Dataset Information

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials.

ABSTRACT:

Introduction

Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods.

Methods

We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial.

Results

Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm.

Discussion

Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods.

Highlights

We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.

SUBMITTER: Wang G

PROVIDER: S-EPMC11350030 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials.

Wang Guoqiao G Cutter Gary G Oxtoby Neil P NP Shan Guogen G Wang Whedy W Mangal Brian B Liao Yijie Y Llibre-Guerra Jorge J JJ Li Yan Y Xiong Chengjie C McDade Eric E Delmar Paul P Bateman Randall J RJ Schneider Lon L

Alzheimer's & dementia : the journal of the Alzheimer's Association 20240621 8

<h4>Introduction</h4>Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods.<h4>Methods</h4>We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clini ...[more]

PMID: 39030751

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Project description:ObjectivesTo predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.SettingAs an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes). Population 17,802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.MethodsData from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one.ResultsThe median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.ConclusionsData from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores. The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event. Trial registration number Clinicaltrials.gov NCT00239681.

Dataset Information

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials.

Introduction

Methods

Results

Discussion

Highlights

Publications

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials.

Similar Datasets

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