A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a "matrix" of treatments and outcomes in non-randomized data.
We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software.
The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity.
[Show abstract][Hide abstract] ABSTRACT: Comparative-effectiveness research (CER) aims to produce actionable evidence regarding the effectiveness and safety of medical products and interventions as they are used outside of controlled research settings. Although CER evidence regarding medications is particularly needed shortly after market approval, key methodological challenges include (i) potential bias due to channeling of patients to the newly marketed medication because of various patient-, physician-, and system-related factors; (ii) rapid changes in the characteristics of the user population during the early phase of marketing; and (iii) lack of timely data and the often small number of users in the first few months of marketing. We propose a mix of approaches to generate comparative-effectiveness data in the early marketing period, including sequential cohort monitoring with secondary health-care data and propensity score (PS) balancing, as well as extended follow-up of phase III and phase IV trials, indirect comparisons of placebo-controlled trials, and modeling and simulation of virtual trials.
[Show abstract][Hide abstract] ABSTRACT: Purpose:
High-dimensional propensity score (hd-PS) adjustment has been proposed as a tool to improve control for confounding in pharmacoepidemiological studies using longitudinal claims databases. We investigated whether hd-PS matching improved confounding by indication in a study of Cox-2 inhibitors (coxibs) and traditional nonsteroidal anti-inflammatory drugs (tNSAIDs) and their association with the risk of upper gastrointestinal complications (UGIC).
In a cohort study of new users of coxibs and tNSAIDs we compared the effectiveness of these drugs to reduce UGIC using hd-PS matching and conventional propensity score (PS) matching in the German Pharmacoepidemiological Research Database.
The unadjusted rate ratio (RR) of UGIC for coxib users versus tNSAID users was 1.21 [95 % confidence interval (CI) 0.91-1.61]. The conventional PS matched cohort based on 79 investigator-identified covariates resulted in a RR of 0.84 (0.56-1.26). The use of the hd-PS algorithm based on 900 empirical covariates further decreased the RR to 0.62 (0.43-0.91).
A comparison of hd-PS matching versus conventional PS matching resulted in improved point estimates for studying an intended treatment effect of coxibs versus tNSAIDs when benchmarked against results from randomized controlled trials.
European Journal of Clinical Pharmacology 07/2012; 69(3). DOI:10.1007/s00228-012-1334-2 · 2.97 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Objectives:
Adalimumab, etanercept and infliximab are effective TNF inhibitors (TNFis) in the treatment of RA, but no randomized clinical trials have compared the three agents. Prior observational data are not consistent. We compared their effectiveness over 1 year in a prospective cohort.
Analyses were performed on subjects' first episode of TNFi use in the Rheumatic Diseases Portuguese Register, Reuma.pt. The primary outcome was the proportion of patients with European League Against Rheumatism good response sustained at two consecutive observations separated by 3 months during the first year of TNFi use. Comparisons were performed using conventional adjusted logistic regression, as well as matching subjects across the three agents using a propensity score. In addition, baseline predictors of treatment response to TNFi were identified.
The study cohort included 617 RA patients, 250 starting etanercept, 206 infliximab and 161 adalimumab. Good response was achieved by 59.6% for adalimumab, 59.2% for etanercept and 51.9% for infliximab (P = 0.21). The modelled probability of good response did not significantly differ across agents (etanercept vs adalimumab OR = 0.97, 95% CI 0.55, 1.71; etanercept vs infliximab OR = 1.25, 95% CI 0.74, 2.12; infliximab vs adalimumab OR = 0.80, 95% CI 0.47, 1.36). Matched propensity score analyses also showed no significant treatment response differences. Greater educational attainment was a predictor of better response, while smoking, presence of ACPA, glucocorticoid use and worse physician assessment of disease activity at baseline each predicted a reduced likelihood of treatment response.
Over 1 year, we found no difference in effectiveness between adalimumab, etanercept and infliximab.
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