Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials

2005 ◽  
Vol 2 (5) ◽  
pp. 379-386 ◽  
Author(s):  
Caroline Beunckens ◽  
Geert Molenberghs ◽  
Michael G Kenward
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Randomized Clinical Trials ◽  
Likelihood Analysis

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

Intention-to-Treat Analysis and Accounting for Missing Data in Orthopaedic Randomized Clinical Trials

2009 ◽  
Vol 91 (9) ◽  
pp. 2137-2143 ◽  
Author(s):  
Amir Herman ◽  
Itamar Busheri Botser ◽  
Shay Tenenbaum ◽  
Ahron Chechick
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Randomized Clinical Trials ◽  
Intention To Treat ◽  
Treat Analysis

scholarly journals Addressing missing data in randomized clinical trials: A causal inference perspective

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0234349 ◽  
Author(s):  
Ilja Cornelisz ◽  
Pim Cuijpers ◽  
Tara Donker ◽  
Chris van Klaveren
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Causal Inference ◽  
Randomized Clinical Trials
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