Outcomes following three-line vision loss during treatment of neovascular age-related macular degeneration: subgroup analyses from MARINA and ANCHOR

This study aims to assess the impact of continued ranibizumab treatment for neovascular age-related macular degeneration on patients from the MARINA and ANCHOR randomised clinical studies who lost ≥ 3 lines of best-corrected visual acuity (BCVA) at any time during the first year of treatment.

Ten years of meta-analyses on erythropoiesis-stimulating agents in cancer patients

Since erythropoiesis-stimulating agents (ESAs) were licensed in 1993, more than 70 randomized controlled trials and more than 20 meta-analyses and systematic reviews on their effectiveness were conducted. Here, we present a systematic review on the meta-analyses of trials evaluating ESAs in cancer patients.

Multiplicity of data in trial reports and the reliability of meta-analyses: empirical study

Objectives To examine the extent of multiplicity of data in trial reports and to assess the impact of multiplicity on meta-analysis results. Design Empirical study on a cohort of Cochrane systematic reviews. Data sources All Cochrane systematic reviews published from issue 3 in 2006 to issue 2 in 2007 that presented a result as a standardised mean difference (SMD). We retrieved trial reports contributing to the first SMD result in each review, and downloaded review protocols. We used these SMDs to identify a specific outcome for each meta-analysis from its protocol. Review methods Reviews were eligible if SMD results were based on two to ten randomised trials and if protocols described the outcome. We excluded reviews if they only presented results of subgroup analyses. Based on review protocols and index outcomes, two observers independently extracted the data necessary to calculate SMDs from the original trial reports for any intervention group, time point, or outcome measure compatible with the protocol. From the extracted data, we used Monte Carlo simulations to calculate all possible SMDs for every meta-analysis. Results We identified 19 eligible meta-analyses (including 83 trials). Published review protocols often lacked information about which data to choose. Twenty-four (29%) trials reported data for multiple intervention groups, 30 (36%) reported data for multiple time points, and 29 (35%) reported the index outcome measured on multiple scales. In 18 meta-analyses, we found multiplicity of data in at least one trial report; the median difference between the smallest and largest SMD results within a meta-analysis was 0.40 standard deviation units (range 0.04 to 0.91). Conclusions Multiplicity of data can affect the findings of systematic reviews and meta-analyses. To reduce the risk of bias, reviews and meta-analyses should comply with prespecified protocols that clearly identify time points, intervention groups, and scales of interest.

An overview of the objectives of and the approaches to propensity score analyses

The assessment of treatment effects from observational studies may be biased with patients not randomly allocated to the experimental or control group. One way to overcome this conceptual shortcoming in the design of such studies is the use of propensity scores to adjust for differences of the characteristics between patients treated with experimental and control interventions. The propensity score is defined as the probability that a patient received the experimental intervention conditional on pre-treatment characteristics at baseline. Here, we review how propensity scores are estimated and how they can help in adjusting the treatment effect for baseline imbalances. We further discuss how to evaluate adequate overlap of baseline characteristics between patient groups, provide guidelines for variable selection and model building in modelling the propensity score, and review different methods of propensity score adjustments. We conclude that propensity analyses may help in evaluating the comparability of patients in observational studies, and may account for more potential confounding factors than conventional covariate adjustment approaches. However, bias due to unmeasured confounding cannot be corrected for.