cchsData {cchs}

Data from a case–cohort study with stratified subcohort-selection

Description

A case–cohort dataset where the subcohort was selected by stratified simple random sampling. This is an artificial dataset that was made from nwtco, a real dataset from the National Wilms Tumor Study (NWTS). It is designed for demonstrating the use of cchs.

Format

id	An ID number.
localHistol	Result of the histology from the local institution.
centralLabHistol	Result of the histology from the central laboratory.
stage	Stage of the cancer (I, II, III, or IV).
study	The study (NWTS-3 or NWTS-4). For details see this NWTS webpage (archived copy).
isCase	Indicator for whether this participant had a relapse or not.
time	Number of days from diagnosis of Wilms tumor to relapse or censoring.
ageAtDiagnosis	Age in years at diagnosis of Wilms tumor.
inSubcohort	Indicator for whether this participant is in the subcohort or not.
sampFrac	The sampling fraction for the stratum that contains this participant.

Details

The nwtco data is from two clinical trials but can be regarded as cohort data. cchsData can be created from it by running the code in the Source section below, which is partly based on the Examples section of the cch documentation.

Two strata are used for the subcohort-selection, corresponding to the two values of localHistol. The sampling fraction is 5% for the stratum defined by localHistol="favorable" and 20% for the stratum defined by localHistol="unfavorable". After the subcohort is selected, the sampling fractions are recalculated using the exact integer numbers of participants in the subcohort and the full cohort, and then stored in the data-frame.

As an alternative to the sampling fractions, the stratum sizes in the full cohort could be used. A suitable value for the cohortStratumSizes argument to cchs would be c(favorable=3622, unfavorable=406). This can be worked out by entering table(nwtco$instit, useNA="always") and noting that for nwtco$instit and nwtco$histol, a value of 1 means “favorable histology result” and 2 means “unfavorable”—this is not stated in the nwtco documentation but can be deduced from the line in the cch examples that contains labels=c("FH","UH"), or by comparing the output of the table command with the numbers in Table 1 of Breslow & Chatterjee (1999).

For information about the two clinical trials, NWTS-3 and NWTS-4, see D'Angio et al. (1989) and Green et al. (1998) respectively, or the National Wilms Tumor Study website (archived copy).

Source

# Starting with nwtco, rename variables, convert some to factors, drop  
# in.subcohort (which is used elsewhere for a different simulated dataset), etc. 
library(survival, quietly=TRUE)
cchsData <- data.frame(
   id = nwtco$seqno, 
   localHistol = factor(nwtco$instit, labels=c("favorable", "unfavorable")), 
   centralLabHistol = factor(nwtco$histol, labels=c("favorable", "unfavorable")), 
   stage = factor(nwtco$stage, labels=c("I", "II", "III", "IV")), 
   study = factor(nwtco$study, labels=c("NWTS-3", "NWTS-4")),
   isCase = as.logical(nwtco$rel), 
   time = nwtco$edrel,
   ageAtDiagnosis = nwtco$age / 12  # nwtco$age is in months
)

# Define the intended sampling fractions for the two strata. 
samplingFractions <- c(favorable=0.05, unfavorable=0.2)

# Select participants/rows to be in the subcohort by stratified simple random 
# sampling. 
cchsData$inSubcohort <- rep(FALSE, nrow(cchsData))
set.seed(1)
for (stratumName in levels(cchsData$localHistol)) {
   inThisStratum <- cchsData$localHistol == stratumName
   stratumSubcohortSize <- 
         round(samplingFractions[stratumName] * sum(inThisStratum))
   rowsToSetTrue <- sample(which(inThisStratum), size=stratumSubcohortSize)
   cchsData$inSubcohort[rowsToSetTrue] <- TRUE
}

# Change the sampling fractions to their exact values. 
stratumSubcohortSizes <- table(cchsData$localHistol[cchsData$inSubcohort])
stratumCohortSizes <- table(cchsData$localHistol)
samplingFractions <- stratumSubcohortSizes / stratumCohortSizes
samplingFractions <- c(samplingFractions)  # make it a vector, not a table

# Keep only the cases and the subcohort. 
cchsData <- cchsData[cchsData$isCase | cchsData$inSubcohort,]

# Put the sampling fraction in each row of the data-frame. 
cchsData$sampFrac <- 
      samplingFractions[match(cchsData$localHistol, names(samplingFractions))]

References

Note: doi links are shown where these pass CRAN checks and appear correctly in the PDF reference manual. In other cases, URLs are shown.

Breslow, N.E., Chatterjee, N. (1999). Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. Journal of the Royal Statistical Society: Series C (Applied Statistics) 48 (4), 457–468. https://doi.org/10.1111/1467-9876.00165

D'Angio, G.J., Breslow, N., Beckwith, J.B., Evans, A., Baum, E., Delorimier, A., Fernbach, D., Hrabovsky, E., Jones, B., Kelalis, P., Othersen, H.B., Tefft, M., Thomas, P.R.M. (1989). Treatment of Wilms' tumor: Results of the third National Wilms' Tumor Study. Cancer 64 (2), 349–360. https://doi.org/bc95fv

Green, D.M., Breslow, N.E., Beckwith, J.B., Finklestein, J.Z., Grundy, P.E., Thomas, P.R., Kim, T., Shochat, S.J., Haase, G.M., Ritchey, M.L., Kelalis, P.P., D'Angio, G.J. (1998). Comparison between single-dose and divided-dose administration of dactinomycin and doxorubicin for patients with Wilms' tumor: a report from the National Wilms' Tumor Study Group. Journal of Clinical Oncology 16 (1), 237–245. doi: 10.1200/JCO.1998.16.1.237