Analyzing differences between restricted mean survival time curves using pseudo-values

Ambrogi, Federico; Iacobelli, Simona; Andersen, Per Kragh

doi:10.1186/s12874-022-01559-z

Cited by 6 publications

(6 citation statements)

References 42 publications

(71 reference statements)

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Section: Introductionmentioning

confidence: 99%

“…Several methods have been developed to estimate a difference in RMST for independent data such as the direct integration of the Kaplan-Meier survival curves, 21 Andersen's pseudo-values regression method, [22][23][24] Royston and Parmar's flexible parametric survival model 15,20 as well as methods based on the inverse probability of censoring weighting accounting for covariate-dependent censoring. [25][26][27] Recently, Chen et al 28 adapted to clustered data a method initially proposed by Zhong and Schaubel 27 for independent data that estimates the RMST as a continuous function of the horizon time t* allowing for covariate-dependent censoring.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial

Vilain-Abraham

Tavernier

Dantan

et al. 2023

Stat Methods Med Res

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For time-to-event outcomes, the difference in restricted mean survival time is a measure of the intervention effect, an alternative to the hazard ratio, corresponding to the expected survival duration gain due to the intervention up to a predefined time t*. We extended two existing approaches of restricted mean survival time estimation for independent data to clustered data in the framework of cluster randomized trials: one based on the direct integration of Kaplan-Meier curves and the other based on pseudo-values regression. Then, we conducted a simulation study to assess and compare the statistical performance of the proposed methods, varying the number and size of clusters, the degree of clustering, and the magnitude of the intervention effect under proportional and non-proportional hazards assumption. We found that the extended methods well estimated the variance and controlled the type I error if there was a sufficient number of clusters (≥ 50) under both proportional and non-proportional hazards assumption. For cluster randomized trials with a limited number of clusters (< 50), a permutation test for pseudo-values regression was implemented and corrected the type I error. We also provided a procedure to estimate permutation-based confidence intervals which produced adequate coverage. All the extended methods performed similarly, but the pseudo-values regression offered the possibility to adjust for covariates. Finally, we illustrated each considered method with a cluster randomized trial evaluating the effectiveness of an asthma-control education program.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial

Vilain-Abraham

Tavernier

Dantan

et al. 2023

Stat Methods Med Res

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“…Several covariate adjustment methods for RMST have been proposed. 6–10 Recently, Andersen et al. 8 and Tian et al.…”

Section: Introductionmentioning

confidence: 99%

“…Several covariate adjustment methods for RMST have been proposed. [6][7][8][9][10] Recently, Andersen et al 8 and Tian et al 9 reported covariate adjustment methods that directly model the survival time, avoiding the estimation of nonparametric hazards. Andersen's method uses the leave-one-out technique in the analysis of RMST for censored subjects.…”

Section: Introductionmentioning

confidence: 99%

Variable selection using inverse probability of censoring weighting

Kojima

2023

Stat Methods Med Res

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In this article, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse probability of censoring weighted for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)-type variable selection method, which considers an inverse weighting for of the squared losses, and an information criterion-type variable selection method, which applies an inverse weighting of the survival probability to the power of each density function in the likelihood function. We prove the consistency of the inverse probability of censoring weighted lasso estimator and the maximum inverse probability of censoring weighted likelihood estimator. The performance of the inverse probability of censoring weighted lasso and inverse probability of censoring weighted information criterion are evaluated via a simulation study with six scenarios, and then their variable selection ability is demonstrated using data from two clinical studies. The results confirm that inverse probability of censoring weighted lasso and the inverse probability of censoring weighted likelihood function produce good estimation accuracy and consistent variable selection. We conclude that our two proposed methods are useful variable selection tools for adjusting the censoring information for survival time analyses.

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“…A recent method that uses pseudo‐observations can also be used without parametric assumptions. This method, using a missing data framework, uses a consistent estimator to replace each observation with a pseudo‐observation (i.e., its estimate when it is excluded from the sample) (Andersen et al., 2004; Andersen & Pohar Perme, 2010; Andersen et al., 2017; Ambrogi et al., 2022; Sachs & Gabriel, 2022). There are also parametric and semiparametric approaches that accommodate nonproportionality.…”

Section: Introductionmentioning

confidence: 99%

Analysis of survival data with nonproportional hazards: A comparison of propensity‐score‐weighted methods

et al. 2022

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One of the most common ways researchers compare cancer survival outcomes across treatments from observational data is using Cox regression. This model depends on its underlying assumption of proportional hazards, but in some real‐world cases, such as when comparing different classes of cancer therapies, substantial violations may occur. In this situation, researchers have several alternative methods to choose from, including Cox models with time‐varying hazard ratios; parametric accelerated failure time models; Kaplan–Meier curves; and pseudo‐observations. It is unclear which of these models are likely to perform best in practice. To fill this gap in the literature, we perform a neutral comparison study of candidate approaches. We examine clinically meaningful outcome measures that can be computed and directly compared across each method, namely, survival probability at time T, median survival, and restricted mean survival. To adjust for differences between treatment groups, we use inverse probability of treatment weighting based on the propensity score. We conduct simulation studies under a range of scenarios, and determine the biases, coverages, and standard errors of the average treatment effects for each method. We then demonstrate the use of these approaches using two published observational studies of survival after cancer treatment. The first examines chemotherapy in sarcoma, which has a late treatment effect (i.e., similar survival initially, but after 2 years the chemotherapy group shows a benefit). The other study is a comparison of surgical techniques for kidney cancer, where survival differences are attenuated over time.

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Analyzing differences between restricted mean survival time curves using pseudo-values

Cited by 6 publications

References 42 publications

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial

Variable selection using inverse probability of censoring weighting

Analysis of survival data with nonproportional hazards: A comparison of propensity‐score‐weighted methods

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