Abstract
BackgroundIn the absence of randomized clinical trials, meta-analysis of individual patient data (IPD) from observational studies may provide the most accurate effect estimates for an intervention. However, confounding by indication remains an important concern that can be addressed by incorporating individual patient covariates in different ways. We compared different analytic approaches to account for confounding in IPD from patients treated for multi-drug resistant tuberculosis (MDR-TB).MethodsTwo antibiotic classes were evaluated, fluoroquinolones—considered the cornerstone of effective MDR-TB treatment—and macrolides, which are known to be safe, yet are ineffective in vitro. The primary outcome was treatment success against treatment failure, relapse or death. Effect estimates were obtained using multivariable and propensity-score based approaches.ResultsFluoroquinolone antibiotics were used in 28 included studies, within which 6,612 patients received a fluoroquinolone and 723 patients did not. Macrolides were used in 15 included studies, within which 459 patients received this class of antibiotics and 3,670 did not. Both standard multivariable regression and propensity score-based methods resulted in similar effect estimates for early and late generation fluoroquinolones, while macrolide antibiotics use was associated with reduced treatment success.ConclusionsIn this individual patient data meta-analysis, standard multivariable and propensity-score based methods of adjusting for individual patient covariates for observational studies yielded produced similar effect estimates. Even when adjustment is made for potential confounding, interpretation of adjusted estimates must still consider the potential for residual bias.
Highlights
In the absence of randomized clinical trials, data from non-experimental observational studies may provide the only data to evaluate complex medical interventions
Macrolides were used in 15 included studies, within which 459 patients received this class of antibiotics and 3,670 did not. Both standard multivariable regression and propensity score-based methods resulted in similar effect estimates for early and late generation fluoroquinolones, while macrolide antibiotics use was associated with reduced treatment success
Data from these observational studies can be pooled to provide an estimate of effect that may be more precise than that obtained by a single study [1, 2]
Summary
In the absence of randomized clinical trials, data from non-experimental observational studies may provide the only data to evaluate complex medical interventions. Multivariable regression is used to account for differences in measured covariates between subjects This method may not fully adjust for confounding by indication occurring if the health status of patients affects treatment allocation. Alternative analytic approaches based upon propensity scores have been proposed that may provide more precise estimates of the treatment effect in observational studies in which confounding by indication may occur [6]. Propensity score-based analytic methods have been widely used in individual studies This method has not yet been operationalized for IPD meta-analyses. In the absence of randomized clinical trials, meta-analysis of individual patient data (IPD) from observational studies may provide the most accurate effect estimates for an intervention. We compared different analytic approaches to account for confounding in IPD from patients treated for multidrug resistant tuberculosis (MDR-TB)
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