Coping with missing data in clinical trials: A model-based approach applied to asthma trials

2002 ◽  
Vol 21 (8) ◽  
pp. 1043-1066 ◽  
Author(s):  
James Carpenter ◽  
Stuart Pocock ◽  
Carl Johan Lamm
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Model Based

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 Random-effects meta-analysis for systematic reviews of phase I clinical trials: Rare events and missing data

2016 ◽  
Vol 8 (2) ◽  
pp. 124-135 ◽  
Author(s):  
Mi-Ok Kim ◽  
Xia Wang ◽  
Chunyan Liu ◽  
Kathleen Dorris ◽  
Maryam Fouladi ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Phase I ◽  
Random Effects ◽  
Systematic Reviews ◽  
Rare Events ◽  
Meta Analysis ◽  
Phase I Clinical Trials

scholarly journals Short‐Term Efficacy Reliably Predicts Long‐Term Clinical Benefit in Rheumatoid Arthritis Clinical Trials as Demonstrated by Model‐Based Meta‐Analysis

2015 ◽  
Vol 56 (7) ◽  
pp. 835-844 ◽  
Author(s):  
Yehong Wang ◽  
Rui Zhu ◽  
Jim Xiao ◽  
John C. Davis ◽  
Jaap W. Mandema ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Clinical Trials ◽  
Clinical Benefit ◽  
Meta Analysis ◽  
Short Term ◽  
Model Based ◽  
Term Efficacy

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
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