pyears               package:survival               R Documentation

_P_e_r_s_o_n _Y_e_a_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function computes the person-years of follow-up time
     contributed by a cohort of subjects, stratified into subgroups. It
     also computes the number of subjects who contribute to each cell
     of the output table, and optionally the number of events and/or
     expected number of events in each cell.

_U_s_a_g_e:

     pyears(formula, data, weights, subset, na.action, ratetable=survexp.us,
     scale=365.25, expect=c('event', 'pyears'), model=FALSE, x=FALSE, y=FALSE)

_A_r_g_u_m_e_n_t_s:

 formula: a formula object.  The response variable will be a vector of
          follow-up times for each subject, or a 'Surv' object
          containing the follow-up time and an event indicator. The
          predictors consist of optional grouping variables separated
          by + operators (exactly as in 'survfit'), time-dependent
          grouping variables such as age (specified with 'tcut'), and
          optionally a 'ratetable()' term.  This latter matches each
          subject to his/her expected cohort. 

    data: a data frame in which to interpret the variables named in the
          'formula', or in the 'subset' and the 'weights' argument. 

 weights: case weights. 

  subset: expression saying that only a subset of the rows of the data
          should be used in the fit. 

na.action: a missing-data filter function, applied to the model.frame,
          after any 'subset' argument has been used.  Default is
          'options()$na.action'. 

ratetable: a table of event rates, such as 'survexp.uswhite'. 

   scale: a scaling for the results.  As most rate tables are in
          units/day, the default value of 365.25 causes the output to
          be reported in years. 

  expect: should the output table include the expected number of
          events, or the expected number of person-years of
          observation.  This is only valid with a rate table. 

model,x,y: If any of these is true, then the model frame, the model
          matrix, and/or the vector of response times will be returned
          as components of the final result. 

_D_e_t_a_i_l_s:

     Because 'pyears' may have several time variables, it is necessary
     that all of them be in the same units.  For instance, in the call
     py <- pyears(futime ~ rx + ratetable(age=age, sex=sex,
     year=entry.dt)) with a 'ratetable' whose natural unit is days, it
     is important that 'futime', 'age' and 'entry.dt' all be in days. 
     Given the wide range of possible inputs, it is difficult for the
     routine to do sanity checks of this aspect.

     A special function 'tcut' is needed to specify time-dependent
     cutpoints. For instance, assume that age is in years, and that the
     desired final arrays have as one of their margins the age groups
     0-2, 2-10, 10-25, and 25+. A subject who enters the study at age 4
     and remains under observation for 10 years will contribute
     follow-up time to both the 2-10 and 10-25 subsets.  If 'cut(age,
     c(0,2,10,25,100))' were used in the formula, the subject would be
     classified according to his starting age only. The 'tcut' function
     has the same arguments as 'cut', but produces a different output
     object which allows the 'pyears' function to correctly track the
     subject.

     The results of 'pyears' are normally used as input to further
     calculations. The example below is from a study of hip fracture
     rates from 1930 - 1990 in Rochester, Minnesota.  Survival post hip
     fracture has increased over that time, but so has the survival of
     elderly subjects in the population at large.  A model of relative
     survival helps to clarify what has happened: Poisson regression is
     used, but replacing exposure time with expected exposure (for an
     age and sex matched control). Death rates change with age, of
     course, so the result is carved into 1 year increments of time. 
     Males and females were done separately.

_V_a_l_u_e:

     a list with components

  pyears: an array containing the person-years of exposure. (Or other
          units, depending on the rate table and the scale). 

       n: an array containing the number of subjects who contribute
          time to each cell of the 'pyears' array. 

   event: an array containing the observed number of events.  This will
          be present only if the response variable is a 'Surv' object. 

expected: an array containing the expected number of events (or person
          years). This will be present only if there was a ratetable
          term. 

offtable: the number of person-years of exposure in the cohort that was
          not part of any cell in the 'pyears' array.  This is often
          useful as an error check; if there is a mismatch of units
          between two variables, nearly all the person years may be off
          table. 

 summary: a summary of the rate-table matching.  This is also useful as
          an error check. 

    call: an image of the call to the function. 

na.action: the 'na.action' attribute contributed by an 'na.action'
          routine, if any. 

_S_e_e _A_l_s_o:

     'ratetable', 'survexp', 'Surv'

_E_x_a_m_p_l_e_s:

     # 
     # Simple case: a single male subject, born 6/6/36 and entered on study 6/6/55.
     #

     temp1 <- mdy.date(6,6,36)
     temp2 <- mdy.date(6,6,55)
     # Now compare the results from person-years
     #
     temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))),
             labels=c('0-18', paste(18:30, 19:31, sep='-')))
     temp.yr  <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964)
     temp.time <- 3700   #total days of fu
     py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days

     survexp.uswhite<-survexp.usr[,,"white",]
     py2 <- pyears(temp.time ~ temp.age + temp.yr
                     + ratetable(age=temp2-temp1, year=temp2, sex=1),
                  scale=1, ratetable=survexp.uswhite ) #output in days