An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring

Péron, Julien; Buyse, Marc; Ozenne, Brice; Roche, Laurent; Roy, Pascal

doi:10.1177/0962280216658320

Cited by 51 publications

(99 citation statements)

References 26 publications

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“…Moreover, delays and doubling times may in practice be difficult to measure accurately because of day-to-day perturbations in growth, measurement error and discrete time follow-up. Rank-based comparisons of composite tumour volume or time-to-sacrifice outcomes are a more robust and powerful approach (Péron et al, 2016). However, this does not offer much insight on the tumour growth dynamics and the effect of the treatment.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Stochastic Differential Equation Mixed Effects Models of a Tumour Xenography Study

Picchini

Forman

2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary We consider Bayesian inference for stochastic differential equation mixed effects models (SDEMEMs) exemplifying tumour response to treatment and regrowth in mice. We produce an extensive study on how an SDEMEM can be fitted by using both exact inference based on pseudo‐marginal Markov chain Monte Carlo sampling and approximate inference via Bayesian synthetic likelihood (BSL). We investigate a two‐compartments SDEMEM, corresponding to the fractions of tumour cells killed by and survived on a treatment. Case‐study data consider a tumour xenography study with two treatment groups and one control, each containing 5–8 mice. Results from the case‐study and from simulations indicate that the SDEMEM can reproduce the observed growth patterns and that BSL is a robust tool for inference in SDEMEMs. Finally, we compare the fit of the SDEMEM with a similar ordinary differential equation model. Because of small sample sizes, strong prior information is needed to identify all model parameters in the SDEMEM and it cannot be determined which of the two models is the better in terms of predicting tumour growth curves. In a simulation study we find that with a sample of 17 mice per group BSL can identify all model parameters and distinguish treatment groups.

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Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Stochastic Differential Equation Mixed Effects Models of a Tumour Xenography Study

Picchini

Forman

2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…The win ratio takes into account the clinical importance order among the components, and its simple and intuitive derivation make it attractive, compared with other methods for prioritized multiple outcomes such as weighted composites and rank tests. The net benefit (proportion in favor of treatment) is the difference in win proportion between the two groups, so the two methods are closely related.…”

Section: Introductionmentioning

confidence: 99%

The win ratio: Impact of censoring and follow‐up time and use with nonproportional hazards

Dong¹,

Huang

Chang³

et al. 2019

Pharmaceutical Statistics

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The win ratio has been studied methodologically and applied in data analysis and in designing clinical trials. Researchers have pointed out that the results depend on follow-up time and censoring time, which are sometimes used interchangeably. In this article, we distinguish between follow-up time and censoring time, show theoretically the impact of censoring on the win ratio, and illustrate the impact of follow-up time. We then point out that, if the treatment has long-term benefit from a more important but less frequent endpoint (eg, death), the win ratio can show that benefit by following patients longer, avoiding masking by more frequent but less important outcomes, which occurs in conventional time-to-first-event analyses. For the situation of nonproportional hazards, we demonstrate that the win ratio can be a good alternative to methods such as landmark survival rate, restricted mean survival time, and weighted log-rank tests. KEYWORDS hazard ratio, landmark survival rate, log-rank test, prioritized pairwise comparisons, restricted mean survival time 1 | INTRODUCTIONMuch interest has focused on composite time-to-event endpoints when the component outcomes can be prioritized according to their clinical importance. The win ratio 1 takes into account the clinical importance order among the components, and its simple and intuitive derivation make it attractive, compared with other methods for prioritized multiple outcomes such as weighted composites and rank tests. The net benefit 2-4 (proportion in favor of treatment) is the difference in win proportion between the two groups, so the two methods are closely related.The win ratio method has received much attention in methodological research. 5-14 One possible drawback is that it depends on follow-up time and censoring. [5][6][7][8]15 To reduce the impact of censoring (or follow-up) time, Oakes 7 proposed to calculate the win ratio from the time-to-event information as of a common time, c. Further, Rauch et al 16 argued that the win ratio should not be used in the presence of censoring, and they gave an illustrative example under proportional hazards (see Section 5.2 of Rauch et al 16 ). Also, some discussions of the win ratio have used censoring time and followup time interchangeably in a broad sense.In practice, the win ratio method has been used for many retrospective analyses of clinical trials and applied in study designs. In particular, together with the Finkelstein-Schoenfeld test, 17 the win ratio was used in the primary analysis for the tafamidis Phase III transthyretin amyloid cardiomyopathy (ATTR-ACT) study. 18,19 On the composite primary endpoint of all-cause mortality and frequency of cardiovascular(CV)-related hospitalization, the (stratified) win ratio

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“…For instance, it is unclear what the effect is of adding a threshold of clinical relevance, or the effect of different missingness patterns or censoring on the GPC methods. In the presence of high censoring, the GPC methods may lead to a biased estimator of the treatment effect, which may be corrected with an alternative scoring algorithm . It is noted by Efron and Péron et al that, for example, in the case of unequal censoring distribution, the scoring algorithms of Efron and Péron perform better than the Gehan scoring algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Pairwise comparison methods have been described, which prioritize components of the composite endpoint and methods that do not; both are discussed in this document. Note that some authors have used the term “hierarchy” to denote the importance of the outcomes, but more recent authors have coherently switched to the term “priority” …”

Section: Description Of the Generalized Pairwise Comparison Methodsmentioning

confidence: 99%

“…

Z_{U_{B}} = \frac{U_{B} \sqrt{N}}{\sqrt{{Var}_{U_{B}}}} .

The estimator suggested by Buyse accounts for the number of ties and has received several names, proportion in favor of treatment, global treatment effect, net chance of better outcome, and, more recently, net benefit (personal communication). It reflects the net probability of a better outcome for a patient in the experimental arm compared to a control patient.…”

Section: Description Of the Generalized Pairwise Comparison Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Generalized pairwise comparison methods to analyze (non)prioritized composite endpoints

Verbeeck

Spitzer

Vries

et al. 2019

Statistics in Medicine

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In the analysis of composite endpoints in a clinical trial, time to first event analysis techniques such as the logrank test and Cox proportional hazard test do not take into account the multiplicity, importance, and the severity of events in the composite endpoint. Several generalized pairwise comparison analysis methods have been described recently that do allow to take these aspects into account. These methods have the additional benefit that all types of outcomes can be included, such as longitudinal quantitative outcomes, to evaluate the full treatment effect. Four of the generalized pairwise comparison methods, ie, the Finkelstein-Schoenfeld, the Buyse, unmatched Pocock, and adapted O'Brien test, are summarized. They are compared to each other and to the logrank test by means of simulations while specifically evaluating the effect of correlation between components of the composite endpoint on the power to detect a treatment difference. These simulations show that prioritized generalized pairwise comparison methods perform very similarly, are sensitive to the priority rank of the components in the composite endpoint, and do not measure the true treatment effect from the second priority-ranked component onward. The nonprioritized pairwise comparison test does not suffer from these limitations and correlation affects only its variance. KEYWORDS composite endpoint, generalized pairwise comparison, logrank, net benefit, win ratio Statistics in Medicine. 2019;38:5641-5656.wileyonlinelibrary.com/journal/sim

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An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring

Cited by 51 publications

References 26 publications

Bayesian Inference for Stochastic Differential Equation Mixed Effects Models of a Tumour Xenography Study

Bayesian Inference for Stochastic Differential Equation Mixed Effects Models of a Tumour Xenography Study

The win ratio: Impact of censoring and follow‐up time and use with nonproportional hazards

Generalized pairwise comparison methods to analyze (non)prioritized composite endpoints

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