cchs: Cox model for case-cohort data with stratified...

Description Usage Arguments Details Value References See Also Examples

View source: R/cchs.R


cchs fits a Cox proportional-hazards regression model to case-cohort data where the subcohort was selected by stratified simple random sampling. It uses Estimator III of Borgan et al (2000).


	cchs(formula, data=sys.parent(), inSubcohort, stratum, 
		samplingFractions, cohortStratumSizes, precision=NULL, 
		returnAdjustedTimes=FALSE, swap=TRUE, dropNeverAtRiskRows=TRUE, 
		dropSubcohEventsDfbeta=FALSE, adjustSampFracIfAnyNAs=FALSE, 
		keepAllCoxphElements=FALSE, confidenceLevel=0.95, verbose=FALSE, 
		annotateErrors=TRUE, coxphControl, ...)



An object of class formula that specifies the terms in the model. The left-hand side must be a Surv object. The special terms cluster and strata are not allowed.


A data-frame or environment that contains the variables used in the formula. The variables named in inSubcohort, stratum, samplingFractions, and cohortStratumSizes will be looked for first in data, if that is a data-frame, and then in the environment that cchs was called from.


A vector of logical variables that shows whether each observation/row is in the subcohort (TRUE) or not (FALSE).


A vector that defines the strata within which the subcohort was selected. Each element of stratum corresponds to one observation/row in the data. The elements can be character strings, integers, or any other type of variable that can be converted to a factor.

samplingFractions, cohortStratumSizes

samplingFractions is a vector of the sampling fractions in the different strata, and cohortStratumSizes is a vector of the sizes of the strata in the full cohort. Exactly one of these must be given. There are two possible forms for the vector: if it has names, then these must all be distinct and include the names of the strata (and if one value of stratum is "France", then samplingFraction["France"] should be the sampling fraction for that stratum); if it does not have names, then it must have one element for each observation/row in the data.


For example, if the times were recorded to the nearest day but are stored as numbers of years, then precision should be 1/365.25. If there are no tied event-times, then it makes no difference what precision is. If there are tied event-times and precision is a number, then the tied event-times will be slightly changed before the estimator is calculated. If there are tied event-times and precision is NULL (meaning unspecified), then the estimator cannot be calculated and an error will be thrown.


If this is TRUE, the object returned by cchs will contain the exit-times after they have been adjusted to deal with any tied event-times. If a row is dropped because of missing data (NAs) then its exit-time is not adjusted.


If this is FALSE then the swapping will be omitted (in the formula for Estimator III in Borgan et al 2000, the randomly selected observation/row will not be removed). This is only intended to be used for testing or development.


If this is TRUE, observations/rows whose at-risk periods do not include any of the event-times will be dropped just before cchs internally calls coxph. These observations/rows make no difference to the regression coefficients produced by coxph, but they do affect the dfbeta residuals (see Langholz & Jiao 2007) and therefore the variance-estimates, because coxph calculates the dfbeta residuals using an approximation.


If this is FALSE, which is the default, the dfbeta residuals and therefore the variance-estimates will be calculated exactly as described by Borgan et al (2000). If it is TRUE, they will be calculated as described by Langholz & Jiao (2007) (see “There is a slight approximation ...” in section 2.4).


If this is TRUE, and if any observations are dropped because of missing data (NAs), then the sampling fractions will be recalculated using the numbers of observations after those observations are dropped.


If this is TRUE, then the object returned by cchs will contain elements such as loglik and linear.predictors from the object that was produced by cchs's internal call to coxph. These are not likely to be relevant or correct, since cchs manipulates and changes the dataset in many ways before passing it to coxph. (For a list of the elements produced by coxph, see coxph.object.)


The level for the hazard-ratio confidence intervals (a number in the interval [0,1]).


If this is TRUE, detailed information about the internal manipulations and calculations will be displayed.


If this is TRUE, and if certain functions that are called internally by cchs produce errors or warnings, then extra messages will be added to make those easier to understand. The disadvantage of this is that the call stack produced by traceback is more complicated.

coxphControl, ...

These are optional arguments to control the working of coxph when it is called internally by cchs. If coxphControl is supplied then it must be a list produced by coxph.control, and if “...” arguments are supplied then it must be possible to pass them to coxph.control.


In a case–cohort study, the dataset consists only of the cases (the participants who have an event) and the participants who are in the subcohort, which is a randomly selected subset of the cohort. In a stratified case–cohort study, the subcohort is selected by stratified simple random sampling. This means that the cohort is divided into strata, and from each stratum a proportion of the participants equal to that stratum's sampling fraction is selected to be in the subcohort (and within each stratum, each participant is selected with equal probability). For more on stratified case–cohort studies see any of the references listed below.

cchs fits a Cox proportional-hazards regression model to data from a stratified case–cohort study, using the time-fixed version of Estimator III from Borgan et al (2000). Estimators I and II from Borgan et al (2000) are available by using cch with the options method="I.Borgan" and method="II.Borgan", but only Estimator III is score-unbiased, which is the main desirable criterion. The data must be in the usual form where each row corresponds to one observation (that is, one participant). cchs works by manipulating the data in various ways, then passing it to coxph (which is suitable for fitting a Cox model to data from a cohort study), and finally making corrections to the variance-estimates. It is planned that a vignette will be produced and this will contain more detail.

For normal use, the logical (boolean) arguments should have their default values. cchs performs a complete-case analysis, meaning that rows will be dropped if they contain NAs in any of the variables that appear in the model, including inside the Surv(), or in inSubcohort or stratum. NAs are not allowed in samplingFractions or cohortStratumSizes, unless that vector has names and any of those names are not equal to values of stratum, in which case the corresponding elements can be NA.

cchs does not normally give replicable results, because the swapping and the small changes to tied event-times are random (see swap and precision in the Arguments section). To get exactly the same results every time, use set.seed with a fixed seed just before calling cchs.


An S3 object of class cchs. This is a list that contains the following elements:


The vector of coefficients.


The variance matrix of the coefficients.


A vector of two elements: the first is the log-likelihood with the initial values of the coefficients that were used in the iteration to find the maximum likelihood, and the second is the maximized log-likelihood—that is, the log-likelihood with the final values of the coefficients. (Strictly speaking these should all say “pseudo-likelihood” instead of “likelihood”.)


The number of iterations used by coxph.


The number of observations (that is, rows), that were used in the call to coxph.


The number of events (also called failures).


The call that was used to create the cchs object (an object of mode call).


A summary of the main output. This is a matrix that contains the hazard ratios, confidence intervals for them, p-values for the Wald tests, log hazard ratios (which are the coefficients in the Cox model), and standard errors of the log hazard ratios.


The level for the confidence intervals in coeffsTable. (This is a copy of the confidenceLevel argument.)


A vector with three elements: the numbers of subcohort non-cases, subcohort cases, and non-subcohort cases. The sum of these is n.


The number of strata that appear in the data.


A message about observations that have been dropped because of NAs and event-times that have been changed to deal with ties, if either of these happened.

If keepAllCoxphElements is TRUE, then the cchs object will also contain the other elements listed under coxph.object. If returnAdjustedTimes is TRUE, then it will contain an adjustedTimes element, which is a vector of the adjusted exit-times (with elements in the same order as the observations/rows in the data).


Borgan, Ø., Langholz, B., Samuelsen S.O., Goldstein, L., Pogoda, J. (2000). Exposure stratified case–cohort designs. Lifetime Data Analysis 6 (1), 39–58. link

Cologne, J., Preston, D.L., Imai, K., Misumi, M., Yoshida, K., Hayashi, T., Nakachi, K. (2012). Conventional case–cohort design and analysis for studies of interaction. International Journal of Epidemiology 41 (4), 1174–1186. link

Langholz, B., Jiao, J. (2007). Computational methods for case–cohort studies. Computational Statistics and Data Analysis 51 (8), 3737–3748. link

See Also

cch, which can calculate Estimators I and II from Borgan et al (2000), coxph, which cchs uses internally, and coxph.control, a container for certain parameters that are passed to coxph. These are all in the survival package.

cchsData, an example dataset that cchs can be used on.


# Analyze the relation between survival and three covariates in cchsData. 
# The times are stored as numbers of days, so precision has to be 1. The 
# selection of the subcohort was stratified according to two strata, defined 
# by cchsData$localHistol, and the sampling fractions are stored in 
# cchsData$sampFrac. 

cchs(Surv(time, isCase) ~ stage + centralLabHistol + ageAtDiagnosis, 
      data=cchsData, inSubcohort=inSubcohort, stratum=localHistol, 
      samplingFractions=sampFrac, precision=1) 

# Do the same analysis using cohortStratumSizes instead of samplingFractions.
# For the value of cohortStratumSizes see the Details section of ?cchsData. 
# These two calls to cchs will give slightly different results unless set.seed  
# is used with the same seed just before both of them.

cchs(Surv(time, isCase) ~ stage + centralLabHistol + ageAtDiagnosis, 
      data=cchsData, inSubcohort=inSubcohort, stratum=localHistol, 
      cohortStratumSizes=c(favorable=3622, unfavorable=406), precision=1) 

cchs documentation built on Aug. 26, 2017, 5:03 p.m.

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