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


CONCLUSIONS
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.
Gabler
Nicole B
NB
Center for Healthcare Policy and Research, University of California, Davis, California, USA. nrbloser@ucdavis.edu
Duan
Naihua
N
Liao
Diana
D
Elmore
Joann G
JG
Ganiats
Theodore G
TG
Kravitz
Richard L
RL
D016009:Chi-Square Distribution; D019317:Evidence-Based Medicine / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D016032:Randomized Controlled Trials as Topic / Q000592:standards* / Q000706:statistics & numerical data*; D012307:Risk Factors; D013812:Therapeutics / Q000706:statistics & numerical data*; D016896:Treatment Outcome