cch {survival}  R Documentation 
Returns estimates and standard errors from relative risk regression fit to data from casecohort studies. A choice is available among the Prentice, SelfPrentice and LinYing methods for estimation of regression coefficients and standard errors.
cch(formula, data = sys.parent(), subcoh, id, cohort.size, method = c("Prentice", "SelfPrentice", "LinYing"))
formula 
A formula object that must have a Surv object as the response.
The Surv object must be of type "right" , or of type "counting" .

subcoh 
Vector of indicatorsfor subjects sampled as part of the
subcohort. Code 1 or TRUE for members of the
subcohort, 0 or FALSE for others. If data is a
data frame then subcoh may be a onesided formula.

id 
Vector of unique identifiers, or formula specifying such a vector. If data is a
data frame then subcoh may be a onesided formula.

cohort.size 
Scalar with size of original cohort from which subcohort was sampled 
data 
An optional data frame in which to interpret the variables occurring in the formula. 
method 
Three procedures are available. The default method is "Prentice", with options for "SelfPrentice" or "LinYing". 
Implements methods for casecohort data analysis described by Therneau and Li (1999). The three methods differ in the choice of "risk sets" used to compare the covariate values of the failure with those of others at risk at the time of failure. "Prentice" uses the subcohort members "at risk" plus the failure if that occurs outside the subcohort and is score unbiased. "SelfPren" (SelfPrentice) uses just the subcohort members "at risk". These two have the same asymptotic variancecovariance matrix. "LinYing" (LinYing) uses the all members of the subcohort and all failures outside the subcohort who are "at risk". The methods also differ in the weights given to different score contributions.
A whole bunch of stuff including list of estimated regression coefficients and two estimates of their asymptotic variancecovariance matrix.
coef 
regression coefficients.

naive.var 
SelfPrentice model based variancecovariance matrix.

var 
LinYing empirical variancecovariance matrix.

Norman Breslow, modified by Thomas Lumley
Prentice, RL (1986). A casecohort design for epidemiologic cohort studies and disease prevention trials. Biometrika 73: 1–11.
Self, S and Prentice, RL (1988). Asymptotic distribution theory and efficiency results for casecohort studies. Annals of Statistics 16: 64–81.
Lin, DY and Wei, LJ (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association 84: 1074–1078.
Lin, DY and Ying, Z (1993). Cox regression with incomplete covariate measurements. Journal of the American Statistical Association 88: 1341–1349.
Barlow, WE (1994). Robust variance estimation for the casecohort design. Biometrics 50: 1064–1072
Therneau, TM and Li, H (1999). Computing the Cox model for casecohort designs. Lifetime Data Analysis 5: 99–112.
Borgan, O et al. (1999). Exposure stratified casecohort designs. Lifetime Data Analysis
## The complete Wilms Tumor Data ## (Breslow and Chatterjee, Applied Statistics, 1999) ## subcohort selected by simple random sampling. ## data(nwtco) subcoh < nwtco$in.subcohort selccoh < with(nwtco, rel==1subcoh==1) ccoh.data < nwtco[selccoh,] ccoh.data$subcohort < subcoh[selccoh] ccoh.data$histol < factor(ccoh.data$histol,labels=c("FH","UH")) # Central histology ccoh.data$stage < factor(ccoh.data$stage,labels=c("I","II","III","IV")) # Stage ccoh.data$age < ccoh.data$age/12 # Age in years ## ## Standard casecohort analysis: simple random subcohort ## fit.ccP < cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028) fit.ccP fit.ccSP < cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="SelfPren") summary(fit.ccSP)