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 tidsskriftReviewForskningfagfællebedømt

Standard

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 tidsskriftReviewForskningfagfællebedømt

Harvard

Wharton, S, Astrup, A, Endahl, L, Lean, MEJ, Satylganova, A, Skovgaard, D, Wadden, TA & Wilding, JPH 2021, 'Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data', International Journal of Obesity, bind 45, s. 923-933. https://doi.org/10.1038/s41366-020-00733-x

APA

Wharton, S., Astrup, A., Endahl, L., Lean, M. E. J., Satylganova, A., Skovgaard, D., Wadden, T. A., & Wilding, J. P. H. (2021). Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data. International Journal of Obesity, 45, 923-933. https://doi.org/10.1038/s41366-020-00733-x

Vancouver

Wharton S, Astrup A, Endahl L, Lean MEJ, Satylganova A, Skovgaard D o.a. Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data. International Journal of Obesity. 2021;45:923-933. https://doi.org/10.1038/s41366-020-00733-x

Author

Wharton, Sean ; Astrup, Arne ; Endahl, Lars ; Lean, Michael E J ; Satylganova, Altynai ; Skovgaard, Dorthe ; Wadden, Thomas A ; Wilding, John P H. / Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data. I: International Journal of Obesity. 2021 ; Bind 45. s. 923-933.

Bibtex

@article{7453c7753f2940f1a88d3a007cfe8015,
title = "Estimating and reporting treatment effects in clinical trials for weight management: Using estimands to interpret effects of intercurrent events and missing data",
abstract = "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.",
author = "Sean Wharton and Arne Astrup and Lars Endahl and Lean, {Michael E J} and Altynai Satylganova and Dorthe Skovgaard and Wadden, {Thomas A} and Wilding, {John P H}",
note = "CURIS 2021 NEXS 028",
year = "2021",
doi = "10.1038/s41366-020-00733-x",
language = "English",
volume = "45",
pages = "923--933",
journal = "International Journal of Obesity",
issn = "0307-0565",
publisher = "nature publishing group",

}

RIS

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