Sample size re-estimation for survival data in clinical trials with an adaptive design

2010 ◽  
Vol 10 (4) ◽  
pp. 325-331 ◽  
Author(s):  
Kanae Togo ◽  
Manabu Iwasaki
2005 ◽  
Vol 15 (4) ◽  
pp. 707-718 ◽  
Author(s):  
Gang Li ◽  
Weichung J. Shih ◽  
Yining Wang

2009 ◽  
Vol 15 (4) ◽  
pp. 468-492 ◽  
Author(s):  
Uttam Bandyopadhyay ◽  
Atanu Biswas ◽  
Rahul Bhattacharya

2012 ◽  
Vol 11 (2) ◽  
pp. 141-148 ◽  
Author(s):  
Susan Todd ◽  
Elsa Valdés-Márquez ◽  
Jodie West

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 6576-6576
Author(s):  
Satoshi Teramukai ◽  
Takashi Daimon ◽  
Sarah Zohar

6576 Background: The aim of phase II trials is to determine if a new treatment is promising for further testing in confirmatory clinical trials. Most phase II clinical trials are designed as single-arm trials using a binary outcome with or without interim monitoring for early stopping. In this context, we propose a Bayesian adaptive design denoted as PSSD, predictive sample size selection design (Statistics in Medicine 2012;31:4243-4254). Methods: The design allows for sample size selection followed by any planned interim analyses for early stopping of a trial, together with sample size determination before starting the trial. In the PSSD, we determined the sample size using the predictive probability criterion with two kinds of prior distributions, that is, an ‘analysis prior’ used to compute posterior probabilities and a ‘design prior’ used to obtain prior predictive distributions. In the sample size determination, we provide two sample sizes, that is, N and Nmax, using two types of design priors. At each interim analysis, we calculate the predictive probability of achieving a successful result at the end of the trial using analysis prior in order to stop the trial in case of low or high efficacy, and we select an optimal sample size, that is, either N or Nmax as needed, on the basis of the predictive probabilities. Results: We investigated the operating characteristics through simulation studies, and the PSSD retrospectively applies to a lung cancer clinical trial. As the number of interim looks increases, the probability of type I errors slightly decreases, and that of type II errors increases. The type I error probabilities of the probabilities of the proposed PSSD are almost similar to those of the non-adaptive design. The type II error probabilities in the PSSD are between those of the two fixed sample size (N or Nmax) designs. Conclusions: From a practical standpoint, the proposed design could be useful in phase II single-arm clinical trials with a binary endpoint. In the near future, this approach will be implemented in actual clinical trials to assess its usefulness and to extend it to more complicated clinical trials.


2010 ◽  
Vol 64 (2) ◽  
pp. 202-226 ◽  
Author(s):  
Uttam Bandyopadhyay ◽  
Atanu Biswas ◽  
Rahul Bhattacharya

2019 ◽  
pp. 1-13
Author(s):  
Xiaoyu Cai ◽  
Yi Tsong ◽  
Meiyu Shen

Adaptive sample size re-estimation (SSR) methods have been widely used for designing clinical trials, especially during the past two decades. We give a critical review for several commonly used two-stage adaptive SSR designs for superiority trials with continuous endpoints. The objective, design and some of our suggestions and concerns of each design will be discussed in this paper. Keywords: Adaptive Design; Sample Size Re-estimation; Review Introduction Sample size determination is a key part of designing clinical trials. The objective of a good clinical trial design is to achieve the balance between efficiently spending resources and enrolling enough patients to achieve a desired power. At the designing stage of a clinical trial, there usually only have limited information available about the population, so that the sample size calculated at this stage may not be sufficient to address the study objective. Assumed that the data from two parallel treatment groups (e.g. treatment and control groups) are normally distributed with mean treatment effect μ_1 and μ_2, and equal within-group variance 𝜎2. Let the mean difference (treatment effect) . The efficacy of the treatment will be evaluated by testing the hypothesis.


1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.


2021 ◽  
Author(s):  
L. Howells ◽  
S. Gran ◽  
J. R. Chalmers ◽  
B. Stuart ◽  
M. Santer ◽  
...  

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