A comprehensive clinical trial design and analysis package with a focus on non-proportional hazards and weighted log-rank methods, plus broad support for group sequential design, adaptive design, multiplicity, dose-finding, and endpoint-specific power and sample size calculations.
Kaifeng Lu, kaifenglu@gmail.com
For proportional hazards, power is determined by the total number of events and the constant hazard ratio together with information rates and spending functions. For non-proportional hazards, the hazard ratio varies over time and calendar time determines the mean and variance of the weighted log-rank score statistic. The package uses the analytic approach in Lu (2021) and approximates the variance-covariance matrix of sequential statistics under the alternative by that under the null to leverage the independent increments structure in Tsiatis (1982) for the Fleming-Harrington family.
The package capabilities can be grouped as follows:
Time-to-event design under proportional and
non-proportional hazards: weighted log-rank statistics and design
operating characteristics (for example lrstat, lrpower,
lrsamplesize, lrsim, lrschoenfeld), including
equivalence settings, event prediction, accrual modeling, and calendar
time determination (for example accrual, caltime,
nevent, pevent, patrisk, natrisk,
getDurationFromNevents, getNeventsFromHazardRatio).
Group sequential and adaptive designs: standard,
adaptive, multi-arm multi-stage (MAMS), and seamless frameworks,
including boundaries, conditional power, confidence intervals, and
exit probabilities (for example getDesign, adaptDesign,
getBound, getCP, getCI, getRCI,
getADCI, getADRCI, exitprob, and their
_multiarm and _seamless variants).
Fixed and sequential sample size/power across endpoint
types: binary, continuous, ordinal/multinomial, paired,
crossover/carryover, MMRM, repeated measures ANOVA, two-way ANOVA,
Wilcoxon, Fisher's exact, McNemar, one-sample exact, agreement,
logistic regression, and negative binomial endpoints (for example
getDesign*, power*Exact, samplesize*Exact,
kmpower, rmpower, nbpower, and associated
*samplesize and *equiv functions).
Multiplicity and graphical procedures: weighted
Bonferroni and graph-based updates, Bonferroni mixtures of weighted
Simes and Dunnett, and gatekeeping procedures for multiple hypotheses
(for example fadjpbon, fadjpsim, fadjpdun,
fseqbon, fstp2seq, fstdmix, fmodmix,
fwgtmat, updateGraph).
Early-phase and dose-finding tools: Simon's two-stage,
Bayesian Simon designs, mTPI-2, and BOIN utilities
(for example simon2stage, simonBayesAnalysis,
simonBayesSim, mTPI2Table, BOINTable).
Estimation, confidence intervals, and supporting
utilities: Clopper-Pearson, exact risk ratio/risk difference,
Brookmeyer-Crowley survival quantiles, Miettinen-Nurminen and REML
intervals/statistics for stratified measures, Hedges' g,
piecewise-exponential distribution tools, multivariate normal
integration, and model diagnostics
(for example ClopperPearsonCI, riskRatioExactCI,
riskDiffExactCI, survQuantile, mnRiskDiffCI,
remlRiskDiff, hedgesg, dtpwexp, ptpwexp,
qtpwexp, rtpwexp, pbvnorm, pmvnormr,
qmvnormr, phregr, liferegr,
residuals_phregr, survfit_phregr, zph_phregr).
Interactive use: a bundled Shiny interface for
exploratory design workflows (runShinyApp_lrstat).
The development of lrstat was strongly influenced by rpact, with
argument naming aligned where possible for ease of adoption. Key
differences include direct approximation (rather than Schoenfeld-based
approximation) for weighted log-rank design calculations, explicit use of
accrualDuration to define the accrual end, and treatment of final
stage trial outcome when early futility stopping does not occur.
Anastasios A. Tsiatis. Repeated significance testing for a general class of statistics used in censored survival analysis. J Am Stat Assoc. 1982;77:855-861.
Christopher Jennison, Bruce W. Turnbull. Group Sequential Methods with Applications to Clinical Trials. Chapman & Hall/CRC: Boca Raton, 2000, ISBN:0849303168
Kaifeng Lu. Sample size calculation for logrank test and prediction of number of events over time. Pharm Stat. 2021;20:229-244.
rpact, gsDesign
lrpower(kMax = 2, informationRates = c(0.8, 1),
criticalValues = c(2.250, 2.025), accrualIntensity = 20,
piecewiseSurvivalTime = c(0, 6),
lambda1 = c(0.0533, 0.0309), lambda2 = c(0.0533, 0.0533),
gamma1 = 0.00427, gamma2 = 0.00427,
accrualDuration = 22, followupTime = 18)
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