News for version 0.4.12

Unit tests

• No changes in the package itself — fixed a unit test that used the output of survival::summmary.survfit which had improved slightly in 3.6-4.

News for version 0.4.11

• No changes in any functionality — added help page for ?“popEpi-package” as requested by CRAN.

News for version 0.4.10

• No changes for user — “Suggests” packages now used conditionally in vignettes/tests/examples as per Writing R Extensions.

News for version 0.4.9

• popEpi::aggre bugfix: aggre now correctly infers which stratifying variables use Lexis time scales when output is cartesian and argument by is of type character.

News for version 0.4.8

• small internal fixes due to upcoming new package survival release
• popEpi now includes a wrapper for survival::Surv, so you don’t need to do library(“survival”) when using Surv() in formulae (e.g. in survtab)

News for version 0.4.7

• implemented small internal changes due to upcoming R 3.6.0

News for version 0.4.6

• implemented small internal changes due to upcoming data.table version

News for version 0.4.5

• fixed errors arising from new data.table version

News for version 0.4.4

• splitLexisDT/splitMulti bug fix: splitting along multiple time scales sometimes produced duplicate transitions (e.g. alive -> dead in the last two rows). see https://github.com/FinnishCancerRegistry/popEpi/issues/138 for details.
• splitLexisDT/splitMulti now retain time.since attribute; this attribute plays a role in cutLexis
• known issue: splitLexisDT/splitMulti not guaranteed to work identically to splitLexis from Epi when there are NA values in the time scale one is splitting along.

News for version 0.4.3

• survtab adjusting was broken with older versions of data.table (tested 1.9.6); therefore popEpi now requires the newest version of data.table!

News for version 0.4.2

• survtab() bug fix: standard errors were mis-specified for adjusted curves, e.g. age-adjusted Ederer II estimates. This resulted in too wide confidence intervals! SEE HERE FOR EXAMPLE: #135. The standard errors and confidence intervals of non-adjusted curves have always been correct.
• survtab() bug fix: confidence level was always 95 % regardless of conf.level #134

News for version 0.4.1

• lexpand() bug fixed (#120): observations were dropped if their entry by age was smaller than the smallest age value, though entry at exit is correct and used now.
• sir() rewrite (#118, #122). New more consistent output, updates on plotting and minor News for version arguments. Introduce very simple coef() and confint() methods for sir class.
• new functions in sir family: sir_ag(), sir_lex() and sir_exp() for extracting SMRs from aggre and Lexis objects.
• fixed issue in internal test brought by pkg survival version 2.39.5; No changes in functions were needed (#125)
• robustified aggre(); there were issues with Epi pkg dev version which are definitely avoided (#119)

News for version 0.4.0

• removed previously deprecated shift.var (#35)
• popEpi no longer depends on package data.table but imports it - this means the user will have to do library(data.table) separately to make data.table’s functions become usable. Formerly popEpi effectively did library(data.table) when you did library(popEpi).
• summary.survtab: args t and q behaviour changed
• survtab: internal weights now based on counts of subjects in follow-up at the start of follow-up (used to be sum of counts/pyrs over all of follow-up)
• new functions: rate_ratio(), sir_ratio()
• small internal News for version preparation for data.table 1.9.8

News for version 0.3.1

This is a hotfix. survtab() was causing warnings in certain situations, which this update fixes. Also fixed plotting survtab objects so that multiple strata are plotted correctly when one or more curves end before the longest one as well other small fixes: See Github issues #89, #90, #91, and #92.

News for version 0.3.0

Adjusting

Direct adjusting (computing weighted averages of estimates) has been generalized. Functions such as survtab and survmean allow for using adjust() mini function within formulas, or a separate adjust argument. Weights are passed separately. See the examples in the next chapter. See also ?direct_adjusting.

Estimating functions of survival time

The survtab function computes observed, net/relative and cause-specific survivals as well as cumulative incidence functions for Lexis data. Any of the supported survival time functions can be easily adjusted by any number of categorical variables if needed.

One can also use survtab_ag for aggregated data. This means the data does not have to be on the subject-level to compute survival time function estimates.

## prep data
data(sibr)
sire$cancer <- "rectal"
sibr$cancer <- "breast"
sr <- rbind(sire, sibr)

sr$cancer <- factor(sr$cancer)
sr <- sr[sr$dg_date < sr$ex_date, ]

sr$status <- factor(sr$status, levels = 0:2, 
                    labels = c("alive", "canD", "othD"))

## create Lexis object
library(Epi)
x <- Lexis(entry = list(FUT = 0, AGE = dg_age, CAL = get.yrs(dg_date)), 
           exit = list(CAL = get.yrs(ex_date)), 
           data = sr,
           exit.status = status)
#> NOTE: entry.status has been set to "alive" for all.

## population hazards file - see ?pophaz for general instructions
data(popmort)
pm <- data.frame(popmort)
names(pm) <- c("sex", "CAL", "AGE", "haz")

## simple usage - uses lex.Xst as status variable
st <- survtab(FUT ~ cancer, data = x,
              breaks = list(FUT = seq(0, 5, 1/12)),
              surv.type = "surv.rel", pophaz = pm)

## more explicit usage
st <- survtab(Surv(FUT, event = lex.Xst) ~ cancer, data = x,
              breaks = list(FUT = seq(0, 5, 1/12)),
              surv.type = "surv.rel", pophaz = pm)


## adjusting
x$agegr <- cut(x$dg_age, c(0,55,65,75,Inf))
w <- as.numeric(table(x$agegr))
st <- survtab(Surv(FUT, event = lex.Xst) ~ cancer + adjust(agegr), 
              data = x,
              breaks = list(FUT = seq(0, 5, 1/12)),
              surv.type = "surv.rel", 
              pophaz = pm, weights = w)

Rates

The new rate function enables easy calculation of e.g. standardized incidence rates:

## dummy data

a <- merge(0:1, 1:18)
names(a) <- c("sex", "agegroup")
set.seed(1)
a$obs <- rbinom(nrow(a), 100, 0.5)
set.seed(1)
a$pyrs <- rbinom(nrow(a), 1e4, 0.75)

## so called "world" standard rates (weighted to hypothetical world pop in 2000)
r <- rate(data = a, obs = obs, pyrs = pyrs, print = sex, 
          adjust = agegroup, weights = 'world_2000_18of5')
#> Warning in pyrJjCscXlsrH * pyrJjCscXlsrH: NAs produced by integer overflow

#> Warning in pyrJjCscXlsrH * pyrJjCscXlsrH: NAs produced by integer overflow