The purpose of popEpi is to facilitate computing certain epidemiological statistics where population data is used. Current main attractions:
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 41.68877x <- 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 41.69
#> 1 0.91 1995.00 42.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 1.00 1995.09 42.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 1.91 1996.00 43.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 2.00 1996.09 43.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 2.91 1997.00 44.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 3.00 1997.09 44.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 3.91 1998.00 45.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 4.00 1998.09 45.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69
#> 1 4.91 1999.00 46.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69data(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 41.69 0 1.00
#> 1 0.91 1995.00 42.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.00
#> 1 1.00 1995.09 42.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.00
#> 1 1.91 1996.00 43.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.00
#> 1 2.00 1996.09 43.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.00
#> 1 2.91 1997.00 44.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.00
#> 1 3.00 1997.09 44.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.01
#> 1 3.91 1998.00 45.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.01
#> 1 4.00 1998.09 45.69 0.91 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.01
#> 1 4.91 1999.00 46.60 0.09 0 0 1 1952-05-27 1994-02-03 2012-12-31 0 41.69 0 1.01a <- 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 1One 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 0.094The 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