Predictive approaches to heterogeneous treatment effects: a scoping review
- PDF / 720,218 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 24 Downloads / 215 Views
(2020) 20:264
RESEARCH ARTICLE
Open Access
Predictive approaches to heterogeneous treatment effects: a scoping review Alexandros Rekkas1,2, Jessica K. Paulus3, Gowri Raman4, John B. Wong5, Ewout W. Steyerberg1,6, Peter R. Rijnbeek2, David M. Kent3* and David van Klaveren3,6
Abstract Background: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. Methods: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. Results: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). Conclusions: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.
Introduction Evidence based medicine (EBM) has heavily influenced the standards of current medical practice. Randomized clinical trials (RCTs) and meta-analyses of RCTs are regarded as the gold standards for determining the comparative efficacy or effectiveness of two (or more) treatments within the EBM framework. Within this framework, as described in Guyatt et al’s classic User’s Guide to the Medical Literature II [1], “if the patient meets all the [trial] inclusion criteria, and doesn’t violate * Correspondence: [email protected] 3 Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, 800 Washington St, Box 63, Boston, MA 02111, USA Full list of author information is available at the end of the article
any of the exclusion criteria—there is little question that the results [of the trial] are applicable”. It has thus been argued that RCTs should attempt to include even broader populations to ensure generalizability of their results to more (and more diverse) individuals [2, 3]. However, generalizability of an RCT result a
Data Loading...
