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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/474| Predictive approaches to heterogeneous treatment effects: a systematic review | |
| John B. Wong Gowri Raman Jessica K. Paulus Alexandros Rekkas Peter R. Rijnbeek David van Klaveren Ewout W. Steyerberg David M. Kent | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/19010827 | |
| 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). Conclusion: 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by a Patient Centered Outcomes Research Institute (PCORI) contract, the Predictive Analytics Resource Center [SA.Tufts.PARC.OSCO.2018.01.25]. We also acknowledge support from the Innovative Medicines Initiative (IMI). ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data collected for systematic review is publically available on Medline and Cochrane Central. | |
| Cold Spring Harbor Laboratory Press | |
| 2019 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/19010827v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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