Adjusted win ratio and its applications in the translational studies

The 2024/25 application process is now closed

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Abstract

Background

Many clinical and epidemiological studies include a composite outcome. Conventional analysis of a composite outcome uses the time to the occurrence of first event, ignoring the clinical importance of each component event. As a result, the result of a composite outcome could be dominated by clinical less important events. To address this limitation, win ratio has been proposed and applied in design and analysis of some high-profile clinical trials. However, win ratio has not been applied in the observational studies since it is a univariate approach that is unable to control for confounders.

Methods

This project will propose an adjusted win ratio to address the confounding issue in the observational studies. Below are the specific objectives of this project.

 

  • To propose an adjusted win ratio statistic by means of inverse-probability-of-weighting (IPW) via propensity score.
  • To derive the point estimate, interval estimate and p-value for the adjusted win ratio statistic.
  • To apply the suggested adjusted win ratio statistic to the design and analysis of translational studies such as clinical trials and observational studies.

We will perform the statistical computing using the R package, WINS, available on the Comprehensive R Archive Network.   

Expected Results

The following results are expected:

 

  • Manuscript on the adjusted win ratio will be prepared with a focus on the methodological issues of adjusted win ratio.
  • Applied paper on the application of adjusted win ratio in clinical trials will be produced. The data from PMC trial, AMBITION trial and some other trials from the Cultech Limited will be used for this study.
  • Applied paper on the application of adjusted win ration in observational studies will be generated. The data from Framingham Heart Study, CHARM programme and some observational studies will be used in this study.

Conclusion

Adjusted win ratio can provide alternative measurements of exposure effect for a composite outcome in observational studies.  In addition, the IPCW-adjusted win ratio is effective at controlling for baseline characteristics imbalances in clinical trials and confounding factors in the observational studies.

Where does the project lie on the Translational Pathway?

T2 Human / Clinical Research    

T3 Evidence into Practice            

T4 Practice to Policy / Population

Expected Outputs
  • At least three papers are expected from this project:

1. Adjusted win ratio using inverse-probability-of-weighting (IPW) via propensity score.

2. Application of adjusted win ratio in the analysis of composite outcome in clinical trials. 

3. Application of adjusted win ratio in the analysis of composite outcome in observational studies.

Training Opportunities
  • The following training opportunities will be provided:

1. Design and analysis of clinical research

2. Advanced statistical methods and their applications in clinical research

3. Advanced SAS programming for data analysis

Skills Required

The ideal candidate should have: (1) MSc in Medical statistics or related subject; (2) Some experience in design and analysis of clinical trials and complex datasets; (3) Good command of at least one statistical packages such as STATA/R/SAS; (4) Some experience of writing academic reports; (5) Excellent communication skills; and (6) Knowledge of clinical trials methodology, medical statistics and epidemiology.

Key Publications associated with this project

Pocock SJ, Ariti CA, Collier TJ, Wang D. The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. Eur Heart J. 2012 Jan;33(2):176-82.

Wang D, Pocock S. A win ratio approach to comparing continuous non-normal outcomes in clinical trials. Pharm Stat. 2016 May;15(3):238-45.

Dong G, Qiu J, Wang D, Vandemeulebroecke M. The stratified win ratio. J Biopharm Stat. 2018;28(4):778-796.

Dong G, Huang B, Wang D, Verbeeck J, Wang J, Hoaglin DC. Adjusting win statistics for dependent censoring. Pharm Stat. 2021 May;20(3):440-450.  

Dong G, Huang B, Verbeeck J, Cui Y, Song J, Gamalo-Siebers M, Wang D, Hoaglin DC, Seifu Y, Mütze T, Kolassa J. Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes. Pharm Stat. 2022 Jun 27.