Factorial analyses of treatment effects under independent right-censoring

Dobler, Dennis; Pauly, Markus

doi:10.1177/0962280219831316

Cited by 18 publications

(35 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More general study designs may be part of future research. For that purpose, we can follow Dobler and Pauly (2020) and Ditzhaus et al (2020a), who recently discussed permutationbased inference for the concordance measure and median survival times, respectively, in the general context of factorial designs. Sample size determination can also be developed, in parallel to the asymptotic test based results (Ye and Yu, 2018)…”

Section: Discussion and Remarksmentioning

confidence: 99%

Studentized Permutation Method for Comparing Restricted Mean Survival Times with Small Sample from Randomized Trials

Ditzhaus,

Yu,

2021

Preprint

View full text Add to dashboard Cite

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 analyse 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. As pointed out recently by Horiguchi and Uno (2020a), methods for the RMST based on asymptotic theory suffer from inflated type-I error under small sample sizes. To overcome this problem, they suggested a permutation strategy leading to more convincing results in simulations. However, their proposal requires an exchangeable data set-up between comparison groups which may be limiting in practice. In addition, it is not possible to invert their testing procedure 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 the corresponding permutation-based confidence intervals. In our extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relative small sample sizes and unbalanced groups. Finally we illustrate the application of the proposed method by re-analysing data from a recent lung cancer clinical trial.

show abstract

Section: Discussion and Remarksmentioning

confidence: 99%

Studentized Permutation Method for Comparing Restricted Mean Survival Times with Small Sample from Randomized Trials

Ditzhaus,

Yu,

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As this can be hard to verify or simply wrong, some alternatives to hazard ratios as effect sizes are desired that provide “ clinically meaningful outcomes for (…) patients ” and for “ discussing the value of clinical trials with patients ”. 7 Proposed alternatives cover average hazard ratios, 8,9 concordance and Mann–Whitney effects, 10–12 the (restricted) mean survival, 13,14 or median survival times (MSTs). 15–17…”

Section: Introductionmentioning

confidence: 99%

“…Except for Mann–Whitney effects and concordance odds, 11,18 statistical tools for inferring these quantities have mostly been developed for special two or multiple sample settings. Applying these methods to factorial designs with at least two crossed factors, would neither use the factorial structure to full capacity nor allow for the quantification of potential interaction effects.…”

Section: Introductionmentioning

confidence: 99%

“…7 Proposed alternatives cover average hazard ratios, 8,9 concordance and Mann-Whitney effects, [10][11][12] the (restricted) mean survival, 13,14 or median survival times (MSTs). [15][16][17] Except for Mann-Whitney effects and concordance odds, 11,18 statistical tools for inferring these quantities have mostly been developed for special two or multiple sample settings. Applying these methods to factorial designs with at least two crossed factors, would neither use the factorial structure to full capacity nor allow for the quantification of potential interaction effects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inferring median survival differences in general factorial designs via permutation tests

Ditzhaus

Dobler

Pauly

2020

Stat Methods Med Res

Self Cite

View full text Add to dashboard Cite

Factorial survival designs with right-censored observations are commonly inferred by Cox regression and explained by means of hazard ratios. However, in case of non-proportional hazards, their interpretation can become cumbersome; especially for clinicians. We therefore offer an alternative: median survival times are used to estimate treatment and interaction effects and null hypotheses are formulated in contrasts of their population versions. Permutation-based tests and confidence regions are proposed and shown to be asymptotically valid. Their type-1 error control and power behavior are investigated in extensive simulations, showing the new methods’ wide applicability. The latter is complemented by an illustrative data analysis.

show abstract

“…The finite exactness cannot be preserved but permuted studentized statistics were shown to be still asymptotically exact for various non-exchangeable two-sample scenarios (Neuhaus, 1993;Janssen, 1997;Janssen and Pauls, 2003;Pauly, 2011). Recently, the success story of this idea has been continued in the framework of oneway layouts Romano, 2013, 2016) and even general factorial designs (Pauly et al, 2015;Friedrich et al, 2017;Smaga, 2017;Harrar et al, 2019;Ditzhaus et al, 2019;Dobler and Pauly, 2019).…”

Section: Introductionmentioning

confidence: 99%

Permutation test for the multivariate coefficient of variation in factorial designs

Ditzhaus¹,

Łukas²

2020

Preprint

View full text Add to dashboard Cite

New inference methods for the multivariate coefficient of variation and its reciprocal, the standardized mean, are presented. While there are various testing procedures for both parameters in the univariate case, it is less known how to do inference in the multivariate setting appropriately. There are some existing procedures but they rely on restrictive assumptions on the underlying distributions. We tackle this problem by applying Wald-type statistics in the context of general, potentially heteroscedastic factorial designs. In addition to the k-sample case, higher-way layouts can be incorporated into this framework allowing the discussion of main and interaction effects. The resulting procedures are shown to be asymptotically valid under the null hypothesis and consistent under general alternatives. To improve the finite sample performance, we suggest permutation versions of the tests and shown that the tests' asymptotic properties can be transferred to them. An exhaustive simulation study compares the new tests, their permutation counterparts and existing methods. To further analyse the differences between the tests, we conduct two illustrative real data examples.

show abstract

Factorial analyses of treatment effects under independent right-censoring

Cited by 18 publications

References 54 publications

Studentized Permutation Method for Comparing Restricted Mean Survival Times with Small Sample from Randomized Trials

Studentized Permutation Method for Comparing Restricted Mean Survival Times with Small Sample from Randomized Trials

Inferring median survival differences in general factorial designs via permutation tests

Permutation test for the multivariate coefficient of variation in factorial designs

Contact Info

Product

Resources

About