Background In the absence of sufficient data directly comparing two or more treatments, indirect comparisons using network meta-analyses (NMA) across trials can potentially provide useful information to guide the use of treatments. Under current contrast-based methods for NMA of binary outcomes, which do not model the “baseline” risks and focus on modeling the relative treatment effects, the patient-centered measures including the overall treatment-specific event rates and risk differences are not provided, which may create some unnecessary obstacles for patients to comprehensively understand and trade-off efficacy and safety measures. Many NMAs only report odds ratios which are commonly misinterpreted as risk ratios by many physicians, patients and their care givers. Purpose We aim to develop network meta-analysis to accurately estimate the overall treatment-specific event rates. Methods A novel Bayesian hierarchical model, developed from a missing data perspective, that borrows information across multiple treatment arms, is used to illustrate how treatment-specific event proportions, risk differences (RD) and relative risks (RR) can be computed in NMAs. We first compare our approach to alternative methods using two hypothetical NMAs assuming either a fixe RR or a fixed RD, and then use two published NMAs on new-generation anti-depressants and antimanic drugs to illustrate the improved reporting of NMAs possible with this new approach. Results In the hypothetical NMAs, our approach outperforms current contrast-based NMA methods in terms of bias. In the NMAs on new-generation anti-depressants and on antimanic drugs, the outcomes were common with proportions ranging from 0.21 to 0.62. As expected, the RR estimates differ from ORs. In addition, differences in the magnitude of relative treatment effects and the statistical significance of several pairwise comparisons from previous report could lead to different treatment recommendations. Limitations First, to facilitate the estimation of overall treatment-specific event proportions, we assume that each study hypothetically compares all treatments, with unstudied arms being missing at random conditional on the observed arms. However, it is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to “nonignorable missingness” and potentially bias our event rate estimation. Second, we have not considered methods to identify and account for potential inconsistency in our missing data network meta-analysis framework. Both methods await further development. Conclusions The proposed NMA method can accurately estimate treatment-specific event rates or proportions, RDs, and RRs, and is recommended in practice. Application of this approach can lead to different conclusions, as illustrated here, from current NMA models that only estimate ORs.

Women are often underrepresented in randomized clinical trials (RCT) of HIV-1 drugs. As a result, determining whether women have different virologic outcomes compared to men is not always possible because the gender-related analyses usually lack statistical power. To address this important public health concern, the Food and Drug Administration's (FDA) Division of Antiviral Products (DAVP) created a database including 20,328 HIV-positive subjects from 40 RCTs in 18 New Drug Applications (NDAs) submitted to the FDA between 2000 and 2008. These RCTs were conducted for at least 48 weeks in duration and were used to support approval of new molecular entity, new formulation, or major label change. To delineate potential gender differences in antiretroviral treatment (ART), we evaluated the percentage of subjects with HIV RNA less than 50 copies per milliliter at 48 weeks. Analyses of the database represent the most systematic review of gender-related ART efficacy data to date. Overall, the meta-analyses did not demonstrate statistically or clinically significant gender differences in virologic outcome at week 48. However, the corresponding subgroup analyses appear to show several statistically significant gender differences favoring males.

In comparative effectiveness research, it is often of interest to calibrate treatment effect estimates from a clinical trial to a target population that differs from the study population. One important application is an indirect comparison of a new treatment with a placebo control on the basis of two separate randomized clinical trials: a non-inferiority trial comparing the new treatment with an active control and a historical trial comparing the active control with placebo. The available methods for treatment effect calibration include an outcome regression (OR) method based on a regression model for the outcome and a weighting method based on a propensity score (PS) model. This article proposes new methods for treatment effect calibration: one based on a conditional effect (CE) model and two doubly robust (DR) methods. The first DR method involves a PS model and an OR model, is asymptotically valid if either model is correct, and attains the semiparametric information bound if both models are correct. The second DR method involves a PS model, a CE model, and possibly an OR model, is asymptotically valid under the union of the PS and CE models, and attains the semiparametric information bound if all three models are correct. The various methods are compared in a simulation study and applied to recent clinical trials for treating human immunodeficiency virus infection.

Summary There is growing interest in understanding the heterogeneity of treatment effects (HTE), which has important implications in treatment evaluation and selection. The standard approach to assessing HTE (i.e. subgroup analyses based on known effect modifiers) is informative about the heterogeneity between subpopulations but not within. It is arguably more informative to assess HTE in terms of individual treatment effects, which can be defined by using potential outcomes. However, estimation of HTE based on potential outcomes is challenged by the lack of complete identifiability. The paper proposes methods to deal with the identifiability problem by using relevant information in baseline covariates and repeated measurements. If a set of covariates is sufficient for explaining the dependence between potential outcomes, the joint distribution of potential outcomes and hence all measures of HTE will then be identified under a conditional independence assumption. Possible violations of this assumption can be addressed by including a random effect to account for residual dependence or by specifying the conditional dependence structure directly. The methods proposed are shown to reduce effectively the uncertainty about HTE in a trial of human immunodeficiency virus.

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