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TxEffectsSurvival - Treatment Effect Inference for Terminal and Non-Terminal Events under Competing Risks

Provides several confidence interval and testing procedures, based on either semiparametric (using event-specific win ratios) or nonparametric measures, including the ratio of integrated cumulative hazard (RICH) and the ratio of integrated transformed cumulative hazard (RITCH), for treatment effect inference with terminal and non-terminal events under competing risks. The semiparametric results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 <doi:10.1093/biostatistics/kxv032>) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 <doi:10.1002/sim.70205>).

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1.70 score 1 scripts 361 downloads

YPmodelPhreg - The Short-Term and Long-Term Hazard Ratio Model with Proportional Adjustment

Provides covariate-adjusted comparison of two groups of right censored data, where the binary group variable has separate short-term and long-term effects on the hazard function, while effects of covariates such as age, blood pressure, etc. are proportional on the hazard. The model was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends the two sample version of the short-term and long-term hazard ratio model proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model extends the usual Cox proportional hazards model to allow more flexible hazard ratio patterns, such as gradual onset of effect, diminishing effect, and crossing hazard or survival functions. This package provides the following: 1) point estimates and confidence intervals for model parameters; 2) point estimate and confidence interval of the average hazard ratio; and 3) plots of estimated hazard ratio function with point-wise and simultaneous confidence bands.

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1.70 score 5 scripts 174 downloads

EventWinRatios - Event-Specific Win Ratios for Terminal and Non-Terminal Events

Provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets.

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1.70 score 4 scripts 307 downloads