scholarly journals Missing data and sensitivity analysis for binary data with implications for sample size and power of randomized clinical trials

2019 ◽  
Vol 39 (2) ◽  
pp. 192-204
Author(s):  
Thomas Cook ◽  
Ryan Zea
Keyword(s):  
Sensitivity Analysis ◽  
Clinical Trials ◽  
Missing Data ◽  
Sample Size ◽  
Binary Data ◽  
Randomized Clinical Trials

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.

scholarly journals Missing Data in Randomized Clinical Trials for Weight Loss: Scope of the Problem, State of the Field, and Performance of Statistical Methods

PLoS ONE ◽  
2009 ◽  
Vol 4 (8) ◽  
pp. e6624 ◽  
Author(s):  
Mai A. Elobeid ◽  
Miguel A. Padilla ◽  
Theresa McVie ◽  
Olivia Thomas ◽  
David W. Brock ◽  
...  
Keyword(s):  
Weight Loss ◽  
Clinical Trials ◽  
Missing Data ◽  
Statistical Methods ◽  
Randomized Clinical Trials ◽  
And Performance ◽  
State Of The Field

Statistical power of negative randomized clinical trials (RCTs) presented at the American Society of Clinical Oncology (ASCO) Annual Meetings

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6516-6516
Author(s):  
P. Bedard ◽  
M. K. Krzyzanowska ◽  
M. Pintilie ◽  
I. F. Tannock
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Annual Meeting ◽  
Primary Endpoint ◽  
Effect Size ◽  
Statistical Power ◽  
Randomized Clinical Trials ◽  
Medium Effect ◽  
Post Hoc ◽  
Adequate Sample Size

6516 Background: Underpowered randomized clinical trials (RCTs) may expose participants to risks and burdens of research without scientific merit. We investigated the prevalence of underpowered RCTs presented at ASCO annual meetings. Methods: We surveyed all two-arm parallel phase III RCTs presented at the ASCO annual meeting from 1995–2003 where differences for the primary endpoint were non-statistically significant. Post hoc calculations were performed using a power of 80% and a=0.05 (two-sided) to determine the sample size required to detect a small, medium, and large effect size between the two groups. For studies reporting a proportion or time to event as a primary endpoint, effect size was expressed as an odds ratio (OR) or hazard ratio (HR) respectively, with a small effect size defined as OR/HR=1.3, medium effect size OR/HR=1.5, and large effect OR/HR=2.0. Logistic regression was used to identify factors associated with lack of statistical power. Results: Of 423 negative RCTs for which post hoc sample size calculations could be performed, 45 (10.6%), 138 (32.6%), and 333 (78.7%) had adequate sample size to detect small, medium, and large effect sizes respectively. Only 35 negative RCTs (7.1%) reported a reason for inadequate sample size. In a multivariable model, studies presented at plenary or oral sessions (p<0.0001) and multicenter studies supported by a co-operative group were more likely to have adequate sample size (p<0.0001). Conclusion: Two-thirds of negative RCTs presented at the ASCO annual meeting do not have an adequate sample to detect a medium-sized treatment effect. Most underpowered negative RCTs do not report a sample size calculation or reasons for inadequate patient accrual. No significant financial relationships to disclose.

Sign in / Sign up

Export Citation Format

Share Document