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pa_ interfaces for prediction,
evaluation, calibration, model summaries, sampling weights, plotting and
simulation. Survival methods are adapted from TimeMetric; its historical
release notes are retained below.pa_nlm() for continuous-outcome R-squared and
L-squared, returning unrounded numeric results. Retain
pam.nlm() with its historical field names and four-decimal
character format, using the same calculation core.pa_.
pam.nlm() is the sole supported historical public function
name. The PAmeasures 0.1.0 names pam.censor(),
pam.coxph() and pam.survreg(), the
tm_ interfaces and earlier TimeMetric script aliases are
not exported. Full API compatibility with 0.1.0 is not claimed. The
moore dataset is retained.Three- and four-valued ordinary risks now use linear Cox calibration, matching unit-weighted calibration without spline errors or console output.
Saved-result upgrades defer manuscript publication until PBC, Framingham and GBSG all contain ICI. Explicit input paths work without a published-run manifest. A simulated end-to-end migration test preserves prior metrics, original files and the active manifest until the upgrade is complete.
Added tm_calibration() for right-censored event
probabilities and tm_calibration_cr() for a target-cause
CIF, both at one specified horizon and without requiring the original
prediction model. Results contain ICI, E50 and E90 plus calibrated
probabilities in original subject order.
Right-censored calibration uses pmcalibration
restricted cubic splines; competing-risks calibration uses
prodlim nearest-neighborhood Aalen–Johansen estimates. Both
use pmcalibration::cal_metrics() for the summaries,
retaining repeated predictions with their subject frequencies.
pmcalibration and prodlim are now declared
runtime dependencies.
Ordinary and competing-risks evaluators and comparison tables
include ICI by default. E50/E90 are opt-in
(metrics = c("ici", "e50", "e90") or "all").
tm_fit_and_eval() now defaults to
metrics = NULL for this same ICI-only calibration
selection; explicit "all" includes all three.
calibration_args forwards knot/bandwidth settings to the
new functions.
Calibration uses the requested outcome horizon and the last supplied prediction-grid value at or before it. Direct probability interfaces and fit-and-evaluate can use predictions calculated exactly at the horizon. Zero/one probabilities are never silently dropped; ordinary survival fitting clips them inward with a warning and summaries use the originals. Non-estimable calibration fits warn and leave other metrics available. Calibration confidence intervals are not included.
Right-censored two-phase evaluation now includes ICI by default,
with E50/E90 available explicitly or via "all".
tm_calibration(case_weights = ...) fits a sampling-weighted
Cox spline model and computes weighted summaries;
tm_evaluate_two_phase() and tm_sample_design()
forward aligned sampling weights and calibration_args.
Calibration-only calls need no original prediction model, mean-time
predictions, or separate censoring fit. Weighted constant predictions
use Kaplan-Meier; all fits retain original boundary probabilities for
metrics. Hmisc is a direct dependency for the shared spline
basis and weighted quantiles. Design-based intervals and
sampling-adjusted competing-risks calibration are not
implemented.
Simulation 1 now uses common random numbers: all 16 scenarios
share the training seed 1234 (as in Simulation 2) and test seed r for
repetition r, replacing scenario-specific seeds. Its Cox training fits
use timefix = FALSE, because with 1/v = 2 the default
merged thousands of distinct times into ties and shrank the beta = 1.5
coefficients by about 16%. Simulation 3 keeps the Simulation 1 beta = 1,
v = 1 model, which is now the seed-1234 fit.
Figure 6 is drawn as a final figure when its only missing values
are Simulation 3 ICI repetitions with no follow-up at the horizon. Those
boxes use the finite repetitions; the cells and counts are written to
paper.fig6.partial_repetitions.csv (n = 300, 70% censoring,
90% horizon: 52/100) for the manuscript caption.
Simulation ICI now requests fitted-model probabilities at exactly its outcome horizon, rather than the preceding test-follow-up grid point. Other metrics retain their existing evaluation procedures. The Simulation 3 reference remains the mean across the uncensored n=3000 repetitions. A paired replay script compares saved and corrected ICI without refitting prediction models.
Simulation 1, both Simulation 2 parts and Simulation 3 now include ICI at their existing evaluation horizons, with five-knot Cox spline calibration. ICI is stored without rounding and enters replication summaries, Simulation 3 references/errors, figure panels and report tables. Calibration diagnostics are saved per job and model. Resume and report checks require ICI rows. Existing simulation outputs must be kept in a separate directory.
Competing-risks C-index now uses reciprocal censoring-survival products, following Wolbers et al. (2014), equation (3.4). Previously it multiplied comparable pairs by those products. Only contributing pairs are inverted, avoiding undefined weights in unused rows after the evaluation horizon. Existing competing-risks C-index results require reevaluation.
Point R-squared now includes target events at the evaluation time
(T <= t) in both survival and competing-risks
evaluation, matching the manuscript. Ordinary survival point R-squared
compares the event indicator with 1 - S rather than
S; this changes its adequacy and pseudo-R-squared
values.
Competing-risks generalized R-squared now uses the
event-observation IPCW strategy in
PA measure new/pam.survreg.update.R: original follow-up
times estimate censoring and evaluate weights, and only observed events
of any cause contribute. Both interval and point versions expose Cox
(default, optionally conditional) and KM censoring estimates, with a
probability floor of 1e-4. Late censoring remains zero-weight by design.
The adapted Cox baseline/predictor centering and tied-time left limits
are kept consistent. These changes alter CR R-squared results; saved
tables need recomputation.
Cox predictions now obtain the baseline hazard exclusively from
the training fit. Validation labels cannot change survival probabilities
on a fixed grid. Training weights, centering, ties, factors and strata
follow survival. The legacy evaluation-grid integration
remains explicit; this repair does not claim that unrestricted mean
survival is identified. Offset models use the same weighted
training-offset centering as the baseline; reference tests cover
in-sample and validation predictions, case weights, strata, and Efron,
Breslow and exact tie methods.
Matched NCC weights exclude cases from other matching strata.
Each case is a sampling occasion, including tied cases; small risk sets
provide all available controls. m remains the number of
controls per case, with full-cohort input. Simultaneous cases are
excluded from control draws and events precede censoring at ties. Paper
examples and tests use this convention.
IPCW weights for ordinary survival R-squared, point R-squared and the Cox/AFT prediction helpers now use one censoring left limit G(t-) for all rows tied at t. Previously, results with tied times depended on row order, and point R-squared was off when a subject was censored exactly at t*. Data with tied times can give slightly different values.
The competing-risks Cox censoring model uses Breslow ties, so the no-covariate model equals exp(-Nelson-Aalen) with tied censoring times.
tm_predict_coxph() supports strata-by-covariate
interactions such as x * strata(g) when
new_data is given, and no longer repeats survival’s
interaction warning on every call.
tm_fit_and_eval() computes R-squared only when
requested. Cox/AFT R-squared with undefined censoring weights returns
missing values with a suppressible diagnostic instead of recycling
shorter calibration vectors.
Added independent prediction/inclusion-probability checks and
paper analysis scripts. PBC is compared before/after within 0.2.1; full
simulation execution is an explicit separate action. See the paper
workflow in README.md.
Resubmission addressing the CRAN review of 0.2.0 and correcting numerical errors found during independent validation. Several metric values change; analyses using previous versions should be rerun.
DESCRIPTION: the Description no longer starts with the
package name, quotes software names, spells out AUC, and cites the
methods it implements with DOIs. The predecessor package is now
correctly named ‘PAmeasures’.tm_predict_coxph() and
tm_predict_survreg() default to predict = TRUE
rather than predict = T; T/F are
no longer used anywhere in the code.print() or
cat(). A failed model fit inside
tm_predict_cif() and a failed weighted least-squares fit
inside tm_predict_survreg() now raise a warning.tm_fit_and_eval() now calls
survivalROC::survivalROC() directly instead of shipping a
copy of that function’s GPL-licensed source, and ‘survivalROC’ is added
to Imports.tm_simulate_fine_gray() and
tm_plot_summary() no longer reset the random number
generator by default. Their seed argument now defaults to
NULL; pass a seed to get the previous reproducible
behavior.tm_fit_and_eval() excludes incomplete
training/evaluation rows with a suppressible warning before invoking any
metric backend. This also prevents unsuppressible missing-value output
from ‘survivalROC’.td_auc can now be requested without also
requesting brier_score.tm_fit_and_eval() matches values to metric names when a
subset is supplied in a different order. Previously it could silently
label a Brier score as concordance (or vice versa). Unsupported models
and metrics now give clear errors.tm_predict_survreg() retains the covariate column when
predicting from a data frame containing a single predictor.survival::survfit(). Previously the baseline was
re-estimated using validation outcomes, leaking information and ignoring
fitted case weights.1 - survival), including events at the
evaluation horizon. Point calibration-component and pseudo R-squared
values consequently change.tm_fit_and_eval() orients AFT scores by their model
meaning and retains AUC values below 0.5. Previously
max(AUC, 1 - AUC) could make reversed held-out predictions
appear highly discriminative.m as the documented number of
controls per case. Sampling opportunities are restricted to cases in the
matching stratum; tied failures and censoring follow the documented
risk-set convention. NCC metric values and finite-horizon AFT results
change as a consequence. Previously reported manuscript results need to
be regenerated.Package-quality release preparing resubmission to JOSS. The evaluation methodology is unchanged; metric values are identical to 0.1.0 except where a defect is listed below.
randomForestSRC in NAMESPACE without declaring
it anywhere in DESCRIPTION, so R CMD INSTALL
failed on any machine that did not already have it and
remotes::install_github() could not succeed.rms,
survminer, expint, pec,
magrittr, purrr, tibble and
utils were used but undeclared.randomForestSRC and cmprsk moved to
Suggests as optional backends of
tm_predict_cif(), each guarded with an informative error
naming the package to install.rms is no longer a dependency. It was
used only to fit a model for the since-withdrawn r_sh
estimator and to extract linear predictors, both of which are now
survival calls. Because rms requires R >=
4.4.0, removing it lowers the declared minimum from R 4.4.0 to R
>= 4.1.0, the floor set by survival and
dplyr.Every exported function now carries a tm_ prefix,
correcting the survial and surverg
misspellings and dropping the pam. prefix inherited from
PAmeasures. All previous names still work and forward to their
replacement with a deprecation warning; see
?"TimeMetric-deprecated".
tm_evaluate_two_phase() is newly
exported. The case-cohort and nested case-control functionality
was previously internal and unreachable by users.tm_metric_names() returns the canonical metric
identifiers.r_sh and r_e from the pre-release
API following a maintainer decision to exclude metrics that are not
sufficiently validated for the first CRAN release. Requesting either
name, or any of the legacy spellings R_sh, R_E
and R_sph, now raises an error naming the withdrawal.tm_metric_names() consequently returns 11 canonical
names rather than 13.Point-in-time metrics could differ between platforms, and
their values have changed. The evaluation time was chosen with
which.min(abs(event_time - t_star)). When
t_star is the median of an even-length vector the two
middle observed times are mathematically equidistant from it, so the
winner was decided by a rounding difference in the last bit of the
result – which falls differently on platforms with 80-bit
long double (x86_64 Linux and Windows) than on those where
it is plain double (arm64 macOS). The same data therefore produced
different pseudo_r2_point, r2_point,
l2_point, brier_score and td_auc
on different machines.
The rule is now nearest observed time with ties broken toward the earlier event time, which is also independent of input row order. All metrics now agree bit-for-bit across arm64 macOS, x86_64 Linux and x86_64 Windows. Integrated and concordance metrics were never affected.
The Brier score’s censoring weights were wrong, and
brier_score from tm_fit_and_eval() changes on
data with tied times. Gt(), the Kaplan-Meier
estimate of the censoring distribution behind the
inverse-probability-of-censoring weights, had three independent defects:
it derived interpolation indices from a sorted table but applied them to
the raw unsorted input, so G(t) was neither monotone nor invariant to
row order; it identified censoring events as
status == min(status), which inverts on data with no
censoring and made G decay from 1 instead of staying at 1; and it
silently replaced a zero censoring survival with the smallest positive
value, concealing an undefined weight.
Gt() is now a reverse-Kaplan-Meier step
function with no interpolation between jump times, using G(t)
for the evaluation-time weight and G(t-) for subject-specific event-time
weights, and the reverse-Kaplan-Meier tie convention. It returns
NA beyond the last observed time and
errors when the censoring distribution is exhausted,
rather than substituting a value. Values agree exactly with
pec::ipcw().
Reported Brier scores on tied data at an evaluation time between jump
times were substantially overstated: on one 200-observation fixture the
reported value was 0.75 against a correct 0.22. Metrics other than
brier_score are unaffected, as is
tm_survival_eval(), which computes the Brier score by a
different route.
tm_fit_and_eval() (was
pam.survival_eval()) never worked. It
passed covariates=/newdata= to functions
taking covs=/new_data=, so every call failed
with unused arguments.
tm_plot_pred() (was plot_pred())
returned a silently empty plot on its documented
default call. sample_index defaulted to NULL
and the function then subset with x[NULL].
NULL now means “plot every subject”.
survival::concordancefit() was called without being
imported, so the primary evaluation function failed under
library(TimeMetric) alone. It worked only if the user
happened to have attached survival separately.
tm_survival_eval_cr() rejected every legacy
metric spelling. It carried a duplicate metric-validation block
that ran before name normalization, so
"Harrells_C", "C_index" and
"Brier Score" all failed on the competing-risks path with
Invalid metrics: while working on every other entry point.
Validation now runs after normalization, as it does elsewhere, and the
documented legacy spellings are accepted there too. Genuinely unknown
names are still rejected.
tm_survival_eval_cr() returned its
Value column as character while
tm_survival_eval() returned numeric, so
is.finite() was silently FALSE for every
competing-risks value. Both now return numeric.
Standardized to one ASCII identifier per quantity. Legacy spellings are still accepted, case-insensitively and tolerant of apostrophe style, with a deprecation warning:
brier_score, c_index,
harrell_c, l_square, l2_point,
pseudo_r2, pseudo_r2_point,
r_square, r2_point, td_auc,
uno_c
Harrell's C and Uno's C previously
required a U+2019 curly apostrophe to match, which made them close to
impossible to discover.Pesudo_R. pseudo_r2 and
pseudo_r2_point remain distinct measures.td_auc.Thirteen unreachable functions were deleted after a call-graph analysis and, for the metric implementations, an equivalence audit against the original Stare-Perme-Henderson reference code:
pam.rsph_metric() and pam.rsh_metric()
were duplicate implementations of the two metrics that have since been
withdrawn entirely (see Removed metrics).pam.Brier_metric() duplicated the Brier score already
computed by a weight-aware implementation.pam.prediction_survial_eval(),
pam.prediction_metrics(),
pam.prediction_metrics_cr(), pam.coxph(),
pam.nlm(), pam.survreg() and related helpers
were unreachable from any export.testthat suite: 498 assertions across 100 tests,
none skipped.r_sh / r_e withdrawal, across all four public
evaluators including the competing-risks and two-phase paths.R-CMD-check across five
platform/version combinations, a full-dependency job that fails if any
test skips, test coverage, and the existing JOSS draft-PDF build, whose
path trigger had pointed at a filename that never existed.R CMD check --as-cran reports 0 errors and 0
warnings.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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