cch                 package:survival                 R Documentation

_F_i_t_s _p_r_o_p_o_r_t_i_o_n_a_l _h_a_z_a_r_d_s _r_e_g_r_e_s_s_i_o_n _m_o_d_e_l _t_o _c_a_s_e-_c_o_h_o_r_t _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Returns estimates and standard errors from relative risk
     regression fit to data from case-cohort studies. A choice is
     available among the Prentice, Self-Prentice and Lin-Ying methods
     for estimation of regression coefficients  and standard errors.

_U_s_a_g_e:

     cch(formula, data = sys.parent(), subcoh, id, cohort.size,
         method = c("Prentice", "SelfPrentice", "LinYing"))

_A_r_g_u_m_e_n_t_s:

 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
          sub-cohort. Code '1' or 'TRUE' for members of the sub-cohort,
          '0' or 'FALSE' for others. If 'data' is a data frame then
          'subcoh' may be a one-sided formula. 

      id: Vector of unique identifiers, or formula specifying such a
          vector. If 'data' is a data frame then 'subcoh' may be a
          one-sided 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".  

_D_e_t_a_i_l_s:

     Implements methods for case-cohort 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 sub-cohort members "at risk" plus the failure if that
     occurs outside the sub-cohort and is score unbiased. "SelfPren"
     (Self-Prentice) uses just the sub-cohort members "at risk". These
     two have the same asymptotic variance-covariance matrix. "LinYing"
     (Lin-Ying) uses the all members of the sub-cohort and all failures
     outside the sub-cohort who are "at risk". The methods also differ
     in the weights given to different score contributions.

_V_a_l_u_e:

     A whole bunch of stuff including list of estimated regression
     coefficients and two estimates of their  asymptotic
     variance-covariance matrix. 

    coef: regression coefficients.

naive.var: Self-Prentice model based variance-covariance matrix.

     var: Lin-Ying empirical variance-covariance matrix. 

_A_u_t_h_o_r(_s):

     Norman Breslow, modified by Thomas Lumley

_R_e_f_e_r_e_n_c_e_s:

     Prentice, RL (1986). A case-cohort 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 case-cohort 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 case-cohort
     design. Biometrics 50: 1064-1072

     Therneau, TM and Li, H (1999). Computing the Cox model for
     case-cohort designs. Lifetime Data Analysis 5: 99-112.

     Borgan, O et al. (1999). Exposure stratified case-cohort designs.
     Lifetime Data Analysis

_E_x_a_m_p_l_e_s:

     ## The complete Wilms Tumor Data 
     ## (Breslow and Chatterjee, Applied Statistics, 1999)
     ## subcohort selected by simple random sampling.
     ##

     data(nwtco)

     subcoh <- nwtco$in.subcohort
     selccoh <- with(nwtco, rel==1|subcoh==1)
     ccoh.data <- nwtco[selccoh,]
     ccoh.data$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 case-cohort 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)