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EHR-based electronic frailty score (10.1093/ageing/afx001) #173

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3f16179
EHR-based electronic frailty score (10.1093/ageing/afx001)
cyanover Jul 28, 2022
4dd2d77
Bug fix in poly-pharmacy deficit
cyanover Jan 4, 2023
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53 changes: 22 additions & 31 deletions R/Aggregation.R
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright 2021 Observational Health Data Sciences and Informatics
# Copyright 2022 Observational Health Data Sciences and Informatics
#
# This file is part of FeatureExtraction
#
Expand Down Expand Up @@ -27,7 +27,7 @@
aggregateCovariates <- function(covariateData) {
if (!isCovariateData(covariateData))
stop("Data not of class CovariateData")
if (!Andromeda::isValidAndromeda(covariateData))
if (!Andromeda::isValidAndromeda(covariateData))
stop("CovariateData object is closed")
if (isAggregatedCovariateData(covariateData))
stop("Data appears to already be aggregated")
Expand All @@ -39,17 +39,18 @@ aggregateCovariates <- function(covariateData) {
attr(result, "metaData") <- attr(covariateData, "metaData")
class(result) <- "CovariateData"
attr(class(result), "package") <- "FeatureExtraction"
populationSize <- attr(covariateData, "metaData")$populationSize
populationSize <- attr(covariateData, "metaData")$populationSize

# Aggregate binary variables
result$covariates <- covariateData$analysisRef %>%
filter(rlang::sym("isBinary") == "Y") %>%
inner_join(covariateData$covariateRef, by = "analysisId") %>%
inner_join(covariateData$covariates, by = "covariateId") %>%
group_by(rlang::sym("covariateId")) %>%
summarize(sumValue = sum(rlang::sym("covariateValue"), na.rm = TRUE),
averageValue = sum(rlang::sym("covariateValue") / populationSize, na.rm = TRUE))

averageValue = sum(rlang::sym("covariateValue")/populationSize,
na.rm = TRUE))

# Aggregate continuous variables where missing means zero
computeStats <- function(data) {
zeroFraction <- 1 - (nrow(data)/populationSize)
Expand All @@ -61,47 +62,37 @@ aggregateCovariates <- function(covariateData) {
result <- tibble(covariateId = data$covariateId[1],
countValue = nrow(data),
minValue = quants[1],
maxValue = quants[7],
averageValue = mean(data$covariateValue) * (1 - zeroFraction),
standardDeviation = sqrt((populationSize * sum(data$covariateValue^2) - sum(data$covariateValue)^2)/(populationSize * (populationSize - 1))),
medianValue = quants[4],
p10Value = quants[2],
p25Value = quants[3],
p75Value = quants[5],
p90Value = quants[6])

maxValue = quants[7], averageValue = mean(data$covariateValue) * (1 - zeroFraction), standardDeviation = sqrt((populationSize *
sum(data$covariateValue^2) - sum(data$covariateValue)^2)/(populationSize * (populationSize -
1))), medianValue = quants[4], p10Value = quants[2], p25Value = quants[3], p75Value = quants[5],
p90Value = quants[6])
}

covariatesContinuous1 <- covariateData$analysisRef %>%
filter(rlang::sym("isBinary") == "N" & rlang::sym("missingMeansZero") == "Y") %>%
inner_join(covariateData$covariateRef, by = "analysisId") %>%
inner_join(covariateData$covariates, by = "covariateId") %>%
Andromeda::groupApply("covariateId", computeStats) %>%
Andromeda::groupApply("covariateId", computeStats) %>%
bind_rows()

# Aggregate continuous variables where missing means missing
computeStats <- function(data) {
probs <- c(0, 0.1, 0.25, 0.5, 0.75, 0.9, 1)
quants <- quantile(data$covariateValue, probs = probs, type = 1)
result <- tibble(covariateId = data$covariateId[1],
countValue = length(data$covariateValue),
minValue = quants[1],
maxValue = quants[7],
averageValue = mean(data$covariateValue),
standardDeviation = sd(data$covariateValue),
medianValue = quants[4],
p10Value = quants[2],
p25Value = quants[3],
p75Value = quants[5],
p90Value = quants[6])
result <- tibble(covariateId = data$covariateId[1], countValue = length(data$covariateValue),
minValue = quants[1], maxValue = quants[7], averageValue = mean(data$covariateValue), standardDeviation = sd(data$covariateValue),
medianValue = quants[4], p10Value = quants[2], p25Value = quants[3], p75Value = quants[5],
p90Value = quants[6])
}

covariatesContinuous2 <- covariateData$analysisRef %>%
filter(rlang::sym("isBinary") == "N" & rlang::sym("missingMeansZero") == "N") %>%
inner_join(covariateData$covariateRef, by = "analysisId") %>%
inner_join(covariateData$covariates, by = "covariateId") %>%
Andromeda::groupApply("covariateId", computeStats) %>%
Andromeda::groupApply("covariateId", computeStats) %>%
bind_rows()

covariatesContinuous <- bind_rows(covariatesContinuous1, covariatesContinuous2)
if (nrow(covariatesContinuous) > 0) {
result$covariatesContinuous <- covariatesContinuous
Expand Down
51 changes: 27 additions & 24 deletions R/CompareCohorts.R
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright 2021 Observational Health Data Sciences and Informatics
# Copyright 2022 Observational Health Data Sciences and Informatics
#
# This file is part of FeatureExtraction
#
Expand Down Expand Up @@ -32,7 +32,10 @@
#' of mean.
#'
#' @export
computeStandardizedDifference <- function(covariateData1, covariateData2, cohortId1 = NULL, cohortId2 = NULL) {
computeStandardizedDifference <- function(covariateData1,
covariateData2,
cohortId1 = NULL,
cohortId2 = NULL) {
if (!isCovariateData(covariateData1))
stop("covariateData1 is not of type 'covariateData'")
if (!isCovariateData(covariateData1))
Expand All @@ -48,21 +51,19 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
covariates1 <- covariates1 %>%
filter(.data$cohortDefinitionId == cohortId1)
}
covariates1 <- covariates1 %>%
select(covariateId = "covariateId",
count1 = "sumValue") %>%
covariates1 <- covariates1 %>%
select(covariateId = "covariateId", count1 = "sumValue") %>%
collect()

covariates2 <- covariateData2$covariates
if (!is.null(cohortId2)) {
covariates2 <- covariates2 %>%
filter(.data$cohortDefinitionId == cohortId2)
}
covariates2 <- covariates2 %>%
select(covariateId = "covariateId",
count2 = "sumValue") %>%
covariates2 <- covariates2 %>%
select(covariateId = "covariateId", count2 = "sumValue") %>%
collect()

n1 <- attr(covariateData1, "metaData")$populationSize
if (!is.null(cohortId1)) {
n1 <- n1[as.character(cohortId1)]
Expand All @@ -78,9 +79,11 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
m$mean2 <- m$count2/n2
m$sd1 <- sqrt(m$mean1 * (1 - m$mean1))
m$sd2 <- sqrt(m$mean2 * (1 - m$mean2))
m$sd <- sqrt((m$sd1^2 + m$sd2^2) / 2)
m$sd <- sqrt((m$sd1^2 + m$sd2^2)/2)
m$stdDiff <- (m$mean2 - m$mean1)/m$sd
result <- bind_rows(result, m[, c("covariateId", "mean1", "sd1", "mean2", "sd2", "sd", "stdDiff")])
result <- bind_rows(result,
m[,
c("covariateId", "mean1", "sd1", "mean2", "sd2", "sd", "stdDiff")])
}
if (!is.null(covariateData1$covariatesContinuous) && !is.null(covariateData2$covariatesContinuous)) {
covariates1 <- covariateData1$covariatesContinuous
Expand All @@ -89,39 +92,39 @@ computeStandardizedDifference <- function(covariateData1, covariateData2, cohort
filter(.data$cohortDefinitionId == cohortId1)
}
covariates1 <- covariates1 %>%
select(covariateId = "covariateId",
mean1 = "averageValue",
sd1 = "standardDeviation") %>%
select(covariateId = "covariateId", mean1 = "averageValue", sd1 = "standardDeviation") %>%
collect()

covariates2 <- covariateData2$covariatesContinuous
if (!is.null(cohortId2)) {
covariates2 <- covariates2 %>%
filter(.data$cohortDefinitionId == cohortId2)
}
covariates2 <- covariates2 %>%
select(covariateId = "covariateId",
mean2 = "averageValue",
sd2 = "standardDeviation") %>%
select(covariateId = "covariateId", mean2 = "averageValue", sd2 = "standardDeviation") %>%
collect()

m <- merge(covariates1, covariates2, all = T)
m$mean1[is.na(m$mean1)] <- 0
m$sd1[is.na(m$sd1)] <- 0
m$mean2[is.na(m$mean2)] <- 0
m$sd2[is.na(m$sd2)] <- 0
m$sd <- sqrt(m$sd1^2 + m$sd2^2)
m$stdDiff <- (m$mean2 - m$mean1)/m$sd
result <- bind_rows(result, m[, c("covariateId", "mean1", "sd1", "mean2", "sd2", "sd", "stdDiff")])
result <- bind_rows(result,
m[,
c("covariateId", "mean1", "sd1", "mean2", "sd2", "sd", "stdDiff")])
}
covariateRef1 <- covariateData1$covariateRef %>%
collect()
covariateRef2 <- covariateData2$covariateRef %>%
collect()

result <- result %>%
left_join(select(covariateRef1, covariateId = "covariateId", covariateName1 = "covariateName"), by = "covariateId") %>%
left_join(select(covariateRef2, covariateId = "covariateId", covariateName2 = "covariateName"), by = "covariateId") %>%
left_join(select(covariateRef1, covariateId = "covariateId", covariateName1 = "covariateName"),
by = "covariateId") %>%
left_join(select(covariateRef2, covariateId = "covariateId", covariateName2 = "covariateName"),
by = "covariateId") %>%
mutate(covariateName = case_when(is.na(covariateName1) ~ covariateName2,
TRUE ~ covariateName1)) %>%
select(-rlang::sym("covariateName1"), -rlang::sym("covariateName2")) %>%
Expand Down
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