Improving testing and description of treatment effect in clinical trials with survival outcomes

Yang, Song

doi:10.1002/sim.7676

Cited by 9 publications

(11 citation statements)

References 36 publications

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“…When the event rate is low, the RMST ratio may result in extreme value close to 1 and therefore is not very intuitive. Instead, the ratio of restricted mean times lost, RMTL( )= can be used [37]. Similar procedures can be used for RMTL( ).…”

Section: Discussionmentioning

confidence: 99%

Dynamic RMST Curves for Survival Analysis in Clinical Trials

Liao

Liu

2020

Preprint

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Background: The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the PH assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered. Methods: The dynamic RMST curve using a mixture model is proposed in this paper to fully enhance the RMST method for survival analysis in clinical trials. It is constructed that the RMST difference or ratio is computed over a range of values to the restriction time τ which traces out an evolving treatment effect profile over time.Results: This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this proposal is illustrated through three real examples. Conclusions: The RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve also allows ones for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may be used for determining an appropriate time point for an interim analysis, and the data monitoring committee (DMC) can use this evaluation tool for study recommendation.

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Section: Discussionmentioning

confidence: 99%

Dynamic RMST Curves for Survival Analysis in Clinical Trials

Liao

Liu

2020

Preprint

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“…The hazard ratio based on the Cox proportional hazards model has been the most popular summary measure of effect size. Yang gave an overview of some recently developed measures such as the restricted mean survival time that can be used whether or not the proportional hazards assumption is valid. If confidence intervals for a given summary measure are to be given following each interim look, proper adjustment is needed to have simultaneous 95% coverage probability for all intervals .…”

Section: Discussionmentioning

confidence: 99%

Interim monitoring using the adaptively weighted log‐rank test in clinical trials for survival outcomes

Yang

2018

Statistics in Medicine

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For testing treatment effect with survival data, the log-rank test has been the method of choice and enjoys an optimality property under proportional hazards alternatives. However, there can be significant loss of power in a variety of nonproportional situations. Yang and Prentice proposed an adaptively weighted log-rank test that improves the power of the log-rank test over a wide range of hazard ratio scenarios. In clinical trials, the data and safety monitoring board typically monitors the trial results periodically. Here, we develop interim testing procedures for the adaptively weighted log-rank test. We establish the asymptotic distribution of the test statistics at the interim looks and use a resampling method to obtain the stopping boundaries. Through examination of the test behavior under local alternatives, we show that the adaptively weighted log-rank test is fully efficient when the limiting censoring distributions in the two groups are equal. Unlike the case for the usual application of the log-rank test, critical values obtained from the resampling method are asymptotically valid without the assumption of equal censoring distributions in the two groups. Extensive simulation studies show that the new method improves the log-rank test for a wide range of treatment effect patterns, and that the resampling approach yields better control of the type 1 error rate than the originally proposed approach in the work of Yang and Prentice. The new procedures are illustrated in several clinical trial examples.

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“…Our first illustration comes from the paper of Yang and corresponds to the second example from that paper. The Gastrointestinal Tumor Study Group conducted a trial comparing chemotherapy alone with chemotherapy and radiation therapy in the treatment of locally unresectable gastric cancer .…”

Section: Practical Examples Of Changes In Codingmentioning

confidence: 99%

“…The coding for the chemotherapy group was represented by Z =0, whereas the coding for the chemotherapy and radiotherapy group was given by Z =1. The data were analyzed by Yang, and the log‐rank test has a p ‐value of 0.64, a value that remains unchanged by a change in coding (Figure ). The adaptative weighted log‐rank test (LRAD) gives a p ‐value of 0.035 using the resampling method with one million repetitions.…”

Section: Practical Examples Of Changes In Codingmentioning

confidence: 99%

“…We read with interest the paper, “Improving testing and description of treatment effect in clinical trials with survival outcomes” by Yang and related papers “Semiparametric analysis of short‐term and long‐term hazard ratios with two‐sample survival data” and “Improved logrank‐type tests for survival data using adaptive weights” published by Yang and Prentice . Alternatives to the absence of treatment effect on survival are most commonly framed within the proportional hazards model structure.…”

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confidence: 99%

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Note on the role of the placebo group in the short‐term and long‐term hazard ratio model

Flandre

O’Quigley

2020

Statistics in Medicine

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We read with interest the paper, "Improving testing and description of treatment effect in clinical trials with survival outcomes" by Yang 1 and related papers "Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data" and "Improved logrank-type tests for survival data using adaptive weights" published by Yang and Prentice. 2,3 Alternatives to the absence of treatment effect on survival are most commonly framed within the proportional hazards model structure. When broader alternatives of nonproportional hazards nature are of interest, it is known that the high efficiency of the log-rank test can be lost. One test that focuses on this issue is the test of Yang and Prentice, 3 based on the short-term and long-term hazard ratio model. The authors argue that the test is able to detect departures from a null hypothesis of no effect against quite broad alternatives. We recall the model on which this test is based and the test itself. Theoretical work and simulations appear to show the test to work well. However, it is important to underline that the test can only be used when the placebo group is clearly defined. Failing to correctly identify which group is the placebo group can have serious consequences and lead to erroneous conclusions. The results may not be interpretable. In situations where we wish to compare Treatment A with Treatment B, neither of which can be considered to correspond unambiguously to the placebo group, our advice would be to avoid this test altogether. We give two examples before concluding that the validity of this test hinges crucially upon a clear and sharp definition of the placebo group.

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Improving testing and description of treatment effect in clinical trials with survival outcomes

Cited by 9 publications

References 36 publications

Dynamic RMST Curves for Survival Analysis in Clinical Trials

Dynamic RMST Curves for Survival Analysis in Clinical Trials

Interim monitoring using the adaptively weighted log‐rank test in clinical trials for survival outcomes

Note on the role of the placebo group in the short‐term and long‐term hazard ratio model

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