Phase II cancer clinical trials with a one-sample log-rank test and its corrections based on the Edgeworth expansion

Sun, Xiaoqun; Peng, Paul; Tu, Dongsheng

doi:10.1016/j.cct.2010.09.009

Cited by 25 publications

(33 citation statements)

References 22 publications

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“…Kwak and Jung , Jung , and Sun et al . applied the one‐sample log‐rank test to phase II clinical trial designs.…”

Section: Introductionmentioning

confidence: 99%

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Sample size calculation for the one-sample log-rank test

2014

Pharmaceut. Statist.

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“…Kwak and Jung , Jung , and Sun et al . applied the one‐sample log‐rank test to phase II clinical trial designs.…”

Section: Introductionmentioning

confidence: 99%

“…The study designs using the one-sample log-rank test are proposed by Finkelstein et al [1]. Kwak and Jung [8], Jung [9], and Sun et al [10] applied the one-sample log-rank test to phase II clinical trial designs.…”

Section: Introductionmentioning

confidence: 99%

Sample size calculation for the one-sample log-rank test

2014

Pharmaceut. Statist.

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“…Hence, we reject the null hypothesis H 0 with one-sided type I error α if L 1 = W /υ̂ < − z 1−α , where z 1−α is the 100(1 − α) percentile of the standard normal distribution. Simulation results showed, however, that the OSLRT L 1 is conservative, even when the sample size is relatively large (Kwak and Jung, 2013; Sun et al, 2011; and Wu, 2015). …”

Section: Test Statisticsmentioning

confidence: 94%

Single-arm Phase II cancer survival trial designs

2016

Journal of Biopharmaceutical Statistics

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The current practice for designing single-arm phase II trials with time-to-event endpoints is limited to using either a maximum likelihood estimate test under the exponential model or a naive approach based on dichotomizing the event time at a landmark time point. A trial designed under the exponential model may not be reliable, and the naive approach is inefficient. The modified one-sample log-rank test statistic proposed in this paper fills the void. In general, the proposed test can be used to design single-arm phase II survival trials under any parametric survival distribution. Simulation results showed that it preserves type I error well and provides adequate power for phase II cancer trial designs with time-to-event endpoints.

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“…Frequentist approaches to the design of phase II trials with survival endpoints have been given by Case and Morgan [8], Owzar and Jung [9] and Sun, Peng and Tu [10]. The method described in [8] is based on the Nelson-Aalen estimator of survival probabilities and its approximate normal distribution: comparisons will be made with [8] throughout this paper.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], three methods are explored: one requires dichotomisation of the survival times, one assumes exponential survival times and the third takes a nonparametric approach. A one-sample logrank test statistic [11] is adopted in [10], with an Edgeworth expansion being used to improve approximations to its distribution. Much of the remaining literature on this topic takes a Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

One‐stage and two‐stage designs for phase II clinical trials with survival endpoints

Whitehead

2014

Statistics in Medicine

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This work is motivated by trials in rapidly lethal cancers or cancers for which measuring shrinkage of tumours is infeasible. In either case, traditional phase II designs focussing on tumour response are unsuitable. Usually, tumour response is considered as a substitute for the more relevant but longer-term endpoint of death. In rapidly lethal cancers such as pancreatic cancer, there is no need to use a surrogate, as the definitive endpoint is (sadly) available so soon. In uveal cancer, there is no counterpart to tumour response, and so, mortality is the only realistic response available. Cytostatic cancer treatments do not seek to kill tumours, but to mitigate their effects. Trials of such therapy might also be based on survival times to death or progression, rather than on tumour shrinkage. Phase II oncology trials are often conducted with all study patients receiving the experimental therapy, and this approach is considered here. Simple extensions of one-stage and two-stage designs based on binary responses are presented. Outcomes based on survival past a small number of landmark times are considered: here, the case of three such times is explored in examples. This approach allows exact calculations to be made for both design and analysis purposes. Simulations presented here show that calculations based on normal approximations can lead to loss of power when sample sizes are small. Two-stage versions of the procedure are also suggested.

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Phase II cancer clinical trials with a one-sample log-rank test and its corrections based on the Edgeworth expansion

Cited by 25 publications

References 22 publications

Sample size calculation for the one-sample log-rank test

Sample size calculation for the one-sample log-rank test

Single-arm Phase II cancer survival trial designs

One‐stage and two‐stage designs for phase II clinical trials with survival endpoints

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