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Linear Combinations of Multiple Outcome Measures to Improve the Power of Efficacy Analysis ---Application to Clinical Trials on Early Stage Alzheimer Disease.

ABSTRACT: Modern clinical trials on Alzheimer disease (AD) focus on the early symptomatic stage or even the preclinical stage. Subtle disease progression at the early stages, however, poses a major challenge in designing such clinical trials. We propose a multivariate mixed model on repeated measures to model the disease progression over time on multiple efficacy outcomes, and derive the optimum weights to combine multiple outcome measures by minimizing the sample sizes to adequately power the clinical trials. A cross-validation simulation study is conducted to assess the accuracy for the estimated weights as well as the improvement in reducing the sample sizes for such trials. The proposed methodology is applied to the multiple cognitive tests from the ongoing observational study of the Dominantly Inherited Alzheimer Network (DIAN) to power future clinical trials in the DIAN with a cognitive endpoint. Our results show that the optimum weights to combine multiple outcome measures can be accurately estimated, and that compared to the individual outcomes, the combined efficacy outcome with these weights significantly reduces the sample size required to adequately power clinical trials. When applied to the clinical trial in the DIAN, the estimated linear combination of six cognitive tests can adequately power the clinical trial.

SUBMITTER: Xiong C

PROVIDER: S-EPMC5849424 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Linear Combinations of Multiple Outcome Measures to Improve the Power of Efficacy Analysis ---Application to Clinical Trials on Early Stage Alzheimer Disease.

Xiong Chengjie C Luo Jingqin J Morris John C JC Bateman Randall R

Biostatistics & epidemiology 20170602 1

Modern clinical trials on Alzheimer disease (AD) focus on the early symptomatic stage or even the preclinical stage. Subtle disease progression at the early stages, however, poses a major challenge in designing such clinical trials. We propose a multivariate mixed model on repeated measures to model the disease progression over time on multiple efficacy outcomes, and derive the optimum weights to combine multiple outcome measures by minimizing the sample sizes to adequately power the clinical tr ...[more]

PMID: 29546251

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Project description:BACKGROUND:Acute severe haemorrhage is a common complication of injury, childbirth, surgery, gastrointestinal pathologies and other medical conditions. Bleeding is a major cause of death, but patients also die from non-bleeding causes, the frequency of which varies by the site of haemorrhage and between populations. Because patients can bleed to death within hours, established interventions inevitably take priority over randomisation into a trial. These circumstances raise challenges in selecting appropriate outcome measures for clinical trials of haemostatic interventions. MAIN BODY:We use data from three large randomised controlled trials in acute severe haemorrhage (CRASH-2, WOMAN and HALT-IT) to explore the strengths and limitations of outcome measures commonly used in trials of haemostatic treatments, including all-cause and cause-specific mortality, blood transfusion and surgical interventions. Many deaths following acute severe haemorrhage are due to patient comorbidities or complications rather than bleeding. If non-bleeding deaths are unaffected by a haemostatic intervention, even large trials will have low power to detect an effect on all-cause mortality. Due to the dilution from deaths unaffected or reduced by the trial treatment, all-cause mortality can also obscure important harmful effects. Additionally, because the relative contributions of different causes of death vary within and between patient populations, all-cause mortality is not generalisable. Different causes of death occur at different time intervals from bleeding onset, with bleeding deaths generally occurring early. Time-specific mortality can therefore be used as a proxy for cause in un-blinded trials where bias is a concern or in situations where cause of death cannot be assessed. Urgent treatment is critical, and so post-randomisation blood transfusion and surgery are often planned before or at the time of randomisation and therefore cannot be influenced by the trial treatment. CONCLUSIONS:All-cause mortality has low power, lacks generalisability and can obscure harmful effects. Cause-specific mortality, such as death due to bleeding or thrombosis, avoids these drawbacks. In certain scenarios, time-specific mortality can be used as a proxy for cause-specific mortality. Blood transfusion and surgical procedures have limited utility as outcome measures in trials of haemostatic treatments.

Dataset Information

Linear Combinations of Multiple Outcome Measures to Improve the Power of Efficacy Analysis ---Application to Clinical Trials on Early Stage Alzheimer Disease.

Publications

Linear Combinations of Multiple Outcome Measures to Improve the Power of Efficacy Analysis ---Application to Clinical Trials on Early Stage Alzheimer Disease.

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