Description
Functions for Epidemiological Analysis using Population Data.
Description
Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'.
README.md
popEpi: Epidemiology with population data
The purpose of popEpi is to facilitate computing certain epidemiological statistics where population data is used. Current main attractions:
Splitting, merging population hazards, and aggregating
the lexpand function allows users to split their subject-level follow-up data into sub-intervals along age, follow-up time and calendar time, merge corresponding population hazard information to those intervals, and to aggregate the resulting data if needed.
data(sire)
sr <- sire[1,]
print(sr)
#> sex bi_date dg_date ex_date status dg_age
#> <int> <IDat> <IDat> <IDat> <int> <num>
#> 1: 1 1952-05-27 1994-02-03 2012-12-31 0 41.68877
x <- lexpand(sr, birth = bi_date, entry = dg_date, exit = ex_date,
status = status %in% 1:2,
fot = 0:5, per = 1994:2000)
print(x)
#> lex.id fot per age lex.dur lex.Cst lex.Xst sex bi_date dg_date ex_date status dg_age
#> 1 0.00 1994.09 41.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 0.91 1995.00 42.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 1.00 1995.09 42.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 1.91 1996.00 43.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 2.00 1996.09 43.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 2.91 1997.00 44.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 3.00 1997.09 44.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 3.91 1998.00 45.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 4.00 1998.09 45.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
#> 1 4.91 1999.00 46.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689
data(popmort)
x <- lexpand(sr, birth = bi_date, entry = dg_date, exit = ex_date,
status = status %in% 1:2,
fot = 0:5, per = 1994:2000, pophaz = popmort)
print(x)
#> lex.id fot per age lex.dur lex.Cst lex.Xst sex bi_date dg_date ex_date status dg_age pop.haz pp
#> 1 0.00 1994.09 41.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.001 1.001
#> 1 0.91 1995.00 42.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.001 1.001
#> 1 1.00 1995.09 42.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.001 1.002
#> 1 1.91 1996.00 43.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.001 1.002
#> 1 2.00 1996.09 43.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.001 1.003
#> 1 2.91 1997.00 44.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.002 1.003
#> 1 3.00 1997.09 44.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.002 1.005
#> 1 3.91 1998.00 45.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.002 1.005
#> 1 4.00 1998.09 45.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.002 1.007
#> 1 4.91 1999.00 46.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.689 0.002 1.007
a <- lexpand(sr, birth = bi_date, entry = dg_date, exit = ex_date,
status = status %in% 1:2,
fot = 0:5, per = 1994:2000, aggre = list(fot, per))
print(a)
#> Key: <fot, per>
#> fot per pyrs at.risk from0to0
#> <int> <int> <num> <num> <num>
#> 1: 0 1994 0.90958904 0 0
#> 2: 0 1995 0.09041096 1 0
#> 3: 1 1995 0.90958904 0 0
#> 4: 1 1996 0.09041096 1 0
#> 5: 2 1996 0.90958904 0 0
#> 6: 2 1997 0.09041096 1 0
#> 7: 3 1997 0.90958904 0 0
#> 8: 3 1998 0.09041096 1 0
#> 9: 4 1998 0.90958904 0 0
#> 10: 4 1999 0.09041096 1 1
SIRs / SMRs
One can make use of the sir function to estimate indirectly standardised incidence or mortality ratios (SIRs/SMRs). The data can be aggregated by lexpand or by other means. While sir is simple and flexible in itself, one may also use sirspline to fit spline functions for the effect of e.g. age as a continuous variable on SIRs.
data(popmort)
data(sire)
c <- lexpand( sire, status = status %in% 1:2, birth = bi_date, exit = ex_date, entry = dg_date,
breaks = list(per = 1950:2013, age = 1:100, fot = c(0,10,20,Inf)),
aggre = list(fot, agegroup = age, year = per, sex) )
#> dropped 16 rows where entry == exit
se <- sir( coh.data = c, coh.obs = 'from0to1', coh.pyrs = 'pyrs',
ref.data = popmort, ref.rate = 'haz',
adjust = c('agegroup', 'year', 'sex'), print = 'fot')
se
#> SIR (adjusted by agegroup, year, sex) with 95% confidence intervals (profile)
#> Test for homogeneity: p < 0.001
#>
#> Total sir: 3.08 (2.99-3.17)
#> Total observed: 4559
#> Total expected: 1482.13
#> Total person-years: 39906
#>
#> Key: <fot>
#> fot observed expected pyrs sir sir.lo sir.hi p_value
#> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 0 4264 1214.54 34445.96 3.51 3.41 3.62 0.000
#> 2: 10 295 267.59 5459.96 1.10 0.98 1.23 0.094
(Relative) survival
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.
library(Epi)
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"))
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.
st <- survtab(FUT ~ cancer, data = x,
breaks = list(FUT = seq(0, 5, 1/12)),
surv.type = "cif.obs")
st
#>
#> Call:
#> survtab(formula = FUT ~ cancer, data = x, breaks = list(FUT = seq(0, 5, 1/12)), surv.type = "cif.obs")
#>
#> Type arguments:
#> surv.type: cif.obs --- surv.method: hazard
#>
#> Confidence interval arguments:
#> level: 95 % --- transformation: log-log
#>
#> Totals:
#> person-time:62120 --- events: 5375
#>
#> Stratified by: 'cancer'
#> Key: <cancer>
#> cancer Tstop surv.obs.lo surv.obs surv.obs.hi SE.surv.obs CIF_canD CIF_othD
#> <fctr> <num> <num> <num> <num> <num> <num> <num>
#> 1: breast 2.5 0.8804 0.8870 0.8933 0.003290 0.0687 0.0442
#> 2: breast 5.0 0.7899 0.7986 0.8070 0.004368 0.1162 0.0852
#> 3: rectal 2.5 0.6250 0.6359 0.6465 0.005480 0.2981 0.0660
#> 4: rectal 5.0 0.5032 0.5148 0.5263 0.005901 0.3727 0.1125