Sample size formulae for two-stage randomized trials with survival outcomes

Li, Zhiguo; Murphy, Susan A.

doi:10.1093/biomet/asr019

Cited by 32 publications

(42 citation statements)

References 25 publications

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“…It is necessary to point out that, although we assume that there are two decision points (two stages), our method generalizes to multiple stages straightforwardly, as also pointed out by . If there are multiple stages, the only difference is in the weight function.…”

Section: Weighted Logrank Test For Observational Studiesmentioning

confidence: 97%

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A global logrank test for adaptive treatment strategies based on observational studies

Valenstein

Pfeiffer

et al. 2013

Statistics in Medicine

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In studying adaptive treatment strategies, a natural question that is of paramount interest is whether there is any significant difference among all possible treatment strategies. When the outcome variable of interest is time-to-event, we propose an inverse probability weighted logrank test for testing the equivalence of a fixed set of pre-specified adaptive treatment strategies based on data from an observational study. The weights take into account both the possible selection bias in an observational study and the fact that the same subject may be consistent with more than one treatment strategy. The asymptotic distribution of the weighted logrank statistic under the null hypothesis is obtained. We show that, in an observational study where the treatment selection probabilities need to be estimated, the estimation of these probabilities does not have an effect on the asymptotic distribution of the weighted logrank statistic, as long as the estimation of the parameters in the models for these probabilities is n-consistent. Finite sample performance of the test is assessed via a simulation study. We also show in the simulation that the test can be pretty robust to misspecification of the models for the probabilities of treatment selection. The method is applied to analyze data on antidepressant adherence time from an observational database maintained at the Department of Veterans Affairs’ Serious Mental Illness Treatment Research and Evaluation Center.

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Section: Weighted Logrank Test For Observational Studiesmentioning

confidence: 97%

“…where

R (t) MathClass-rel= I (S ⩽ normalmin (t MathClass-punc, T MathClass-punc, C))

. The reason that this weight is more efficient is discussed in . For simplicity of presentation, in this section, we assume that the constant weight is used.…”

Section: Weighted Logrank Test For Observational Studiesmentioning

confidence: 99%

A global logrank test for adaptive treatment strategies based on observational studies

Valenstein

Pfeiffer

et al. 2013

Statistics in Medicine

View full text Add to dashboard Cite

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“…Much work has concerned survival endpoints [12, 13, 14, 36]. Relevant sample size formulae can be found in Feng and Wahed [37] and Li and Murphy [38]. A web application for sample size calculation in this case can be found at http://methodologymedia.psu.edu/logranktest/samplesize.…”

Section: Data Sources For Constructing Dtrsmentioning

confidence: 99%

Dynamic Treatment Regimes

Chakraborty

Murphy

2014

Annu. Rev. Stat. Appl.

185

152

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A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes – informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.

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“…Of course, these non-simultaneous confidence intervals will not confirm that one regime is best. In fact, much of the current work on sample size planning for SMARTs (Feng and Wahed 2008; Feng and Wahed 2009; Oetting et al 2010; Li and Murphy 2011) does not focus on devising sample size formulae that control the experiment-wise error rate. Rather, the focus has been on formulae that control the Type I error of a pre-specified primary aim that aides in building an effective DTR.…”

Section: Designing Smart Studiesmentioning

confidence: 99%