Big Green CD 8/BGCD_8_Dev.iso/NEXTSTEP/UNIX/Educational/R-0.49-MI/R-0.49-I/help/survival4/survexp

Text File | 1997-09-13 | 5.5 KB | 130 lines

    _C_o_m_p_u_t_e _E_x_p_e_c_t_e_d _S_u_r_v_i_v_a_l _R_e_t_u_r_n_s _e_i_t_h_e_r _t_h_e _e_x_p_e_c_t_e_d _s_u_r_-
    _v_i_v_a_l _o_f _a _c_o_h_o_r_t _o_f _s_u_b_j_e_c_t_s, _o_r _t_h_e _i_n_d_i_v_i_d_u_a_l _e_x_p_e_c_t_e_d
    _s_u_r_v_i_v_a_l _f_o_r _e_a_c_h _s_u_b_j_e_c_t.

         survexp(formula, data, weights, subset, na.action,
          times, cohort=T, conditional=F,
          ratetable=survexp.us, scale=1, se.fit, model=F, x=F, y=F)

    _A_r_g_u_m_e_n_t_s:

          formula:
         a formula object.  The response variable will be a vec-
         tor of follow-up times, and is optional.  The predic-
         tors will consist of optional grouping variables
         separated by + operators (exactly as in survfit), along
         with a ratetable() term.  This latter matches each sub-
         ject to his/her expected cohort.

    data, weights, subset, na.action:
         as in other modeling routines.  Weights are currently
         ignored.

            times:
         an optional vector of times at which the resulting sur-
         vival curve should be evaluated.  If absent, the result
         will be reported for each unique value of the vector of
         follow-up times.

           cohort:
         If false, each subject is treated as a subgroup of size
         1.

      conditional:
         If y is missing in the formula, this argument is
         ignored.  Otherwise it is an indicator of whether y
         includes death times, which leads to conditional
         expected survival, or y includes only the potential
         censoring times.

        ratetable:
         a table of event rates, such as survexp.uswhite, or a
         fitted Cox model.

            scale:
         a scaling for the results.  As most rate tables are in
         units/day, a value of 365.24 would cause the output to
         be reported in years.

          npoints:
         calculate intermediate results at npoints values,
         evenly spaced on the range of y.  The usual (exact)
         calculation is done at each unique 'y' value; for very
         large data sets this may incur too much storage for the
         scratch array.  For a prediction from a Cox model this
         arument is ignored.

           se.fit:
         compute the standard error of the predicted survival.
         The default is to compute this whenever the routine
         can, which at this time is only for the Ederer method
         and a Cox model as the rate table.

      model, x, y:
         flags to control what is returned.  If any of these is
         true, then the model frame, the model matrix, and/or
         the vector of response times will be returned as com-
         ponents of the final result, with the same names as the
         flag arguments.

         Value:

         if cohort=T an object of class survexp, otherwise a
         vector of per-subject expected survival values.  The
         former contains the number of subjects at risk and the
         expected survival for the cohort at each requested
         time.  Individual expected survival is ususally used in
         models or testing, to correct for the age and sex com-
         position of a group of subjects.  For instance, assume
         that birth date, entry date onto the study,sex and
         actual survival time are all known for a group of sub-
         jects.  The uswhite population tables contain expected
         death rates based on calendar year, sex and age.  Then

         haz <- -log(survexp(death.time ~ ratetable(sex=sex, year=entry.dt, age=(birth.dt-entry.dt)), cohort=F))
    gives for each subject the total hazard experienced up to
    their observed death time or censoring time.  This probabil-
    ity can be used as a rescaled time value in models:

         glm(status ~ 1 + offset(log(haz)), family=poisson)
           glm(status ~ x + offset(log(haz)), family=poisson)
    In the first model, a test for intercept=0 is the one sample
    log-rank test of whether the observed group of subjects has
    equivalent survival to the baseline population.  The second
    model tests for an effect of variable x after adjustment for
    age and sex.  Cohort survival is used to produce an overall
    survival curve.  This is then added to the Kaplan-Meier plot
    of the study group for visual comparison between these sub-
    jects and the population at large.  There are three common
    methods of computing cohort survival.  In the "exact method"
    of Ederer the cohort is not censored; this corresponds to
    having no response variable in the formula.  Hakulinen
    recommends censoring the cohort at the anticipated censoring
    time of each patient, and Verhuel recommends censoring the
    cohort at the actual observation time of each patient.  The
    last of these is the conditional method.  These are obtained
    by using the respective time values as the follow-up time or
    response in the formula.

         References:

         G. Berry.  The analysis of mortality by the subject-
         years method.  Biometrics 1983, 39:173-84.  F Ederer, L
         Axtell, and S Cutler.  The relative survival rate: a
         statistical methodology. Natl Cnacer Inst Monogr 1961,
         6:101-21.  T. Hakulinen.  Cancer survival corrected for
         heterogeneity in patient withdrawal.  Biometrics 1892,
         38:933.  H. Verheul, E. Dekker, P. Bossuyt, A. Moulijn,
         and A. Dunning.  Backround mortality in clinical sur-
         vival studies.  Lancet 1993, 341:872-5.

         survfit, survexp.us, survexp.fit, personyr, date

    _E_x_a_m_p_l_e_s:

         efit <- survexp( ~ ratetable(sex=sex, year=entry.dt, age=entry.dt-birth.dt))
         plot(survfit(Surv(futime, status) ~1))
         lines(efit)