisticCoP$pval.covariateBinary.multi,
logisticCoPP.inter.multi = SimulationData$results$resultLogisticCoP$pval.interaction.multi,
logisticCoPAIC.add = SimulationData$results$resultLogisticCoP$aic.aic.add,
logisticCoPAIC.multi = SimulationData$results$resultLogisticCoP$aic.aic.multi,
#
logisticQuadCoPVE.all.add = SimulationData$results$resultLogisticQuadCoP$VE.all.add/100,
logisticQuadCoPCI.LB.all.add = SimulationData$results$resultLogisticQuadCoP$VECI.all.add.LB/100,
logisticQuadCoPCI.UB.all.add = SimulationData$results$resultLogisticQuadCoP$VECI.all.add.UB/100,
logisticQuadCoPVE.all.multi = SimulationData$results$resultLogisticQuadCoP$VE.all.multi/100,
logisticQuadCoPCI.LB.all.multi = SimulationData$results$resultLogisticQuadCoP$VECI.all.multi.LB/100,
logisticQuadCoPCI.UB.all.multi = SimulationData$results$resultLogisticQuadCoP$VECI.all.multi.UB/100,
logisticQuadCoPVE.groupA.add = SimulationData$results$resultLogisticQuadCoP$VE.groupA.add/100,
logisticQuadCoPCI.LB.groupA.add = SimulationData$results$resultLogisticQuadCoP$VECI.groupA.add.LB/100,
logisticQuadCoPCI.UB.groupA.add = SimulationData$results$resultLogisticQuadCoP$VECI.groupA.add.UB/100,
logisticQuadCoPVE.groupA.multi = SimulationData$results$resultLogisticQuadCoP$VE.groupA.multi/100,
logisticQuadCoPCI.LB.groupA.multi = SimulationData$results$resultLogisticQuadCoP$VECI.groupA.multi.LB/100,
logisticQuadCoPCI.UB.groupA.multi = SimulationData$results$resultLogisticQuadCoP$VECI.groupA.multi.UB/100,
logisticQuadCoPVE.groupB.add = SimulationData$results$resultLogisticQuadCoP$VE.groupB.add/100,
logisticQuadCoPCI.LB.groupB.add = SimulationData$results$resultLogisticQuadCoP$VECI.groupB.add.LB/100,
logisticQuadCoPCI.UB.groupB.add = SimulationData$results$resultLogisticQuadCoP$VECI.groupB.add.UB/100,
logisticQuadCoPVE.groupB.multi = SimulationData$results$resultLogisticQuadCoP$VE.groupB.multi/100,
logisticQuadCoPCI.LB.groupB.multi = SimulationData$results$resultLogisticQuadCoP$VECI.groupB.multi.LB/100,
logisticQuadCoPCI.UB.groupB.multi = SimulationData$results$resultLogisticQuadCoP$VECI.groupB.multi.UB/100,
logisticQuadCoPP.T.add = SimulationData$results$resultLogisticQuadCoP$pval.titer.add,
logisticQuadCoPP.T2.add = SimulationData$results$resultLogisticQuadCoP$pval.quadtiter.add,
logisticQuadCoPP.A.add = SimulationData$results$resultLogisticQuadCoP$pval.covariateBinary.add,
logisticQuadCoPP.T.multi = SimulationData$results$resultLogisticQuadCoP$pval.titer.multi,
logisticQuadCoPP.T2.multi = SimulationData$results$resultLogisticQuadCoP$pval.quadtiter.multi,
logisticQuadCoPP.A.multi = SimulationData$results$resultLogisticQuadCoP$pval.covariateBinary.multi,
logisticQuadCoPP.inter.multi = SimulationData$results$resultLogisticQuadCoP$pval.interaction.multi,
logisticQuadCoPAIC.add = SimulationData$results$resultLogisticQuadCoP$aic.aic.add,
logisticQuadCoPAIC.multi = SimulationData$results$resultLogisticQuadCoP$aic.aic.multi
)
## Positive test for a covariate effect, typical approach ----
# test is positive either if the interaction term in (in the multiplicative model) is significant
# or id the covariate term (in the additive model) is significant
Results <- Results %>% mutate(positive.typical = (logisticP.A.add < 0.05 | logisticP.inter.multi < 0.05))
## Positive test for a covariate effect, CoP-based approach, linear term for titer
Results <- Results %>%
mutate(positive.CoP = (logisticCoPP.A.add < 0.05 | logisticCoPP.inter.multi < 0.05)) %>%
mutate(positive.quadCoP = (logisticQuadCoPP.A.add < 0.05 | logisticQuadCoPP.inter.multi < 0.05))
positive <- list("typical" = sum(Results$positive.typical)/nrow(Results),
"CoPbased.linear" = sum(Results$positive.CoP)/nrow(Results),
"CoPbased.quadratic" = sum(Results$positive.quadCoP)/nrow(Results))
positive2500 <- list("typical" = sum(Results$positive.typical[1:2500])/2500,
"CoPbased.linear" = sum(Results$positive.CoP[1:2500])/2500,
"CoPbased.quadratic" = sum(Results$positive.quadCoP[1:2500])/1:2500)
#### 1a Results based on typical approach, model without an interaction term ----
## Width of CI plot
# Compare logistic additive model and case-count
WidthDataset.1a <- prepareDataCIWidth(CILB1.groupA = Results$logisticCI.LB.groupA.add*100,
CILB1.groupB = Results$logisticCI.LB.groupB.add*100,
CIUB1.groupA = Results$logisticCI.UB.groupA.add*100,
CIUB1.groupB = Results$logisticCI.UB.groupB.add*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.logistic.add <- ggplotCIBA(WidthDataset.1a$data)
narrower.casecount.logistic.add <- WidthDataset.1a$narrower
#### 1b Results based on typical approach, model with an interaction term ----
## Width of CI plot
# Compare logistic multiplicative model and case-count
WidthDataset.1b <- prepareDataCIWidth(CILB1.groupA = Results$logisticCI.LB.groupA.multi*100,
CILB1.groupB = Results$logisticCI.LB.groupB.multi*100,
CIUB1.groupA = Results$logisticCI.UB.groupA.multi*100,
CIUB1.groupB = Results$logisticCI.UB.groupB.multi*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.logistic.multi <- ggplotCIBA(WidthDataset.1b$data)
narrower.casecount.logistic.multi <- WidthDataset.1b$narrower
#### 1final Results based on typical approach, selected model ----
## Model selection, typical approach
Results <- Results %>% mutate(selected.typical.model = "multi")
Results$selected.typical.model[Results$logisticAIC.add < Results$logisticAIC.multi] <- "add"
selected.typical.model <- table(Results$selected.typical.model)
## VE
Results <- Results %>%
mutate(selected.typical.VE.all = ifelse(selected.typical.model == "add", logisticVE.all.add, logisticVE.all.multi)) %>%
mutate(selected.typical.CI.LB.all = ifelse(selected.typical.model == "add", logisticCI.LB.all.add, logisticCI.LB.all.multi)) %>%
mutate(selected.typical.CI.UB.all = ifelse(selected.typical.model == "add", logisticCI.UB.all.add, logisticCI.UB.all.multi)) %>%
mutate(selected.typical.VE.groupA = ifelse(selected.typical.model == "add", logisticVE.groupA.add, logisticVE.groupA.multi)) %>%
mutate(selected.typical.CI.LB.groupA = ifelse(selected.typical.model == "add", logisticCI.LB.groupA.add, logisticCI.LB.groupA.multi)) %>%
mutate(selected.typical.CI.UB.groupA = ifelse(selected.typical.model == "add", logisticCI.UB.groupA.add, logisticCI.UB.groupA.multi)) %>%
mutate(selected.typical.VE.groupB = ifelse(selected.typical.model == "add", logisticVE.groupB.add, logisticVE.groupB.multi)) %>%
mutate(selected.typical.VE.groupB = ifelse(selected.typical.model == "add", logisticVE.groupB.add, logisticVE.groupB.multi)) %>%
mutate(selected.typical.CI.LB.groupB = ifelse(selected.typical.model == "add", logisticCI.LB.groupB.add, logisticCI.LB.groupB.multi)) %>%
mutate(selected.typical.CI.UB.groupB = ifelse(selected.typical.model == "add", logisticCI.UB.groupB.add, logisticCI.UB.groupB.multi))
# Width of CI plot
# Compare case-count and logistic selected model
WidthDataset.1f1 <- prepareDataCIWidth(CILB1.groupA = Results$selected.typical.CI.LB.groupA*100,
CILB1.groupB = Results$selected.typical.CI.LB.groupB*100,
CIUB1.groupA = Results$selected.typical.CI.UB.groupA*100,
CIUB1.groupB = Results$selected.typical.CI.UB.groupB*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.logistic.selected <- ggplotCIBA(WidthDataset.1f1$data)
narrower.casecount.logistic.selected <- WidthDataset.1f1$narrower
#### 2a Results based on linear CoP-based approach, model without an interaction term ----
## VE boxplots
efficacyDF<- prepareDataVE(CoP.all = Results$logisticCoPVE.all.add,
typical.all = Results$logisticVE.all.add,
CoP.groupA = Results$logisticCoPVE.groupA.add,
typical.groupA = Results$logisticVE.groupA.add,
CoP.groupB = Results$logisticCoPVE.groupB.add,
typical.groupB = Results$logisticVE.groupB.add,
casecount.all = Results$casecountVE.all,
casecount.groupA = Results$casecountVE.groupA,
casecount.groupB = Results$casecountVE.groupB)
boxplots.linear.add <- ggplotEfficacy(efficacyDF,
Results$trueVE.all[1],
Results$trueVE.groupA[1],
Results$trueVE.groupB[1],
"",
"VE")
## Width of CI plot
# Compare logistic additive model and logistic with linear titer additive model
WidthDataset.2a1 <- prepareDataCIWidth(CILB1.groupA = Results$logisticCoPCI.LB.groupA.add*100,
CILB1.groupB = Results$logisticCoPCI.LB.groupB.add*100,
CIUB1.groupA = Results$logisticCoPCI.UB.groupA.add*100,
CIUB1.groupB = Results$logisticCoPCI.UB.groupB.add*100,
CILB2.groupA = Results$logisticCI.LB.groupA.add*100,
CILB2.groupB = Results$logisticCI.LB.groupB.add*100,
CIUB2.groupA = Results$logisticCI.UB.groupA.add*100,
CIUB2.groupB = Results$logisticCI.UB.groupB.add*100)
widthplot.logistic.CoP.linear.add <- ggplotCIBA(WidthDataset.2a1$data)
narrower.logistic.CoP.linear.add <- WidthDataset.2a1$narrower
# Compare case-count and logistic with linear titer additive model
WidthDataset.2a2 <- prepareDataCIWidth(CILB1.groupA = Results$logisticCoPCI.LB.groupA.add*100,
CILB1.groupB = Results$logisticCoPCI.LB.groupB.add*100,
CIUB1.groupA = Results$logisticCoPCI.UB.groupA.add*100,
CIUB1.groupB = Results$logisticCoPCI.UB.groupB.add*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.CoP.linear.add <- ggplotCIBA(WidthDataset.2a2$data)
narrower.casecount.CoP.linear.add <- WidthDataset.2a2$narrower
#### 2b Results based on linear CoP-based approach, model with an interaction term ----
## VE boxplots
efficacyDF<- prepareDataVE(CoP.all = Results$logisticCoPVE.all.multi,
typical.all = Results$logisticVE.all.multi,
CoP.groupA = Results$logisticCoPVE.groupA.multi,
typical.groupA = Results$logisticVE.groupA.multi,
CoP.groupB = Results$logisticCoPVE.groupB.multi,
typical.groupB = Results$logisticVE.groupB.multi,
casecount.all = Results$casecountVE.all,
casecount.groupA = Results$casecountVE.groupA,
casecount.groupB = Results$casecountVE.groupB)
boxplots.linear.multi <- ggplotEfficacy(efficacyDF,
Results$trueVE.all[1],
Results$trueVE.groupA[1],
Results$trueVE.groupB[1],
"",
"VE")
## Width of CI plot
# Compare logistic multiplicative model and logistic with linear titer multiplicative model
WidthDataset.2b1 <- prepareDataCIWidth(CILB1.groupA = Results$logisticCoPCI.LB.groupA.multi*100,
CILB1.groupB = Results$logisticCoPCI.LB.groupB.multi*100,
CIUB1.groupA = Results$logisticCoPCI.UB.groupA.multi*100,
CIUB1.groupB = Results$logisticCoPCI.UB.groupB.multi*100,
CILB2.groupA = Results$logisticCI.LB.groupA.multi*100,
CILB2.groupB = Results$logisticCI.LB.groupB.multi*100,
CIUB2.groupA = Results$logisticCI.UB.groupA.multi*100,
CIUB2.groupB = Results$logisticCI.UB.groupB.multi*100)
widthplot.logistic.CoP.linear.multi <- ggplotCIBA(WidthDataset.2b1$data)
narrower.logistic.CoP.linear.multi <- WidthDataset.2b1$narrower
# Compare case-count and logistic with linear titer multiplicative model
WidthDataset.2b2 <- prepareDataCIWidth(CILB1.groupA = Results$logisticCoPCI.LB.groupA.multi*100,
CILB1.groupB = Results$logisticCoPCI.LB.groupB.multi*100,
CIUB1.groupA = Results$logisticCoPCI.UB.groupA.multi*100,
CIUB1.groupB = Results$logisticCoPCI.UB.groupB.multi*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.CoP.linear.multi <- ggplotCIBA(WidthDataset.2b2$data)
narrower.casecount.CoP.linear.multi <- WidthDataset.2b2$narrower
#### 2c Results based on quadratic CoP-based approach, model without an interaction term ----
## VE boxplots
efficacyDF<- prepareDataVE(CoP.all = Results$logisticQuadCoPVE.all.add,
typical.all = Results$logisticVE.all.add,
CoP.groupA = Results$logisticQuadCoPVE.groupA.add,
typical.groupA = Results$logisticVE.groupA.add,
CoP.groupB = Results$logisticQuadCoPVE.groupB.add,
typical.groupB = Results$logisticVE.groupB.add,
casecount.all = Results$casecountVE.all,
casecount.groupA = Results$casecountVE.groupA,
casecount.groupB = Results$casecountVE.groupB)
boxplots.quad.add <- ggplotEfficacy(efficacyDF,
Results$trueVE.all[1],
Results$trueVE.groupA[1],
Results$trueVE.groupB[1],
"",
"VE")
## Width of CI plot
# Compare logistic additive model and logistic with quadratic titer additive model
WidthDataset.2c1 <- prepareDataCIWidth(CILB1.groupA = Results$logisticQuadCoPCI.LB.groupA.add*100,
CILB1.groupB = Results$logisticQuadCoPCI.LB.groupB.add*100,
CIUB1.groupA = Results$logisticQuadCoPCI.UB.groupA.add*100,
CIUB1.groupB = Results$logisticQuadCoPCI.UB.groupB.add*100,
CILB2.groupA = Results$logisticCI.LB.groupA.add*100,
CILB2.groupB = Results$logisticCI.LB.groupB.add*100,
CIUB2.groupA = Results$logisticCI.UB.groupA.add*100,
CIUB2.groupB = Results$logisticCI.UB.groupB.add*100)
widthplot.logistic.CoP.quad.add <- ggplotCIBA(WidthDataset.2c1$data)
narrower.logistic.CoP.quad.add <- WidthDataset.2c1$narrower
# Compare case-count and logistic with quadratic titer additive model
WidthDataset.2c2 <- prepareDataCIWidth(CILB1.groupA = Results$logisticQuadCoPCI.LB.groupA.add*100,
CILB1.groupB = Results$logisticQuadCoPCI.LB.groupB.add*100,
CIUB1.groupA = Results$logisticQuadCoPCI.UB.groupA.add*100,
CIUB1.groupB = Results$logisticQuadCoPCI.UB.groupB.add*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.CoP.quad.add <- ggplotCIBA(WidthDataset.2c2$data)
narrower.casecount.CoP.quad.add <- WidthDataset.2c2$narrower
#### 2d Results based on quadratic CoP-based approach, model with an interaction term ----
## VE boxplots
efficacyDF<- prepareDataVE(CoP.all = Results$logisticQuadCoPVE.all.multi,
typical.all = Results$logisticVE.all.multi,
CoP.groupA = Results$logisticQuadCoPVE.groupA.multi,
typical.groupA = Results$logisticVE.groupA.multi,
CoP.groupB = Results$logisticQuadCoPVE.groupB.multi,
typical.groupB = Results$logisticVE.groupB.multi,
casecount.all = Results$casecountVE.all,
casecount.groupA = Results$casecountVE.groupA,
casecount.groupB = Results$casecountVE.groupB)
boxplots.quad.multi <- ggplotEfficacy(efficacyDF,
Results$trueVE.all[1],
Results$trueVE.groupA[1],
Results$trueVE.groupB[1],
"",
"VE")
## Width of CI plot
# Compare logistic multiplicative model and logistic with quadratic titer multiplicative model
WidthDataset.2d1 <- prepareDataCIWidth(CILB1.groupA = Results$logisticQuadCoPCI.LB.groupA.multi*100,
CILB1.groupB = Results$logisticQuadCoPCI.LB.groupB.multi*100,
CIUB1.groupA = Results$logisticQuadCoPCI.UB.groupA.multi*100,
CIUB1.groupB = Results$logisticQuadCoPCI.UB.groupB.multi*100,
CILB2.groupA = Results$logisticCI.LB.groupA.multi*100,
CILB2.groupB = Results$logisticCI.LB.groupB.multi*100,
CIUB2.groupA = Results$logisticCI.UB.groupA.multi*100,
CIUB2.groupB = Results$logisticCI.UB.groupB.multi*100)
widthplot.logistic.CoP.quad.multi <- ggplotCIBA(WidthDataset.2d1$data)
narrower.logistic.CoP.quad.multi <- WidthDataset.2d1$narrower
# Compare logistic additive model and logistic with quadratic titer additive model
WidthDataset.2d2 <- prepareDataCIWidth(CILB1.groupA = Results$logisticQuadCoPCI.LB.groupA.multi*100,
CILB1.groupB = Results$logisticQuadCoPCI.LB.groupB.multi*100,
CIUB1.groupA = Results$logisticQuadCoPCI.UB.groupA.multi*100,
CIUB1.groupB = Results$logisticQuadCoPCI.UB.groupB.multi*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.CoP.quad.multi <- ggplotCIBA(WidthDataset.2d2$data)
narrower.casecount.CoP.quad.multi <- WidthDataset.2d2$narrower
#### 2final Results based on CoP-based approach, selected model ----
## Model selection, CoP-based approach
Results <- Results %>% mutate(minAIC = pmin(logisticCoPAIC.add,
logisticCoPAIC.multi,
logisticQuadCoPAIC.add,
logisticQuadCoPAIC.multi))
Results$selected.CoP.model[Results$logisticCoPAIC.add == Results$minAIC] <- "linear add"
Results$selected.CoP.model[Results$logisticCoPAIC.multi == Results$minAIC] <- "linear multi"
Results$selected.CoP.model[Results$logisticQuadCoPAIC.add == Results$minAIC] <- "quad add"
Results$selected.CoP.model[Results$logisticQuadCoPAIC.multi == Results$minAIC] <- "quad multi"
selected.CoP.model <- table(Results$selected.CoP.model)
## VE
Results <- Results %>%
mutate(selected.CoP.VE.all = case_when(selected.CoP.model == "linear add" ~ logisticCoPVE.all.add,
selected.CoP.model == "linear multi" ~ logisticCoPVE.all.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPVE.all.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPVE.all.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.CI.LB.all = case_when(selected.CoP.model == "linear add" ~ logisticCoPCI.LB.all.add,
selected.CoP.model == "linear multi" ~ logisticCoPCI.LB.all.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPCI.LB.all.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPCI.LB.all.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.CI.UB.all = case_when(selected.CoP.model == "linear add" ~ logisticCoPCI.UB.all.add,
selected.CoP.model == "linear multi" ~ logisticCoPCI.UB.all.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPCI.UB.all.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPCI.UB.all.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.VE.groupA = case_when(selected.CoP.model == "linear add" ~ logisticCoPVE.groupA.add,
selected.CoP.model == "linear multi" ~ logisticCoPVE.groupA.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPVE.groupA.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPVE.groupA.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.CI.LB.groupA = case_when(selected.CoP.model == "linear add" ~ logisticCoPCI.LB.groupA.add,
selected.CoP.model == "linear multi" ~ logisticCoPCI.LB.groupA.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPCI.LB.groupA.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPCI.LB.groupA.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.CI.UB.groupA = case_when(selected.CoP.model == "linear add" ~ logisticCoPCI.UB.groupA.add,
selected.CoP.model == "linear multi" ~ logisticCoPCI.UB.groupA.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPCI.UB.groupA.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPCI.UB.groupA.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.VE.groupB = case_when(selected.CoP.model == "linear add" ~ logisticCoPVE.groupB.add,
selected.CoP.model == "linear multi" ~ logisticCoPVE.groupB.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPVE.groupB.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPVE.groupB.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.CI.LB.groupB = case_when(selected.CoP.model == "linear add" ~ logisticCoPCI.LB.groupB.add,
selected.CoP.model == "linear multi" ~ logisticCoPCI.LB.groupB.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPCI.LB.groupB.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPCI.LB.groupB.multi,
TRUE ~ NA_real_)) %>%
mutate(selected.CoP.CI.UB.groupB = case_when(selected.CoP.model == "linear add" ~ logisticCoPCI.UB.groupB.add,
selected.CoP.model == "linear multi" ~ logisticCoPCI.UB.groupB.multi,
selected.CoP.model == "quad add" ~ logisticQuadCoPCI.UB.groupB.add,
selected.CoP.model == "quad multi" ~ logisticQuadCoPCI.UB.groupB.multi,
TRUE ~ NA_real_))
## VE boxplots
efficacyDF<- prepareDataVE(CoP.all = Results$selected.CoP.VE.all,
typical.all = Results$selected.typical.VE.all,
CoP.groupA = Results$selected.CoP.VE.groupA,
typical.groupA = Results$selected.typical.VE.groupA,
CoP.groupB = Results$selected.CoP.VE.groupB,
typical.groupB = Results$selected.typical.VE.groupB,
casecount.all = Results$casecountVE.all,
casecount.groupA = Results$casecountVE.groupA,
casecount.groupB = Results$casecountVE.groupB)
boxplots.selected <- ggplotEfficacy(efficacyDF,
Results$trueVE.all[1],
Results$trueVE.groupA[1],
Results$trueVE.groupB[1],
"",
"VE")
# Width of CI plot
# Compare logistic selected model and logistic with titer selected model
WidthDataset.2f1 <- prepareDataCIWidth(CILB1.groupA = Results$selected.CoP.CI.LB.groupA*100,
CILB1.groupB = Results$selected.CoP.CI.LB.groupB*100,
CIUB1.groupA = Results$selected.CoP.CI.UB.groupA*100,
CIUB1.groupB = Results$selected.CoP.CI.UB.groupB*100,
CILB2.groupA = Results$selected.typical.CI.LB.groupA*100,
CILB2.groupB = Results$selected.typical.CI.LB.groupB*100,
CIUB2.groupA = Results$selected.typical.CI.UB.groupA*100,
CIUB2.groupB = Results$selected.typical.CI.UB.groupB*100)
widthplot.logistic.CoP.selected <- ggplotCIBA(WidthDataset.2f1$data)
narrower.logistic.CoP.selected <- WidthDataset.2f1$narrower
# Compare case-count and logistic with titer selected model
WidthDataset.2f2 <- prepareDataCIWidth(CILB1.groupA = Results$selected.CoP.CI.LB.groupA*100,
CILB1.groupB = Results$selected.CoP.CI.LB.groupB*100,
CIUB1.groupA = Results$selected.CoP.CI.UB.groupA*100,
CIUB1.groupB = Results$selected.CoP.CI.UB.groupB*100,
CILB2.groupA = Results$casecountCI.LB.groupA*100,
CILB2.groupB = Results$casecountCI.LB.groupB*100,
CIUB2.groupA = Results$casecountCI.UB.groupA*100,
CIUB2.groupB = Results$casecountCI.UB.groupB*100)
widthplot.casecount.CoP.selected <- ggplotCIBA(WidthDataset.2f2$data)
narrower.casecount.CoP.selected <- WidthDataset.2f2$narrower
#### Summary of results ----
## Boxplots of VE
boxplots <- list(linear.add = boxplots.linear.add,
linear.multi = boxplots.linear.multi,
quad.add = boxplots.quad.add,
quad.multi = boxplots.quad.multi,
selected = boxplots.selected)
## Widths of VE CI
widthdatasets <- list(casecount.logistic.selected = WidthDataset.1f1,
casecount.CoP.selected = WidthDataset.2f2,
logistic.CoP.selected = WidthDataset.2f1,
casecount.logistic.add = WidthDataset.1a,
casecount.logistic.multi = WidthDataset.1b,
logistic.CoP.linear.add = WidthDataset.2a1,
casecount.CoP.linear.add = WidthDataset.2a2,
logistic.CoP.linear.multi = WidthDataset.2b1,
casecount.CoP.linear.multi = WidthDataset.2b2,
logistic.CoP.quad.add = WidthDataset.2c1,
casecount.CoP.quad.add = WidthDataset.2c2,
logistic.CoP.quad.multi = WidthDataset.2d1,
casecount.CoP.quad.multi = WidthDataset.2d2
)
widthplots <- list(casecount.logistic.selected = widthplot.casecount.logistic.selected,
casecount.CoP.selected = widthplot.casecount.CoP.selected,
logistic.CoP.selected = widthplot.logistic.CoP.selected,
casecount.logistic.add = widthplot.casecount.logistic.add,
casecount.logistic.multi = widthplot.casecount.logistic.multi,
logistic.CoP.linear.add = widthplot.logistic.CoP.linear.add,
casecount.CoP.linear.add = widthplot.casecount.CoP.linear.add,
logistic.CoP.linear.multi = widthplot.logistic.CoP.linear.multi,
casecount.CoP.linear.multi = widthplot.casecount.CoP.linear.multi,
logistic.CoP.quad.add = widthplot.logistic.CoP.quad.add,
casecount.CoP.quad.add = widthplot.casecount.CoP.quad.add,
logistic.CoP.quad.multi = widthplot.logistic.CoP.quad.multi,
casecount.CoP.quad.multi = widthplot.casecount.CoP.quad.multi
)
narrower <- list(casecount.logistic.selected = narrower.casecount.logistic.selected,
casecount.CoP.selected = narrower.casecount.CoP.selected,
logistic.CoP.selected = narrower.logistic.CoP.selected,
casecount.logistic.add = narrower.casecount.logistic.add,
casecount.logistic.multi = narrower.casecount.logistic.multi,
logistic.CoP.linear.add = narrower.logistic.CoP.linear.add,
casecount.CoP.linear.add = narrower.casecount.CoP.linear.add,
logistic.CoP.linear.multi = narrower.logistic.CoP.linear.multi,
casecount.CoP.linear.multi = narrower.casecount.CoP.linear.multi,
logistic.CoP.quad.add = narrower.logistic.CoP.quad.add,
casecount.CoP.quad.add = narrower.casecount.CoP.quad.add,
logistic.CoP.quad.multi = narrower.logistic.CoP.quad.multi,
casecount.CoP.quad.multi = narrower.casecount.CoP.quad.multi
)
## Median VE
medianVE <- list("casecount" = list(all = median(Results$casecountVE.all),
groupA = median(Results$casecountVE.groupA),
groupB = median(Results$casecountVE.groupB)),
"typical.add" = list(all = median(Results$logisticVE.all.add),
groupA = median(Results$logisticVE.groupA.add),
groupB = median(Results$logisticVE.groupB.add)),
"typical.multi" = list(all = median(Results$logisticVE.all.multi),
groupA = median(Results$logisticVE.groupA.multi),
groupB = median(Results$logisticVE.groupB.multi)),
"typical.selected" = list(all = median(Results$selected.typical.VE.all),
groupA = median(Results$selected.typical.VE.groupA),
groupB = median(Results$selected.typical.VE.groupB)),
"CoP.linear.add" = list(all = median(Results$logisticCoPVE.all.add),
groupA = median(Results$logisticCoPVE.groupA.add),
groupB = median(Results$logisticCoPVE.groupB.add)),
"CoP.linear.multi" = list(all = median(Results$logisticCoPVE.all.multi),
groupA = median(Results$logisticCoPVE.groupA.multi),
groupB = median(Results$logisticCoPVE.groupB.multi)),
"CoP.quad.add" = list(all = median(Results$logisticQuadCoPVE.all.add),
groupA = median(Results$logisticQuadCoPVE.groupA.add),
groupB = median(Results$logisticQuadCoPVE.groupB.add)),
"CoP.quad.multi" = list(all = median(Results$logisticQuadCoPVE.all.multi),
groupA = median(Results$logisticQuadCoPVE.groupA.multi),
groupB = median(Results$logisticQuadCoPVE.groupB.multi)),
"CoP.selected" = list(all = median(Results$selected.CoP.VE.all),
groupA = median(Results$selected.CoP.VE.groupA),
groupB = median(Results$selected.CoP.VE.groupB)))
## Coverage of VE CI
coverage <- list("casecount" = list(all = sum((Results$casecountCI.LB.all < Results$trueVE.all[1])&(Results$casecountCI.UB.all > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$casecountCI.LB.groupA < Results$trueVE.groupA[1])&(Results$casecountCI.UB.groupA > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$casecountCI.LB.groupB < Results$trueVE.groupB[1])&(Results$casecountCI.UB.groupB > Results$trueVE.groupB[1]))/nrow(Results)),
"typical.add" = list(all = sum((Results$logisticCI.LB.all.add < Results$trueVE.all[1])&(Results$logisticCI.UB.all.add > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$logisticCI.LB.groupA.add < Results$trueVE.groupA[1])&(Results$logisticCI.UB.groupA.add > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$logisticCI.LB.groupB.add < Results$trueVE.groupB[1])&(Results$logisticCI.UB.groupB.add > Results$trueVE.groupB[1]))/nrow(Results)),
"typical.multi" = list(all = sum((Results$logisticCI.LB.all.multi < Results$trueVE.all[1])&(Results$logisticCI.UB.all.multi > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$logisticCI.LB.groupA.multi < Results$trueVE.groupA[1])&(Results$logisticCI.UB.groupA.multi > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$logisticCI.LB.groupB.multi < Results$trueVE.groupB[1])&(Results$logisticCI.UB.groupB.multi > Results$trueVE.groupB[1]))/nrow(Results)),
"typical.selected" = list(all = sum((Results$selected.typical.CI.LB.all < Results$trueVE.all[1])&(Results$selected.typical.CI.UB.all > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$selected.typical.CI.LB.groupA < Results$trueVE.groupA[1])&(Results$selected.typical.CI.UB.groupA > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$selected.typical.CI.LB.groupB < Results$trueVE.groupB[1])&(Results$selected.typical.CI.UB.groupB > Results$trueVE.groupB[1]))/nrow(Results)),
"CoP.linear.add" = list(all = sum((Results$logisticCoPCI.LB.all.add < Results$trueVE.all[1])&(Results$logisticCoPCI.UB.all.add > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$logisticCoPCI.LB.groupA.add < Results$trueVE.groupA[1])&(Results$logisticCoPCI.UB.groupA.add > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$logisticCoPCI.LB.groupB.add < Results$trueVE.groupB[1])&(Results$logisticCoPCI.UB.groupB.add > Results$trueVE.groupB[1]))/nrow(Results)),
"CoP.linear.multi" = list(all = sum((Results$logisticCoPCI.LB.all.multi < Results$trueVE.all[1])&(Results$logisticCoPCI.UB.all.multi > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$logisticCoPCI.LB.groupA.multi < Results$trueVE.groupA[1])&(Results$logisticCoPCI.UB.groupA.multi > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$logisticCoPCI.LB.groupB.multi < Results$trueVE.groupB[1])&(Results$logisticCoPCI.UB.groupB.multi > Results$trueVE.groupB[1]))/nrow(Results)),
"CoP.quad.add" = list(all = sum((Results$logisticQuadCoPCI.LB.all.add < Results$trueVE.all[1])&(Results$logisticQuadCoPCI.UB.all.add > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$logisticCoPCI.LB.groupA.add < Results$trueVE.groupA[1])&(Results$logisticCoPCI.UB.groupA.add > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$logisticQuadCoPCI.LB.groupB.add < Results$trueVE.groupB[1])&(Results$logisticQuadCoPCI.UB.groupB.add > Results$trueVE.groupB[1]))/nrow(Results)),
"CoP.quad.multi" = list(all = sum((Results$logisticQuadCoPCI.LB.all.multi < Results$trueVE.all[1])&(Results$logisticQuadCoPCI.UB.all.multi > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$logisticQuadCoPCI.LB.groupA.multi < Results$trueVE.groupA[1])&(Results$logisticQuadCoPCI.UB.groupA.multi > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$logisticQuadCoPCI.LB.groupB.multi < Results$trueVE.groupB[1])&(Results$logisticQuadCoPCI.UB.groupB.multi > Results$trueVE.groupB[1]))/nrow(Results)),
"CoP.selected" = list(all = sum((Results$selected.CoP.CI.LB.all < Results$trueVE.all[1])&(Results$selected.CoP.CI.UB.all > Results$trueVE.all[1]))/nrow(Results),
groupA = sum((Results$selected.CoP.CI.LB.groupA < Results$trueVE.groupA[1])&(Results$selected.CoP.CI.UB.groupA > Results$trueVE.groupA[1]))/nrow(Results),
groupB = sum((Results$selected.CoP.CI.LB.groupB < Results$trueVE.groupB[1])&(Results$selected.CoP.CI.UB.groupB > Results$trueVE.groupB[1]))/nrow(Results)))
## Truth used for simulating trial data
truth <- SimulationData$truth
## True VE
trueVE <- list("all" = Results$trueVE.all[1],
"groupA" = Results$trueVE.groupA[1],
"groupB" = Results$trueVE.groupB[1])
## Summarizing data to be used by the roc function (from pROC package)
df <- data.frame(Results$logisticCoPP.inter.multi,Results$logisticCoPP.A.add)
predictor.roc.CoP <- apply(df, 1, FUN = min)
dff <- data.frame(Results$logisticP.inter.multi,Results$logisticP.A.add)
predictor.roc.typical <- apply(dff, 1, FUN = min)
predictors.roc <- list("CoP" = predictor.roc.CoP,
"typical" = predictor.roc.typical)
return(list("trueVE" = trueVE,
"medianVE" = medianVE,
"coverage" = coverage,
"positive" = positive,
"positive2500" = positive2500,
"boxplots" = boxplots,
"truth" = truth,
"predictors.roc" = predictors.roc,
"widthplots" = widthplots,
"widthdatasets" = widthdatasets,
"narrowerCI" = narrower,
"selected.typical.model" = selected.typical.model,
"selected.CoP.model" = selected.CoP.model,
"Results" = Results))
}
### Numerical results ----
## Coverage
# Calculates coverage from lower & upper CI, in comparison with true efficacy - based on PoDBA package data structure
CoverageFun <- function(LowerCI,
UpperCI,
TrueEfficacy,
NumberOfSimulations){
coverage <- sum(LowerCI < TrueEfficacy & UpperCI > TrueEfficacy)/NumberOfSimulations
return(coverage)
}
# Coverage of both case-count Efficacay and PoDBA efficacy - based on PoDBA package data structure
CoverageDataset <- function(results1, results2, ClinicalTrial, includeNAs = T, DecimalRound = 2, group = "all", mod = "add", method1, method2){
CILB1 <- as.numeric(unlist(results1[paste0("VECI.",group,".",mod,".LB")]))/100
CILB2 <- unlist(results2[paste0("VECI.",group,".lowerBound")])
CIUB1 <- as.numeric(unlist(results1[paste0("VECI.",group,".",mod,".UB")]))/100
CIUB2 <- unlist(results2[paste0("VECI.",group,".upperBound")])
TrueEfficacy <- unlist(ClinicalTrial[paste0("trueEfficacy.",group)])
if(includeNAs){
noSim <- length(TrueEfficacy)
}else{
noSim <- length(na.omit(TrueEfficacy))
}
results1$VECI.CILow <- CILB1[!is.na(TrueEfficacy)]
results1$VECI.CIHigh <- CIUB1[!is.na(TrueEfficacy)]
results2$VECI.lowerBound <- CILB2[!is.na(TrueEfficacy)]
results2$VECI.upperBound <- CIUB2[!is.na(TrueEfficacy)]
ClinicalTrial$trueEfficacy <- ClinicalTrial$trueEfficacy.all[!is.na(TrueEfficacy)]
data <- c(results1[c("VECI.CILow",
"VECI.CIHigh")],
results2[c("VECI.lowerBound",
"VECI.upperBound")],
ClinicalTrial[paste0("trueEfficacy.",group)])
data <- lapply(data, function(x) ifelse(is.na(x), 1, x) )
Coverage1Eff95 <- with(data,
CoverageFun(VECI.CILow,
VECI.CIHigh,
unique(na.omit(TrueEfficacy)),
noSim
))
Coverage2Eff95 <- with(data,
CoverageFun(VECI.lowerBound,
VECI.upperBound,
unique(na.omit(TrueEfficacy)),
noSim
))
out <- list()
name1 <- paste0("coverage.", method1,".",group)
name2 <- paste0("coverage.", method2,".",group)
out[[name1]] <- round(Coverage1Eff95*100,DecimalRound)
out[[name2]] <- round(Coverage2Eff95*100,DecimalRound)
return(out)
}
### Visualization ----
size <- 12
themePlot <-
theme(
plot.title = element_text(size=size,hjust = 0.5),
axis.text.y = element_text(size=size),
axis.text.x = element_text(size=size),
axis.title.y = element_text(size=size),
axis.title.x = element_text(size=size),
legend.text = element_text(size = size),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=0.5)
)
## Efficacy boxplot ----
# function for data preparation
prepareDataVE <- function(CoP.all,
typical.all,
CoP.groupA,
typical.groupA,
CoP.groupB,
typical.groupB,
casecount.all = Results$casecountVE.all,
casecount.groupA = Results$casecountVE.groupA,
casecount.groupB = Results$casecountVE.groupB){
df <- data.frame(value = c(CoP.all,
typical.all,
CoP.groupA,
typical.groupA,
CoP.groupB,
typical.groupB,
casecount.all,
casecount.groupA,
casecount.groupB),
type = c(rep("CoP-based", length(casecount.all)),
rep("typical", length(casecount.all)),
rep("CoP-based", length(casecount.all)),
rep("typical", length(casecount.all)),
rep("CoP-based", length(casecount.all)),
rep("typical", length(casecount.all)),
rep("case-count", 3*length(casecount.all))),
group = c(rep("overall", 2*length(casecount.all)),
rep("groupA", length(casecount.all)),
rep("groupA",length(casecount.all)),
rep("groupB", length(casecount.all)),
rep("groupB",length(casecount.all)),
rep("overall", length(casecount.all)),
rep("groupA",length(casecount.all)),
rep("groupB", length(casecount.all)))
)
df$type <- factor(df$type,levels = c("CoP-based","typical","case-count"),ordered = TRUE)
df$group <- factor(df$group,levels = c("groupA","groupB","overall"),ordered = TRUE)
return(df)
}
wprepareDataVE <- function(CoP.all,
typical.all,
CoP.groupA,
typical.groupA,
CoP.groupB,
typical.groupB){
df <- data.frame(value = c(CoP.all,
typical.all,
CoP.groupA,
typical.groupA,
CoP.groupB,
typical.groupB),
type = c(rep("CoP-based", length(CoP.all)),
rep("typical", length(typical.all)),
rep("CoP-based", length(CoP.groupA)),
rep("typical", length(typical.groupA)),
rep("CoP-based", length(CoP.groupB)),
rep("typical", length(typical.groupB))),
group = c(rep("overall", length(CoP.all)),
rep("overall", length(typical.all)),
rep("groupA", length(CoP.groupA)),
rep("groupA",length(typical.groupA)),
rep("groupB", length(CoP.groupB)),
rep("groupB",length(typical.groupB)))
)
df$type <- factor(df$type,levels = c("CoP-based","typical"),ordered = TRUE)
df$group <- factor(df$group,levels = c("groupA","groupB","overall"),ordered = TRUE)
return(df)
}
# ggplot function for efficacy point estimates
ggplotEfficacy <- function(data, trueVE, trueVE.groupA, trueVE.groupB, title = " ", ylabel = "Efficacy") {
ggplot(data, aes(x = type, y = value, col=group)) +
geom_boxplot(outlier.shape= 1, outlier.size=1, outlier.fill = NULL) +
# geom_jitter(aes(x = type, y = value, fill = NULL), width = 0.15, shape = 1, size = 2) +
geom_hline(aes(yintercept = trueVE.groupA),
color = "darkslategray3",
size = 0.5,
linetype = "dashed") +
geom_hline(aes(yintercept = trueVE.groupB),
color = "gray20",
size = 0.5,
linetype = "dashed") +
geom_hline(aes(yintercept = trueVE),
color = "gray",
size = 0.5,
linetype = "dashed") +
labs(x = NULL, y = ylabel) +
coord_cartesian(ylim = c(0,1)) +
scale_y_continuous(breaks = seq(from = 0, to = 1, by = 0.25),
limits = c(-5,1)) +
ggtitle(title) +
themePlot +
scale_color_manual(values=c("darkslategray3", "gray20", "gray"))
}
# ggplot function for efficacy point estimates using "wrongly" selected model
wggplotEfficacy <- function(data, trueVE, trueVE.groupA, trueVE.groupB, title = " ", ylabel = "Efficacy") {
ggplot(data, aes(x = type, y = value, col=group)) +
geom_violin(outlier.shape= 1, outlier.size=1, outlier.fill = NULL) +
# geom_jitter(aes(x = type, y = value, fill = NULL), width = 0.15, shape = 1, size = 2) +
geom_hline(aes(yintercept = trueVE.groupA),
color = "darkslategray3",
size = 0.5,
linetype = "dashed") +
geom_hline(aes(yintercept = trueVE.groupB),
color = "gray20",
size = 0.5,
linetype = "dashed") +
geom_hline(aes(yintercept = trueVE),
color = "gray",
size = 0.5,
linetype = "dashed") +
labs(x = NULL, y = ylabel) +
coord_cartesian(ylim = c(0,1)) +
scale_y_continuous(breaks = seq(from = 0, to = 1, by = 0.25),
limits = c(-5,1)) +
ggtitle(title) +
themePlot +
scale_color_manual(values=c("darkslategray3", "gray20", "gray"))
}
## Widths of VE CI ----
prepareDataCIWidth <- function(CILB1.groupA,
CILB1.groupB,
CIUB1.groupA,
CIUB1.groupB,
CILB2.groupA,
CILB2.groupB,
CIUB2.groupA,
CIUB2.groupB){
width1.groupA <- CIUB1.groupA-CILB1.groupA
width1.groupB <- CIUB1.groupB-CILB1.groupB
width2.groupA <- CIUB2.groupA-CILB2.groupA
width2.groupB <- CIUB2.groupB-CILB2.groupB
df <- data.frame(width1 = unlist(c(width1.groupA,
width1.groupB)),
width2 = unlist(c(width2.groupA,
width2.groupB)),
group = c(rep("groupA", length(width1.groupA)),
rep("groupB", length(width1.groupB)))
)
df$group <- factor(df$group,levels = c("groupA","groupB"))
narrower.groupA <- sum(width1.groupA<width2.groupA, na.rm = TRUE)/length(width1.groupA)
narrower.groupB <- sum(width1.groupB<width2.groupB, na.rm = TRUE)/length(width1.groupB)
narrower <- list("groupA" = narrower.groupA,
"groupB" = narrower.groupB)
return(list("data" = df,
"narrower" = narrower,
"median.width1.groupA" = median(width1.groupA),
"median.width1.groupB" = median(width1.groupB),
"median.width2.groupA" = median(width2.groupA),
"median.width2.groupB" = median(width2.groupB),
"median.diff.groupA" = median(width2.groupA-width1.groupA),
"median.diff.groupB" = median(width2.groupB-width1.groupB))
)
}
ggplotCIWidth <- function(df){
ggplot(df, aes(x = width1, y = width2, color=group)) +
geom_point(size = 0.5) +
geom_abline(slope=1, intercept=0)+
coord_cartesian(ylim = c(0,100), xlim=c(0,100)) +
# scale_y_continuous(breaks = seq(from = 0, to = 1, by = 0.25),
# limits = c(-5,1)) +
themePlot +
scale_color_manual(values=c("darkslategray3", "gray20", "gray"))
}
ggplotCIBA <- function(df){
ggplot(df, aes(x = width1, y = width2-width1, color=group)) +
geom_point(size = 0.1) +
geom_hline(aes(yintercept=0)) +
#geom_abline(slope=1, intercept=0)+
coord_cartesian(ylim = c(-20,80), xlim=c(0,80)) +
# scale_y_continuous(breaks = seq(from = 0, to = 1, by = 0.25),
# limits = c(-5,1)) +
themePlot +
scale_color_manual(values=c("darkslategray3", "gray20", "gray"))
}
## Ridgeline plots ----
ridgelineSummary <- function(results, truth, trueVE, title){
# Point estimates of VE, pmax, gamma, et50 normalized using the true values
# Point estimates of pmax.covariate, gamma.covariate, et50.covariate (without normalization)
val <- c(results$VE.all/trueVE,
results$PoD.param.pmax.covariateBinary,
results$PoD.param.pmax/truth$PoDModel$valueParams$param.pmax,
results$PoD.param.gamma.covariateBinary,
results$PoD.param.gamma/truth$PoDModel$valueParams$param.gamma,
results$PoD.param.et50.covariateBinary,
results$PoD.param.et50/truth$PoDModel$valueParams$param.et50)
name <- c(rep("VE", length(results$VE.all)),
rep("pmax_ageGroup", length(results$PoD.param.pmax.covariateBinary)),
rep("pmax", length(results$PoD.param.pmax)),
rep("gamma_ageGroup", length(results$PoD.param.gamma.covariateBinary)),
rep("gamma", length(results$PoD.param.gamma)),
rep("et50_ageGroup", length(results$PoD.param.et50.covariateBinary)),
rep("et50", length(results$PoD.param.et50)))
df <- data.frame(value = val,
variable = name)
truth.lines <- data.frame(variable = name,
x0 = c(1,
truth$PoDModel$valueParams$param.pmax.covariateBinary,
1,
truth$PoDModel$valueParams$param.gamma.covariateBinary,
1,
truth$PoDModel$valueParams$param.et50.covariateBinary,
1))
ggplot(df,
aes(x = value, y = variable, fill = stat(abs(1-x)))) +
geom_density_ridges_gradient(scale = 1.5, alpha=0.5, stat="binline", bins=200) +
scale_fill_viridis(name = "value") +
theme_ridges(grid = FALSE) +
theme(legend.position = "none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)) +
geom_vline(aes(xintercept=1)) +
labs(title = paste(title,': All (',length(results$VE.all),') simulated trials', sep = "")) +
geom_vline(aes(xintercept=truth$PoDModel$valueParams$param.pmax.covariateBinary),
linetype = "dashed",
colour = "gray") +
geom_vline(aes(xintercept=truth$PoDModel$valueParams$param.gamma.covariateBinary),
linetype = "dashed",
colour = "gray") +
geom_vline(aes(xintercept=truth$PoDModel$valueParams$param.et50.covariateBinary),
linetype = "dashed",
colour = "gray") +
xlim(0,2.5)+
annotate("text", x = 2.3, y = 1.5, label = paste("# trials = ",sum(!is.na(results$PoD.param.et50)),sep = ""))+
annotate("text", x = 2.3, y = 2.5, label = paste("# trials = ",sum(!is.na(results$PoD.param.et50.covariateBinary)),sep = ""))+
annotate("text", x = 2.3, y = 3.5, label = paste("# trials = ",sum(!is.na(results$PoD.param.gamma)),sep = ""))+
annotate("text", x = 2.3, y = 4.5, label = paste("# trials = ",sum(!is.na(results$PoD.param.gamma.covariateBinary)),sep = ""))+
annotate("text", x = 2.3, y = 5.5, label = paste("# trials = ",sum(!is.na(results$PoD.param.pmax)),sep = ""))+
annotate("text", x = 2.3, y = 6.5, label = paste("# trials = ",sum(!is.na(results$PoD.param.pmax.covariateBinary)),sep = ""))+
annotate("text", x = 2.3, y = 7.5, label = paste("# trials = ",sum(!is.na(results$VE.all)),sep = ""))
}
ridgelineOneSimulatedTrial <- function(id=1, results, truth, trueVE, seeds, title){
idDetail <- which(seeds == id)
VE <- results$VECI_raw[[idDetail]]$VECI_raw$all/trueVE
pmax.covariate <- results$PoDCI_raw[[idDetail]]$PoDCI_raw$results$param.pmax.covariateBinary
pmax <- results$PoDCI_raw[[idDetail]]$PoDCI_raw$results$param.pmax/truth$PoDModel$valueParams$param.pmax
gamma.covariate <- results$PoDCI_raw[[idDetail]]$PoDCI_raw$results$param.gamma.covariateBinary
gamma <- results$PoDCI_raw[[idDetail]]$PoDCI_raw$results$param.gamma/truth$PoDModel$valueParams$param.gamma
et50.covariate <- results$PoDCI_raw[[idDetail]]$PoDCI_raw$results$param.et50.covariateBinary
et50 <- results$PoDCI_raw[[idDetail]]$PoDCI_raw$results$param.et50/truth$PoDModel$valueParams$param.et50
val <- c(VE,
pmax.covariate,
pmax,
gamma.covariate,
gamma,
et50.covariate,
et50)
name <- c(rep("VE", length(VE)),
rep("pmax_ageGroup", length(pmax.covariate)),
rep("pmax", length(pmax)),
rep("gamma_ageGroup", length(gamma.covariate)),
rep("gamma", length(gamma)),
rep("et50_ageGroup", length(et50.covariate)),
rep("et50", length(et50)))
df <- data.frame(value = val,
variable = name)
ggplot(df,
aes(x = value, y = variable, fill = stat(abs(1-x)))) +
geom_density_ridges_gradient(scale = 1.5, alpha=0.5) +
scale_fill_viridis(name = "value", option = "B") +
theme_ridges(grid = FALSE) +
theme(legend.position = "none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)) +
geom_vline(aes(xintercept=1)) +
labs(title = paste(title,': One simulated trial', sep = "")) +
geom_vline(aes(xintercept=truth$PoDModel$valueParams$param.pmax.covariateBinary),
linetype = "dashed",
color = "gray") +
geom_vline(aes(xintercept=truth$PoDModel$valueParams$param.gamma.covariateBinary),
linetype = "dashed",
colour = "gray") +
geom_vline(aes(xintercept=truth$PoDModel$valueParams$param.et50.covariateBinary),
linetype = "dashed",
colour = "gray") +
xlim(0,2.5)