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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
#> 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
#>  1:      1 0.000000 1994.09 41.68877 0.90958904       0       0   1
#>  2:      1 0.909589 1995.00 42.59836 0.09041096       0       0   1
#>  3:      1 1.000000 1995.09 42.68877 0.90958904       0       0   1
#>  4:      1 1.909589 1996.00 43.59836 0.09041096       0       0   1
#>  5:      1 2.000000 1996.09 43.68877 0.90958904       0       0   1
#>  6:      1 2.909589 1997.00 44.59836 0.09041096       0       0   1
#>  7:      1 3.000000 1997.09 44.68877 0.90958904       0       0   1
#>  8:      1 3.909589 1998.00 45.59836 0.09041096       0       0   1
#>  9:      1 4.000000 1998.09 45.68877 0.90958904       0       0   1
#> 10:      1 4.909589 1999.00 46.59836 0.09041096       0       0   1
#>        bi_date    dg_date    ex_date status   dg_age
#>  1: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  2: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  3: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  4: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  5: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  6: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  7: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  8: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#>  9: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
#> 10: 1952-05-27 1994-02-03 2012-12-31      0 41.68877
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
#>  1:      1 0.000000 1994.09 41.68877 0.90958904       0       0   1
#>  2:      1 0.909589 1995.00 42.59836 0.09041096       0       0   1
#>  3:      1 1.000000 1995.09 42.68877 0.90958904       0       0   1
#>  4:      1 1.909589 1996.00 43.59836 0.09041096       0       0   1
#>  5:      1 2.000000 1996.09 43.68877 0.90958904       0       0   1
#>  6:      1 2.909589 1997.00 44.59836 0.09041096       0       0   1
#>  7:      1 3.000000 1997.09 44.68877 0.90958904       0       0   1
#>  8:      1 3.909589 1998.00 45.59836 0.09041096       0       0   1
#>  9:      1 4.000000 1998.09 45.68877 0.90958904       0       0   1
#> 10:      1 4.909589 1999.00 46.59836 0.09041096       0       0   1
#>        bi_date    dg_date    ex_date status   dg_age     pop.haz       pp
#>  1: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.001170685 1.000651
#>  2: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.001441038 1.000651
#>  3: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.001200721 1.001856
#>  4: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.001300846 1.001856
#>  5: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.001400981 1.003207
#>  6: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.002142293 1.003207
#>  7: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.002202424 1.005067
#>  8: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.001771568 1.005067
#>  9: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.002222468 1.007277
#> 10: 1952-05-27 1994-02-03 2012-12-31      0 41.68877 0.002282603 1.007277
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)
#>     fot  per       pyrs at.risk from0to0
#>  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')
#> Confidence intervals calculated from profile-likelihood.
se
#> SIR Standardized by:  agegroup year sex
#> 
#>  Total observed: 4559 
#>  Total expected: 1482.13 
#>  Total person-years: 
#> 39905.92 
#> 
#> Poisson modelled SIR: 
#>    fot observed expected     pyrs  sir 2.5 % 97.5 % p_value
#> 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
#> 
#> Test for homogeneity p < 0.001

(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)
#> 
#> Attaching package: 'Epi'
#> The following object is masked from 'package:base':
#> 
#>     merge.data.frame

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'
#>    cancer Tstop surv.obs.lo surv.obs surv.obs.hi SE.surv.obs CIF_canD
#> 1: breast   2.5      0.8804   0.8870      0.8933    0.003290   0.0687
#> 2: breast   5.0      0.7899   0.7986      0.8070    0.004368   0.1162
#> 3: rectal   2.5      0.6250   0.6359      0.6465    0.005480   0.2981
#> 4: rectal   5.0      0.5032   0.5148      0.5263    0.005901   0.3727
#>    CIF_othD
#> 1:   0.0442
#> 2:   0.0852
#> 3:   0.0660
#> 4:   0.1125