Estimation and modeling of the restricted mean time lost in the presence of competing risks

Conner, Sarah C.; Trinquart, Ludovic

doi:10.1002/sim.8896

Cited by 15 publications

(12 citation statements)

References 43 publications

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“…The cumulative probabilities of transplantation or death on the waiting list were obtained by using the Aalen-Johannsen estimator 19 and the RMST on the waiting list (RMWL) by the area above the sum of the cumulative probability of transplantation and death before transplantation. 20 For each scenario, the previous simulations were performed 1000 times to obtain the 95% confidence intervals (CIs). The statistical analyses were performed with R, version 4.1.2.…”

Section: Estimations Of the Moratorium Impactmentioning

confidence: 99%

Impact of the Kidney Transplantation Moratorium in France Because of the COVID-19 Pandemic: A Cohort-based Study

et al. 2022

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Original Clinical Science-General Background. The COVID-19 pandemic has resulted in worldwide kidney transplantation (KT) moratoriums. The impacts of these moratoriums on the life expectancy of KT candidates remain unclear. Methods. We simulated the evolution of several French candidate populations for KT using a multistate semi-Markovian approach and according to moratorium durations ranging from 0 to 24 mo. The transition rates were modeled from the 63 927 French patients who began dialysis or were registered on the waiting list for KT between 2011 and 2019. Results. Among the 8350 patients active on the waiting list at the time of the French KT moratorium decided on March 16, 2020, for 2.5 mo, we predicted 4.0 additional months (confidence interval [CI], 2.8-5.0) on the waiting list and 42 additional deaths (CI, -70 to 150) up to March 16, 2030, compared with the scenario without moratorium. In this population, we reported a significant impact for a 9-mo moratorium duration: 135 attributable deaths (CI, 31-257) up to March 16, 2030. Patients who became active on the list after March 2020 were less impacted; there was a significant impact for an 18-mo moratorium (175 additional deaths [CI,) in the 10 862 prevalent end-stage renal disease patients on March 16, 2020 and for a 24-mo moratorium (189 additional deaths [CI,) in the 16 355 incident end-stage renal disease patients after this date. Conclusion. The temporary moratorium of KT during a COVID-19 peak represents a sustainable decision to free up hospitals' resources if the moratorium does not exceed a prolonged period.

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Section: Estimations Of the Moratorium Impactmentioning

confidence: 99%

Impact of the Kidney Transplantation Moratorium in France Because of the COVID-19 Pandemic: A Cohort-based Study

et al. 2022

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“…4,5 More classical and known effect sizes as landmark survival 6 and the median survival time [7][8][9] provide rather a snapshot for a time point than information about the complete Kaplan-Meier curves. This may be one of the reasons why the restricted mean survival time (RMST), [10][11][12][13][14][15][16] the integral of the Kaplan-Meier-curve over a clinically relevant time window, gets more and more attention lately. Since this summary measure is "arguably more helpful for clinical decision-making and more easily understood by patients," 17 methods based on it "should be routinely reported in randomized trials with time-to-event outcomes."…”

Section: Introductionmentioning

confidence: 99%

Studentized permutation method for comparing two restricted mean survival times with small sample from randomized trials

Ditzhaus

2023

Statistics in Medicine

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Recent observations, especially in cancer immunotherapy clinical trials with time-to-event outcomes, show that the commonly used proportional hazard assumption is often not justifiable, hampering an appropriate analysis of the data by hazard ratios. An attractive alternative advocated is given by the restricted mean survival time (RMST), which does not rely on any model assumption and can always be interpreted intuitively. Since methods for the RMST based on asymptotic theory suffer from inflated type-I error under small sample sizes, a permutation test was proposed recently leading to more convincing results in simulations. However, classical permutation strategies require an exchangeable data setup between comparison groups which may be limiting in practice. Besides, it is not possible to invert related testing procedures to obtain valid confidence intervals, which can provide more in-depth information. In this paper, we address these limitations by proposing a studentized permutation test as well as respective permutation-based confidence intervals. In an extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relatively small sample sizes and unbalanced groups. Finally, we illustrate the application of the proposed method by re-analyzing data from a recent lung cancer clinical trial.

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“…To account for covariates, Andersen 13 described a generalized linear model for the cause-specific RMTL using pseudo-observations. Conner and Trinquart 14 described an inverse probability of censoring a weighted regression model for the RMTL. In this paper, we focus on causal inference for the difference in RMTL.…”

Section: Introductionmentioning

confidence: 99%

Doubly-robust estimator of the difference in restricted mean times lost with competing risks data

Lin

Trinquart

2022

Stat Methods Med Res

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In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time lost to a specific cause of failure between treatment groups. In non-randomized studies, the average causal effect is conventionally used for decision-making about treatment and public health policies. We show how the difference in RMTL can be estimated by contrasting the integrated cumulative incidence functions from a Fine-Gray model. We also show how the difference in RMTL can be estimated by using inverse probability of treatment weighting and contrasts between weighted non-parametric estimators of the area below the cumulative incidence. We use pseudo-observation approaches to estimate both component models and we integrate them into a doubly-robust estimator. We demonstrate that this estimator is consistent when either component is correctly specified. We conduct simulation studies to assess its finite-sample performance and demonstrate its inherited consistency property from its component models. We also examine the performance of this estimator under varying degrees of covariate overlap and under a model misspecification of nonlinearity. We apply the proposed method to assess biomarker-treatment interaction in subpopulations of the POPLAR and OAK randomized controlled trials of second-line therapy for advanced non-small-cell lung cancer.

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Estimation and modeling of the restricted mean time lost in the presence of competing risks

Cited by 15 publications

References 43 publications

Impact of the Kidney Transplantation Moratorium in France Because of the COVID-19 Pandemic: A Cohort-based Study

Impact of the Kidney Transplantation Moratorium in France Because of the COVID-19 Pandemic: A Cohort-based Study

Studentized permutation method for comparing two restricted mean survival times with small sample from randomized trials

Doubly-robust estimator of the difference in restricted mean times lost with competing risks data

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