R/xgx_check_data.R

Defines functions xgx_check_data

Documented in xgx_check_data

#' Check data for various issues
#'
#' \code{xgx_check_data} performs a series of checks on a PK or PKPD dataset
#' It was inspired by the dataset preparation table from 
#' \href{https://iqrtools.intiquan.com/doc/book/analysis-dataset-preparation.html}{IntiQuan}.
#' 
#' The dataset must have the following columns
#' \itemize{
#'   \item ID      = unique subject identifier.  USUBJID is another option 
#'   if ID is not there
#'   \item EVID    = event ID: 1 for dose, 0 otherwise
#'   \item AMT     = value of the dose
#'   \item TIME    = time of the measurement
#'   \item DV      = dependent value (linear scale).  will check if LIDV or 
#'   LNDV are also there if DV is not
#'   \item YTYPE   = data measurement for LIDV.  will check if CMT is there, 
#'   if YTYPE is not
#' }
#'
#' The dataset may also have additional columns
#' \itemize{
#'   \item CENS = flag for censoring of the data because it's below the 
#'   limit of quantification (BLOQ)
#'   \item MDV  = missing dependent variable - will be counted and then 
#'   filtered out from the data check
#' }
#'
#' @param data, the dataset to check.  Must contain the above columns
#' @param covariates, the column names of covariates, to explore
#'
#' @return data.frame
#'
#' @examples
#' covariates <- c("WEIGHTB", "SEX")
#' check <- xgx_check_data(mad_missing_duplicates, covariates)
#' 
#' @importFrom dplyr rename
#' @importFrom dplyr select
#' @importFrom dplyr filter
#' @importFrom dplyr count
#' @importFrom tibble tibble
#' @importFrom magrittr "%>%"
#' @importFrom dplyr group_by
#' @importFrom dplyr summarise
#' @importFrom dplyr transmute
#' @importFrom dplyr mutate
#' @importFrom dplyr ungroup
#' @importFrom dplyr summarise_all
#' @importFrom stats setNames
#' @importFrom dplyr bind_rows
#' @importFrom pander panderOptions
#' @importFrom pander pander
#' @importFrom utils head
#' @export
xgx_check_data <- function(data, covariates = NULL) {
  # avoid CRAN note
  ID <-  EVID <- YTYPE <- MDV <- AMT <- DV <- TIME <- CENS <-
    Value <- tot <- ntot <- pct <- Data_Check_Issue <- n <- NULL
  
  # check for required column names in dataset
  if (!("YTYPE" %in% names(data)) && ("CMT" %in% names(data))) {
    warning("Setting YTYPE column equal to CMT\n")
    data$YTYPE <- data$CMT
  }
  if (!("ID" %in% names(data)) && ("USUBJID" %in% names(data))) {
    warning("Setting ID column equal to USUBJID\n")
    data$ID <- data$USUBJID
  }
  if (!("DV" %in% names(data))) {
    if ("LIDV" %in% names(data)) {
      warning("Setting DV column equal to LIDV\n")
      data$DV <- data$LIDV
    } else if ("LNDV" %in% names(data)) {
      warning("Setting DV column equal to LNDV\n")
      data$DV <- data$LNDV
    }
  }
  if (!("MDV" %in% names(data))) {
    if ("EVID" %in% names(data)) {
      data$MDV <- as.numeric(data$EVID != 0)
      warning("Setting MDV column equal to as.numeric(EVID!=0)\n")
    }
  }
  if (!("CENS" %in% names(data))) {
    warning("Setting CENS column equal to 0\n")
    data$CENS <- 0
  }
  
  required_names <- c("ID", "EVID", "AMT", "TIME", "DV", "YTYPE")
  missing_cols <- setdiff(required_names, names(data))
  if (length(missing_cols) > 0) {
    missing_text <- paste(missing_cols, collapse = ",")
    stop(paste0("These columns must be present in the dataset: ", missing_text))
  }
  
  # initialize output tibble
  check <- list()
  data_subset <- list()
  i <- 0 #index for table
  j <- 0 #index for list of data indices
  
  # number of patients
  num_patients <- length(unique(data$ID))
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "Patients",
    Description = "Number of Patients",
    YTYPE = "-",
    Statistic = paste0(num_patients),
    Value = num_patients)
  
  # number of patients with zero observations
  zero_obs <- data %>%
    dplyr::group_by(ID) %>%
    dplyr::filter(EVID == 0) %>%
    dplyr::count() %>%
    dplyr::filter(n == 0)
  num_zero_obs <- nrow(zero_obs)
  
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "MDV",
    Description = paste0("Number of patients with zero PK or PD observations"),
    YTYPE = "all",
    Statistic = paste0(num_zero_obs, " ", paste0(zero_obs$ID, collapse = ", ")),
    Value = num_zero_obs)
  
  # number of missing data points, to be filtered out from MDV
  if ("MDV" %in% names(data)) {
    mdv <- data %>%
      dplyr::group_by(YTYPE) %>%
      dplyr::summarise(n = sum(MDV == 1 & EVID == 0))
    num_mdv <- sum(mdv$n)
    
    if (num_mdv == 0) {
      i <- i + 1
      check[[i]] <- tibble::tibble(
        Category = "MDV",
        Description = paste0("Number of Missing Data Points (MDV==1 and EVID==0)"),
        YTYPE = "all",
        Statistic = "0",
        Value = 0)
    } else {
      i <- i + 1
      check[[i]] <- mdv %>%
        dplyr::transmute(
          Category = "MDV",
          Description = paste0("Number of Missing Data Points (MDV==1 and EVID==0)"),
          YTYPE = as.character(YTYPE),
          Statistic = paste0(n),
          Value = n)
      message(paste0("removing ", nrow(num_mdv),
                     " points with MDV==1 & EVID==0 from dataset"))
      data <- dplyr::filter(data, !(MDV == 1 & EVID == 0))
    }
  }
  
  # number of doses
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "Dose",
    Description = paste0("Number of non-zero doses"),
    YTYPE = "-",
    Value = sum(data$AMT > 0),
    Statistic = paste0(Value))
  
  # number of zero doses
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "Dose",
    Description = paste0("Number of zero doses (AMT==0)"),
    YTYPE = "-",
    Value = sum(data$AMT == 0 & data$EVID == 1),
    Statistic = paste0(Value))
  
  # number of patients that have all zero doses or that never receive any dose
  num_doses <- data %>%
    dplyr::group_by(ID) %>%
    dplyr::summarise(n = sum(AMT > 0))
  
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "Dose",
    Description = paste0("Number of patients that never received drug"),
    YTYPE = "-",
    Value = sum(num_doses$n == 0),
    Statistic = paste0(Value))
  
  # number of data points
  num_datapoints <- data %>%
    dplyr::group_by(ID, YTYPE) %>%
    dplyr::count() %>%
    dplyr::group_by(YTYPE) %>%
    dplyr::summarise(tot    = sum(n),
                     min    = min(n),
                     median = median(n),
                     max    = max(n))
  
  i <- i + 1
  check[[i]] <- num_datapoints %>%
    dplyr::transmute(
      Category = "DV",
      Description = paste0("Number of Data Points"),
      YTYPE = as.character(YTYPE),
      Statistic = paste0(tot),
      Value = tot)
  
  i <- i + 1
  check[[i]] <- num_datapoints %>%
    dplyr::transmute(
      Category = "DV",
      Description = paste0("Number of Data Points per Individual"),
      YTYPE = as.character(YTYPE),
      Statistic = paste0("min = ", min, ",  median = ", median,
                         ", max = ", max),
      Value = median)
  
  # check for zero concentrations
  num_zero_datapoints <- data %>%
    dplyr::group_by(ID, YTYPE) %>%
    dplyr::group_by(YTYPE) %>%
    dplyr::summarise(tot = sum(DV == 0 & MDV == 0, na.rm = TRUE))
  
  i <- i + 1
  check[[i]] <- num_zero_datapoints %>%
    dplyr::transmute(
      Category = "DV",
      Description = paste0("Number of Data Points with zero value (DV==0)"),
      YTYPE = as.character(YTYPE),
      Statistic = paste0(tot),
      Value = tot)
  
  j <- j + 1
  data_subset[[j]] <- data %>%
    dplyr::filter(DV == 0 & MDV == 0) %>%
    dplyr::mutate(Data_Check_Issue = "DV == 0")
  
  # check for missing data
  num_na_datapoints <- data %>%
    dplyr::group_by(ID, YTYPE) %>%
    dplyr::group_by(YTYPE) %>%
    dplyr::summarise(tot = sum(is.na(DV) & MDV == 0))
  
  i <- i + 1
  check[[i]] <- num_na_datapoints %>%
    dplyr::transmute(
      Category = "DV",
      Description = paste0("Number of Data Points with NA (is.na(DV))"),
      YTYPE = as.character(YTYPE),
      Statistic = paste0(tot),
      Value = tot)
  
  j <- j + 1
  data_subset[[j]] <- data %>%
    dplyr::filter(is.na(DV) & MDV == 0) %>%
    dplyr::mutate(Data_Check_Issue = "is.na(DV)")
  
  # check for duplicate data
  dup_time <- data %>%
    dplyr::group_by(ID, YTYPE, TIME) %>%
    dplyr::mutate(n = length(DV),
                  n = ifelse(n == 1, 0, n)) %>%
    dplyr::ungroup()
  
  i <- i + 1
  check[[i]] <- dup_time %>%
    dplyr::group_by(YTYPE) %>%
    dplyr::summarise(ntot = sum(n)) %>%
    dplyr::ungroup() %>%
    dplyr::transmute(
      Category = "DV+TIME",
      Description = "Multiple measurements at same time",
      YTYPE = as.character(YTYPE),
      Statistic = paste0(ntot),
      Value = ntot)
  
  j <- j + 1
  dup_time <- dup_time %>%
    dplyr::filter(n >= 2)
  data_subset[[j]] <- data %>%
    dplyr::filter(ID %in% dup_time$ID,
                  TIME %in% dup_time$TIME,
                  YTYPE %in% dup_time$YTYPE) %>%
    dplyr::mutate(Data_Check_Issue = "Duplicate Time Points")
  
  # number of Censored data points
  if ("CENS" %in% names(data)) {
    num_cens <- data %>%
      dplyr::group_by(YTYPE) %>%
      dplyr::summarise(tot  = sum(CENS == 1))
    num_cens$pct <- round(num_cens$tot / num_datapoints$tot * 100)
    
    i <- i + 1
    check[[i]] <- num_cens %>%
      dplyr::transmute(
        Category = "CENS",
        Description = paste0("Number of Censored Data Points"),
        YTYPE = as.character(YTYPE),
        Statistic = paste0(tot, " (", pct, "%)"),
        Value = tot)
  }
  
  # columns with negative data
  neg <- data %>%
    ungroup() %>%
    dplyr::select(DV, covariates) %>%
    dplyr::select_if(is.numeric) %>%
    dplyr::summarise_all(function(x) {sum( x < 0, na.rm = TRUE)})
  nam <- names(neg)
  neg <- neg %>%
    as.numeric() %>%
    stats::setNames(nam)
  neg <- neg[neg > 0]
  
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "All Columns",
    Description = "Negative Values (number)",
    YTYPE = "-",
    Statistic = paste0(names(neg), ":", neg, collapse = ", "),
    Value = sum(neg))
  
  # columns with missing values
  na <- data %>%
    ungroup() %>%
    dplyr::summarise_all(function(x) {sum(is.na(x))}) %>%
    as.numeric() %>%
    stats::setNames(names(data))
  na <- na[na > 0]
  
  i <- i + 1
  check[[i]] <- tibble::tibble(
    Category = "All Columns",
    Description = "Missing Values (number)",
    YTYPE = "-",
    Statistic = paste0(names(na), ":", na, collapse = ", "),
    Value = sum(na))
  missing_summary <- check[[i]]$Statistic
  
  # create summaries
  check <- dplyr::bind_rows(check)
  data_subset <- dplyr::bind_rows(data_subset) %>%
    dplyr::select(Data_Check_Issue, ID, TIME, DV, CENS, YTYPE)
  
  # covariates
  cov_summary <- xgx_summarize_covariates(data, covariates)
  
  # output
  output <- list(summary = check,
                 cts_covariates = cov_summary$cts_covariates,
                 cat_covariates = cov_summary$cat_covariates,
                 data_subset = data_subset)
  
  # print the summary
  pander::panderOptions("table.split.table", Inf)
  pander::panderOptions("table.split.cells", 60)
  pander::panderOptions("table.alignment.default", "left")
  
  cat("\nDATA SUMMARY\n")
  pander::pander(check %>% dplyr::select(-Value))
  
  if (length(output$cts_covariates) > 0) {
    cat("CONTINUOUS COVARIATES\n")
    pander::pander(output$cts_covariates)
  } else {
    cat("NO CONTINUOUS COVARIATES\n")
  }
  
  if (length(output$cat_covariates) > 0) {
    cat("CATEGORICAL COVARIATES\n")
    pander::panderOptions("table.split.cells", 100)
    pander::pander(output$cat_covariates)
  } else {
    cat("NO CATEGORICAL COVARIATES\n")
  }
  
  if (nrow(data_subset) == 0) {
  } else if (nrow(data_subset) <= 6) {
    cat("POSSIBLE DATA ISSUES IN THE FOLLOWING RECORDS\n")
    pander::pander(data_subset)
  } else {
    cat("POSSIBLE DATA ISSUES - FIRST 6 RECORDS\n")
    pander::pander(utils::head(data_subset))
  }
  cat("The following columns contained missing values\n")
  cat(missing_summary)
  
  return(output)
}

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xgxr documentation built on April 23, 2021, 1:07 a.m.