Robust group sequential designs for trials with survival endpoints and delayed response

Ghosh, Pranab; Ristl, Robin; König, Franz; Posch, Martin; Jennison, Christopher; Götte, Heiko; Schueler, Armin; Mehta, Cyrus R.

doi:10.1002/bimj.202000169

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Also, weighted logrank statistics may be included in the inference framework as a further extension. One could also consider performing interim analyses to allow for early stopping 14 , 50 and adaptations such as sample size reassessment 51 and modification of the set of parameters, for example, adding milestone analyses at later time points. Depending on which type of data is considered for the adaptations, 51 , 52 appropriate adaptive tests have to be implemented.…”

Section: Discussionmentioning

confidence: 99%

“…8,9 If the pattern of non-proportional hazards is not well known beforehand, a maximum combination (MaxCombo) test of differently weighted logrank tests has been shown to be a robustly powerful method. 5,[10][11][12][13][14] However, in the absence of further assumptions, a significant result for these tests, at first, only implies that there exists a time point for which the hazard function under treatment is less than the hazard function under control. Whether this result translates into a relevant (or in fact into any 9 ) benefit in terms of the survival functions needs to be assessed by estimates for parameters that appropriately quantify the differences between survival functions.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

Ristl,

Götte,

Schüler

et al. 2024

Stat Methods Med Res

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Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

Ristl,

Götte,

Schüler

et al. 2024

Stat Methods Med Res

Self Cite

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“…As discussed above, this may be reasonable when survival curves are anticipated to separate at around

t = 6

(months), or where the survival probability is somewhere in the region of 0.75. We choose not to test other values of

t^*

(or

s^*

) in this simulation study because its purpose is not to assess the sensitivity of the modestly weighted log‐rank test to the choice of

t^*

(or

s^*

), which has already been shown to be robust (Ghosh et al., 2021; Magirr & Burman, 2019; Magirr, 2021).…”

Section: Simulation Studymentioning

confidence: 99%

Stratified modestly weighted log‐rank tests in settings with an anticipated delayed separation of survival curves

Magirr

Jiménez

2023

Biometrical J

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Delayed separation of survival curves is a common occurrence in confirmatory studies in immuno‐oncology. Many novel statistical methods that aim to efficiently capture potential long‐term survival improvements have been proposed in recent years. However, the vast majority do not consider stratification, which is a major limitation considering that most large confirmatory studies currently employ a stratified primary analysis. In this article, we combine recently proposed weighted log‐rank tests that have been designed to work well under a delayed separation of survival curves, with stratification by a baseline variable. The aim is to increase the efficiency of the test when the stratifying variable is highly prognostic for survival. As there are many potential ways to combine the two techniques, we compare several possibilities in an extensive simulation study. We also apply the techniques retrospectively to two recent randomized clinical trials.

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“…Other computation‐based approaches for the max‐combo test of Fleming‐Harrington class of weighted log‐rank tests have been studied in Ghosh et al 19 and Wang et al 20 The first paper uses the covariance under the null hypothesis for the design, but this approximation is correct asymptotically only under the local alternative assumption. The second paper calculates the covariance under alternative hypothesis via an interpolation approximation approach.…”

Section: Study Design Based On Max‐combo Testmentioning

confidence: 99%

A MCP‐Mod approach to designing and analyzing survival trials with potential non‐proportional hazards

Luo

Sun

2022

Pharmaceutical Statistics

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Non‐proportional hazards have been observed in many studies especially in immuno‐oncology clinical trials. Traditional analysis using the combined approach with log‐rank test as the significance test and Cox model for treatment effect estimation becomes questionable as this approach relies heavily on the proportional hazards assumption. Inspired by the MCP‐Mod (multiple comparisons and modeling approach) that has been widely used in dose‐finding studies, we propose a similar approach to handle non‐proportional hazards. Using this approach, efficacy signal is first established by a max‐combo test, after which hazard ratios across time will be estimated using a logically nested splines model. Simulations studies and real‐data examples are used to illustrate the use of this approach.

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Robust group sequential designs for trials with survival endpoints and delayed response

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article could fully be reproduced.

Cited by 5 publications

References 17 publications

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

Stratified modestly weighted log‐rank tests in settings with an anticipated delayed separation of survival curves

A MCP‐Mod approach to designing and analyzing survival trials with potential non‐proportional hazards

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