Complete redesign of the two effect-estimation functions,
estimate_hr_ir() and estimate_rr_rd() (API
v2). This is a BREAKING release for both functions; the
propensity-score, weighting, matching, and Table-1 functions are
unchanged.
in_df_crude plus either in_df_weight (with
if_weight_weight_var) or in_df_match
(optionally with if_match_match_id) — supplying both
weighted and matched blocks is an error; weight- or match-only calls
without a crude block are allowed. Output column suffixes are now
_Crude / _Weighted / _Matched
(previously _Unwt / _Wted).survival_time ->
followuptime_var; stratify_by ->
stratification_var; rr_rd_per_individuals
-> risk_per_individuals; weight_var ->
if_weight_weight_var.hr_conf_int_method,
cluster_var, conf_int_method,
irr_method, and irr_covariates have been
removed. Each estimate uses one fixed, documented method (below).if_bootstrap_count (plus if_bootstrap_n_cores
/ if_bootstrap_seed) and is always fixed-weight (“frozen”):
analysis weights and standardization shares are held at their original
values, so propensity-score-estimation uncertainty is not propagated (a
message states this on every bootstrap run). Matched blocks resample
matched sets by if_match_match_id. Only percentile
intervals are provided (no BCa).time_unit gains "months" (person-years =
months / 12) in estimate_hr_ir().estimate_hr_ir()): crude block = Cox model SE;
weighted block = robust sandwich SE; matched block = robust SE clustered
on the match id (Lin-Wei 1989; Austin 2016). The old “auto” dispatch is
now the only behavior.estimate_rr_rd() (fg_regression removed) into
estimate_hr_ir(hr_model = "Fine-Gray") with
if_fg_competing_event_var; weighted expansions keep fgwt =
IPCW x PS weight, and matched blocks cluster the robust SE on the match
id. estimate_rr_rd() keeps
risk_estimator = c("KM", "AJ") with the competing-event
indicator renamed to if_aj_competing_event_var
(AJ-only).estimate_hr_ir() as a
marginal rate ratio: crude/matched blocks use a Poisson rate model
(model SE, equal to sqrt(1/D1 + 1/D0)); the weighted block uses
survey::svyglm quasi-Poisson (robust SE). The former
quasi-Poisson option for unweighted fits is gone. Stratified IRRs,
previously an error, are now direct-standardized.estimate_rr_rd()): the log-scale
interval is replaced by the complementary log-log (cloglog) interval
applied to the final (standardized/weighted) estimate with its combined
SE (Kalbfleisch & Prentice 2002). A crude Kaplan-Meier risk now
reproduces survfit(conf.type = "log-log") exactly. A risk
of exactly 0 or 1 has no defined cloglog interval: NA is returned and a
message recommends the bootstrap. RD (normal-Wald) and RR (log-delta)
intervals are unchanged.tests/testthat/fixtures/ and guard the redesign: every
method-unchanged output reproduces the 0.3.0 values exactly; only the
weighted IR/IRD CIs (robust), standardized IR arm CIs (Fay-Feuer),
Risk/CIF arm CIs (cloglog), and the crude IRR SE (Poisson) changed, each
by design.Depends: R (>= 3.5.0)
(serialized test fixtures use serialization format version 3).(Not submitted to CRAN; folded into 0.4.0.)
create_ps_weights() has been removed. The
single function with weight_method + estimand
arguments is replaced by one function per estimand:
create_iptw() — inverse-probability weights for the ATE
(stabilize = TRUE for stabilized IPTW).create_matching_weights() — matching weights, now
correctly labeled as targeting the ATM (Li & Greene 2013), not the
ATT.create_overlap_weights() — overlap weights for the ATO
(Li et al. 2018). IPTW for the ATT (SMR weights) is not supported in
this version. The broken stabilized-IPTW + ATO path has been
removed.create_iptw(), create_matching_weights(),
create_overlap_weights(), and
create_ps_matched_cohort() gain optional propensity-score
trimming (trim_method: "crump" symmetric or
"sturmer" asymmetric). create_iptw()
additionally gains weight truncation/winsorizing
(truncate_method: "percentile" or
"cap"). All default to off; both trimming and truncation
change the analytic population and the interpretation of the
estimand.create_ps_matched_cohort() gains a method
argument ("nearest"/"subclass"),
variable-ratio matching (min_controls /
max_controls / m_order), and
subclass_n. For "subclass" the returned cohort
carries matching weights in .match_weights, which must be
used for the matched Table 1 and any downstream effect estimation.|SMD| against
threshold * 100 and the Love-plot reference line was drawn
at 10 (off-screen) on raw-scale data.estimate_hr_ir() gained a
hr_conf_int_method argument controlling how the
hazard-ratio confidence interval (and the reported lnHR_SE)
was computed:
"auto" (default): model-based SE for the unweighted fit
and robust sandwich SE for the weighted fit (Austin 2016)."model": model-based (naive) Cox SE with a Wald CI for
both fits."robust": robust sandwich SE with a Wald CI for both
fits ("sandwich" accepted as an alias)."bootstrap": percentile bootstrap CI with a bootstrap
SE (lnHR_SE = sd(log(HR))); no p-value reported. (Removed
again in 0.4.0: the “auto” dispatch is now the only behavior.)estimate_hr_ir() gained cluster_var,
bootstrap_count, n_cores, and
seed arguments for the robust/bootstrap methods (removed
again in 0.4.0 in favor of the fixed per-block methods and
if_bootstrap_*).estimate_hr_ir() gained an optional
incidence-rate-ratio rate model via irr_method
("none"/"poisson"/"quasipoisson")
with optional irr_covariates; weighted fits used
survey::svyglm(quasipoisson) (reworked in 0.4.0: IRR is
always-on and marginal).estimate_rr_rd() gained a Fine-Gray
subdistribution-hazard regression via fg_regression +
competing_event_var (moved to estimate_hr_ir()
in 0.4.0).estimate_ps() gained a separation diagnostic for the
propensity-score logistic model and uses na.exclude so the
returned PS column aligns with the input rows.create_ps_weights() and
create_ps_matched_cohort() now also emit the unified
PS-distribution figures (density, within-group histogram, and a
histogram+density overlay for the unweighted/pre and weighted/post
samples, plus a weight box plot for the weighting methods) that
create_ps_fs_weights() already produced. The existing
faceted / mirror plots are still written. A new internal helper
.plot_ps_distribution_set() is shared by all three
functions, so the figure style is now consistent across PS methods.
create_ps_fs_weights() figure output is unchanged.create_ps_matched_cohort() gains a
caliper_scale argument controlling the scale on which
matching and the caliper are applied: "logit_ps_sd" matches
on logit(PS) with a caliper of caliper x SD(logit(PS))
(Austin 2011); "raw_ps_sd" matches on PS with a caliper of
caliper x SD(PS); "raw" applies a flat caliper
on the raw PS scale (e.g. caliper = 0.01).caliper x SD(PS)); the new default
(caliper_scale = "logit_ps_sd") matches on logit(PS)
(caliper x SD(logit(PS))). Pass
caliper_scale = "raw_ps_sd" to reproduce the previous
results exactly. The verbose/report label that previously described the
raw-PS caliper as “SD of logit(PS)” is now accurate for each scale.'Excel' per CRAN
software-name convention.Description
field (Rosenbaum and Rubin 1983 doi:10.1093/biomet/70.1.41; Austin 2011 doi:10.1080/00273171.2011.568786; Desai et al. 2017 doi:10.1097/EDE.0000000000000595).estimate_rr_rd(): removed hardcoded RNG seed from the
internal bootstrap helper. New seed argument (default
NULL) lets the user opt in to reproducibility; when
NULL the function does not touch the global RNG state. In
the sequential branch, .Random.seed is saved and restored
on exit when a seed is supplied.expect_message(..., "Auto-excluding.*race_cat"), which
required both substrings in a single message() call, but
the verbose path emits the header and the variable name on separate
lines. Switched to capture_messages() and asserts both
substrings individually.estimate_ps() categorical extreme-distribution
screen now detects zero-count levels via union of levels across exposure
groups. Previously, a level present in only one exposure group was
silently dropped by table() and could slip past the
cnt < 5 rule, triggering quasi-separation in
glm().