Mid-course sample size modification in clinical trials based on the observed treatment effect

2003 ◽  
Vol 22 (6) ◽  
pp. 971-993 ◽  
Author(s):  
Christopher Jennison ◽  
Bruce W. Turnbull
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Treatment Effect ◽  
Size Modification

Practical midcourse sample size modification in clinical trials

2003 ◽  
Vol 24 (1) ◽  
pp. 4-15 ◽  
Author(s):  
Michael A. Proschan ◽  
Qing Liu ◽  
Sally Hunsberger
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Size Modification

scholarly journals Sample Size Requirements of Glaucoma Clinical Trials When Using Combined Optical Coherence Tomography and Visual Field Endpoints

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zhichao Wu ◽  
Felipe A. Medeiros
Keyword(s):  
Clinical Trials ◽  
Optical Coherence Tomography ◽  
Visual Field ◽  
Sample Size ◽  
Treatment Effect ◽  
Optical Coherence ◽  
Non Invasive ◽  
Combined Use ◽  
Oct Imaging ◽  
Size Estimates

AbstractGlaucoma clinical trials using visual field (VF) endpoints currently require large sample sizes because of the slowly-progressive nature of this disease. We sought to examine whether the combined use of VF testing and non-invasive optical coherence tomography (OCT) imaging of the neuroretinal tissue could improve the feasibility of such trials. To examine this, we included 192 eyes of 121 glaucoma participants seen at ≥5 visits over a 2-year period to extract real-world estimates of the rates of change and variability of VF and OCT imaging measurements for computer simulations to obtain sample size estimates. We observed that the combined use of VF and OCT endpoints led to a 31–33% reduction in sample size requirements compared to using VF endpoints alone for various treatment effect sizes. For example, 189 participants would be required per group to detect a 30% treatment effect with 90% power with combined VF and OCT endpoints, whilst 276 and 285 participants would be required when using VF and OCT endpoints alone respectively. The combined use of OCT and VF endpoints thus has the potential to effectively improve the feasibility of future glaucoma clinical trials.

Comments on “Adaptive sample size modification in clinical trials: Start small then ask for more?”

2019 ◽  
Vol 39 (1) ◽  
pp. 97-98
Author(s):  
Maximilian Pilz ◽  
Meinhard Kieser ◽  
Kevin Kunzmann
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Size Modification

scholarly journals Adaptive sample size modification in clinical trials: start small then ask for more?

2015 ◽  
Vol 34 (29) ◽  
pp. 3793-3810 ◽  
Author(s):  
Christopher Jennison ◽  
Bruce W. Turnbull
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Size Modification

scholarly journals A Critical Review on Adaptive Sample Size Re-estimation (SSR) Designs for Superiority Trials with Continuous Endpoints

2019 ◽  
pp. 1-13
Author(s):  
Xiaoyu Cai ◽  
Yi Tsong ◽  
Meiyu Shen
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Treatment Effect ◽  
Critical Review ◽  
Adaptive Design ◽  
Sample Size Determination ◽  
Limited Information ◽  
Study Objective ◽  
Superiority Trials

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.

Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

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.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

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