Dealing with heterogeneity of treatment effects: is the literature up to the challenge?

Gabler, Duan Naihua, Liao Diana, Elmore, Ganiats, Kravitz

Description

Some patients will experience more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is necessary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to track the use of HTE analyses over time, examine the appropriateness of the statistical methods used, and explore the predictors of such analyses. Methods Articles were selected through a probability sample of randomized controlled trials (RCTs) published in Annals of Internal Medicine , BMJ , JAMA , The Lancet , and NEJM during odd numbered months of 1994, 1999, and 2004. RCTs were independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as reporting: (1) HTE analysis, utilizing a formal test for heterogeneity or treatment-by-covariate interaction, (2) subgroup analysis only, involving no formal test for heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to identify variables associated with HTE reporting. Results 319 studies were included. Ninety-two (29%) reported HTE analysis; another 88 (28%) reported subgroup analysis only, without examining HTE formally. Major covariates examined included individual risk factors associated with prognosis, responsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Journal of publication and sample size were significant independent predictors of HTE analysis (p < 0.05 and p < 0.001, respectively). Conclusion HTE is frequently ignored or incorrectly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized approach to evidence-based medicine.

Informations

• Publié par: biomed
• Publié le: 01 janvier 2009
• Nombre de lectures: 8
• Langue: English

Extrait

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Bio Med Central

Address: 1 Center for Healthcare Policy an d Research, University of California, Davis, California, USA, 2 Columbia University, New York State Psychiatric Institute, New York, USA, 3 University of California, Los Angeles Neur opsychiatric Institute, California, USA, 4 University of Washington School of Medicine, Seattle, Washington, USA, 5 Department of Medicine, Harborview Medi cal Center, Seattle, Washington, USA, 6 Department of Family and Preventive Medicine, University of California, San Diego, California, USA and 7 Department of Internal Medicine, University of California, Davis, California, USA Email: Nicole B Gabler* - nrblose [e-mail]; Naihua Duan - Nai [e-mail]; Diana L iao - [e-mail]; Joann G Elmore - [e-mail] ngton.edu; Theodore G Ganiats - tganiats@ucsd .edu; Richard L Kravitz - [e-mail] Corresponding author *

Abstract Background:Some patients will experien ce more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is nece ssary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to tr ack the use of HTE analyses over time, examine the appropriateness of the statisti cal methods used, and e xplore the predictors of such analyses. Methods: Articles were selected throug h a probability sample of randomized controlled trials (RCTs) published in Annals of Internal Medicine , BMJ , JAMA , The Lancet , and NEJM during odd numbered months of 1994, 1999, and 2004. RCTs we re independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as repo rting: (1) HTE analysis, utilizing a formal test for hetero geneity or treatment-by -covariate interaction, (2) subgroup analysis only, involving no formal test fo r heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to id entify variables associated with HTE reporting. Results: 319 studies were included. Ni nety-two (29%) reported HT E analysis; another 88 (28%) reported subgroup analysis only, without exam ining HTE formally. Major covariates examined included individual risk factors associated with prognosis, re sponsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Jo urnal of publication and sample si ze were significant independent predictors of HTE analysis (p < 0.05 and p < 0.001, respectively). Conclusion: HTE is frequently ignored or incorre ctly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized appr oach to evidence-based medicine.

Published: 19 June 2009 Received: 3 October 2008 Trials 2009, 10 :43 doi:10.1186/1745-6215-10-43  Accepted: 19 June 2009 This article is available from: http:/ /www.trialsjournal.com/content/10/1/43 © 2009 Gabler et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the orig inal work is properly cited.

Trials

Research Open Access Dealing with heterogeneity of treat ment effects: is the literature up to the challenge? Nicole B Gabler* 1 , Naihua Duan 2 , Diana Liao 3 , Joann G Elmore 4,5 , Theodore G Ganiats 6 and Richard L Kravitz 1,7