Age, sex and race matched US reference survival — the dashed comparison line on a clinical survival figure — computed in R.
Status: working. us_matched(), us_cohort_curve(), us_lifetable_vintages() and us_lifetable_model() are implemented and tested against the fitted blocks. Production SAS acceptance is complete: all 32 stored %usmatchd answers from two studies matched to 7.9e-15 worst case, and one study carries a repeatable acceptance document outside this package. The remaining gap is narrower: the AVR/LV-function study’s 31 table84 answers have not yet been installed as a repeatable test in that study. See the design spec.
What this is
An R replacement for the Cleveland Clinic SAS macro %usmatchd.
The surprise that shapes the whole package: %usmatchd is not a life-table lookup. It is PROC HAZPRED evaluating a stored three-phase parametric hazard fit on the age axis — time origin is birth, not surgery. So this is not a data package carrying life tables. It ships ~27 small fitted-parameter blocks and a thin evaluator over TemporalHazard.
survival::survexp.usr cannot substitute. Twelve vintage × interpolation combinations were tested; the best individual-curve error in every one was 0.09–0.11, with an age tilt no vintage removes. The reason is structural — there is no table on the R side of that comparison.
Interface
Signatures, not runnable calls — vintage and stratum appear bare because they have no defaults:
us_matched(age, male, other, times,
id = seq_along(age),
vintage,
table = c("sexrace", "race", "sex", "overall"),
scale = c("years", "months", "days"),
individual = TRUE)
us_cohort_curve(x, by = NULL) # the cohort curve, or one per group
us_lifetable_vintages() # vintages, provenance, race semantics
us_lifetable_model(vintage, stratum) # the raw parameter setArgument names mirror the macro so a SAS job translates by inspection.
Example
A 62-year-old white male and a 71-year-old white female, ten years of follow-up, against the 1984 fits:
library(hvtiRlifetables)
us_matched(age = c(62, 71),
male = c(1, 0),
other = c(0, 0),
times = seq(0, 10, by = 5),
vintage = "table84")
#> id time agesurv smatched hmatched
#> 1 1 0 0.7925575 1.0000000 0.01826492
#> 2 1 5 0.7925575 0.8914976 0.02843656
#> 3 1 10 0.7925575 0.7453229 0.04439022
#> 4 2 0 0.7773741 1.0000000 0.02241349
#> 5 2 5 0.7773741 0.8640873 0.03736907
#> 6 2 10 0.7773741 0.6758045 0.06334817agesurv is survival from birth to the patient’s current age; smatched is conditional on having reached it, so it starts at 1. Add individual = FALSE for the cohort mean curve, which is what a figure’s dashed line usually needs.
The cohort curve, and reporting by group
us_cohort_curve() reduces the per-patient frame to that dashed line, and with by gives one line per group of your own choosing:
x <- us_matched(age = c(62, 71, 84, 88), male = c(1, 0, 1, 0),
other = c(0, 0, 0, 0), times = seq(0, 10, by = 5),
vintage = "table84")
band <- factor(c("<80", "<80", "80+", "80+")[x$id], levels = c("<80", "80+"))
us_cohort_curve(x, by = band)
#> group time smatched hmatched
#> 1 <80 0 1.0000000 0.02033920
#> 2 <80 5 0.8777925 0.03283308
#> 3 <80 10 0.7105637 0.05340550
#> 4 80+ 0 1.0000000 0.13316718
#> 5 80+ 5 0.4201996 0.22110436
#> 6 80+ 10 0.1001218 0.36277801x holds one row per patient per time, so a patient-level grouping is expanded to it by id — that is what band[x$id] is doing above.
by is not table. table chooses which strata the life table itself is built from; by chooses how the resulting curves are grouped for reporting. The two axes are independent, and a report broken down by age band is normally still matched on "sexrace".
The averaging is the macro’s, and it is made in one place so that two studies cannot average differently without noticing: survival and the density hmatched * smatched are averaged across patients at each time, unweighted, and the mean hazard is recovered by dividing. It is not the mean of the individual hazards.
Omitting vintage is an error, deliberately:
us_matched(70, 1, 0, times = 5)
#> Error: `vintage` has no default and must be given.
#> Available: table84, table2008, table2023.
vintage has no default
Omitting it is an error.
%usmatchd’s own default silently moved twice: from table84 to table2008 between 2010 and 2024, then to table2023 on 2025-12-23. Every job re-run across either move got different numbers with no signal. An analysis that does not state its reference vintage is not reproducible, so the package refuses to guess. This is deliberate friction and it is the package’s main value over calling the macro.
Race semantics — read this before citing a stratum
table84 names its non-white strata hzico*, honestly “other”. table2008 and table2023 name the same strata hzicb*, and the macro’s own comment claims these are Black-race estimates.
For 2023 that comment is false. The 2023 “other” category is a risk-weighted average of Black, Asian, American Indian and Hispanic death rates, weighted by number at risk, stored under B only to keep the macro’s naming consistent. This package does not propagate the macro’s error.
hmatched is per year, always
Whatever scale= you pass, time is in those units but hmatched is a hazard per year.
This matches the macro’s source. usmatchd.sas:227 assigns HMATCHED = _HAZARD with no SCALEF multiplication, and PROC HAZPRED is evaluated at AGE_YR in years. The macro’s own header comment at line 47 claims HMATCHED is “scaled by SCALE” and is wrong — but it has never mattered, because every job that consumes it uses scale="years", where SCALEF = 1.
This package reproduces the code, not the comment, because bit-fidelity to the macro is the acceptance criterion. If you need a per-month or per-day hazard, multiply by the corresponding SCALEF yourself.
Installation
hvtiRlifetables is part of the HVTI R family. Install it with the rest of the family through hvtiR:
# install.packages("pak")
pak::pak("ehrlinger/hvtiR")
hvtiR::install()hvtiR::install() installs every member in one call, so the versions the members require of one another resolve together. Update later with hvtiR::update(), from a fresh R session: it will not overwrite a package that is already loaded.
To install hvtiRlifetables on its own:
pak::pak("ehrlinger/hvtiRlifetables")The package requires TemporalHazard (>= 1.2.0), and CRAN currently carries 1.1.0. The bound is real, not defensive: the evaluator calls hzr_decompos_g3(), which 1.1.0 does not provide. Both commands above take TemporalHazard from GitHub instead: hvtiR::install() installs it with the family, and the Remotes: field in DESCRIPTION points pak at it when you install hvtiRlifetables on its own.
Documentation
-
Overview,
vignette("hvtiRlifetables"), takes a synthetic cohort fromus_lifetable_vintages()throughus_matched()tous_cohort_curve(), with and without a reporting group.