Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data. / Wharton, Sean; Astrup, Arne; Endahl, Lars; Lean, Michael E J; Satylganova, Altynai; Skovgaard, Dorthe; Wadden, Thomas A; Wilding, John P H.
I: International Journal of Obesity, Bind 45, 2021, s. 923-933.Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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TY - JOUR
T1 - Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data
AU - Wharton, Sean
AU - Astrup, Arne
AU - Endahl, Lars
AU - Lean, Michael E J
AU - Satylganova, Altynai
AU - Skovgaard, Dorthe
AU - Wadden, Thomas A
AU - Wilding, John P H
N1 - CURIS 2021 NEXS 028
PY - 2021
Y1 - 2021
N2 - In 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.
AB - In 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.
U2 - 10.1038/s41366-020-00733-x
DO - 10.1038/s41366-020-00733-x
M3 - Review
C2 - 33462358
VL - 45
SP - 923
EP - 933
JO - International Journal of Obesity
JF - International Journal of Obesity
SN - 0307-0565
ER -
ID: 255504949