Wild bootstrap logrank tests with broader power functions for testing superiority

Ditzhaus, Marc; Pauly, Markus

doi:10.1016/j.csda.2019.02.001

Cited by 8 publications

(9 citation statements)

References 43 publications

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“…This class is long known and includes the LR as well as the common Peto-Peto test (PP). Recently, a flexible combination of several weighted LRs into one test procedure was proposed [ 16 – 18 ]. It is based upon a combination of alternatives and carried out as a permutation procedure.…”

Section: Methodsmentioning

confidence: 99%

Which test for crossing survival curves? A user’s guideline

Dormuth

Liu

et al. 2022

BMC Med Res Methodol

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Background The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards. Methods We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards. Results We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible. Conclusions The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.

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Section: Methodsmentioning

confidence: 99%

Which test for crossing survival curves? A user’s guideline

Dormuth

Liu

et al. 2022

BMC Med Res Methodol

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“…However, the weight needs to be calibrated to the typically unknown shape of the alternative and a wrong guess of the weight can lead to a very poor power perfor-mance. To address this tricky question of weight choice and to avoid blind guessing, Brendel et al (2014), Ditzhaus and Pauly (2019), and Ditzhaus and Friedrich (2020) followed a combination approach of different weighted log-rank tests for the two-sample scenario. We extended this approach to general factorial survival designs.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…Moreover, let 𝑤 𝑛1 , … , 𝑤 𝑛𝑘 be the corresponding integrands of the form (3) for the Nelson-Aalen-type integrals. To exclude redundant cases, as too similar or even equal weights, we follow Ditzhaus and Pauly (2019) and Ditzhaus and Friedrich (2020) and restrict to weights fulfilling the following.…”

Section: Combination Of Different Weightsmentioning

confidence: 99%

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CASANOVA: Permutation inference in factorial survival designs

et al. 2021

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We propose inference procedures for general factorial designs with time-to-event endpoints. Similar to additive Aalen models, null hypotheses are formulated in terms of cumulative hazards. Deviations are measured in terms of quadratic forms in Nelson-Aalen-type integrals. Different from existing approaches, this allows to work without restrictive model assumptions as proportional hazards.In particular, crossing survival or hazard curves can be detected without a significant loss of power. For a distribution-free application of the method, a permutation strategy is suggested. The resulting procedures' asymptotic validity is proven and small sample performances are analyzed in extensive simulations. The analysis of a data set on asthma illustrates the applicability.

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“…In case of non-proportional hazards, however, they are not always the most appropriate choice and several alternatives to logrank tests have been proposed. [1][2][3][4][5][6] In factorial survival designs, this issue is additionally hampered by the desire to summarize main treatment and interaction effects in single quantities. For hazard ratios as effect estimates, this can only be achieved under the proportional hazards assumption.…”

Section: Introductionmentioning

confidence: 99%

Inferring median survival differences in general factorial designs via permutation tests

Ditzhaus

Dobler

Pauly

2020

Stat Methods Med Res

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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.

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Wild bootstrap logrank tests with broader power functions for testing superiority

Cited by 8 publications

References 43 publications

Which test for crossing survival curves? A user’s guideline

Which test for crossing survival curves? A user’s guideline

CASANOVA: Permutation inference in factorial survival designs

Inferring median survival differences in general factorial designs via permutation tests

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