| pyears {survival} | R Documentation |
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.
pyears(formula, data, weights, subset, na.action, ratetable=survexp.us,
scale=365.25, expect=c('event', 'pyears'), model=FALSE, x=FALSE, y=FALSE)
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. |
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.
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.
|
#
# 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