Browsing by Subject "Heterogeneity in treatment effect"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Estimation of treatment effect in a subpopulation: An empirical Bayes approach
    (Taylor & Francis, 2016) Shen, Changyu; Li, Xiaochun; Jong, Jaesik; Department of Biostatistics, Richard M. Fairbanks School of Public Health
    It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in subpopulations. In particular, due to limited sample size for subpopulations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often noninformative. We propose an empirical Bayes approach to combine both information embedded in a target subpopulation and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.
  • Thumbnail Image
    Item
    Treatment Benefit and Treatment Harm Rate to Characterize Heterogeneity in Treatment Effect
    (Oxford University Press, 2013) Shen, Changyu; Jeong, Jaesik; Li, Xiaochun; Chen, Peng-Shen; Buxton, Alfred; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    It is well recognized that the conventional summary of treatment effect by averaging across individual patients has its limitation in ignoring the heterogeneous responses to the treatment in the target population. However, there are few alternative metrics in the literature that are designed to capture such heterogeneity. We propose the concept of treatment benefit rate (TBR) and treatment harm rate (THR) that characterize both the overall treatment effect and the magnitude of heterogeneity. We discuss a method to estimate TBR and THR that easily incorporates a sensitivity analysis scheme, and illustrate the idea through analysis of a randomized trial that evaluates the implantable cardioverter-defibrillator (ICD) in reducing mortality. A simulation study is presented to assess the performance of the proposed method.