Sequential change detection and monitoring of temporal trends in random-effects meta-analysis

Cumulative meta-analysis: What works

To present time-varying evidence, cumulative meta-analysis (CMA) updates results of previous meta-analyses to incorporate new study results. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects meta-analysis. We use the standardized mean difference (SMD) as an effect measure of interest. For CMA, we compare the standard inverse-variance-weighted estimation of the overall effect using REML-based estimation of between-study variance τ 2 with the sample-size-weighted estimation of the effect accompanied by Kulinskaya-Dollinger-Bjørkestøl (Biometrics. 2011; 67:203-212) (KDB) estimation of τ 2 . For all methods, we consider Type 1 error under no shift and power under a shift in the mean in the random-effects model. To ameliorate the lack of power in CMA, we introduce two-stage CMA, in which τ 2 is estimated at Stage 1 (from the first 5-10 studies), and further CMA monitors a target value of effect, keeping the τ 2 value fixed. We recommend this two-stage CMA combined with cumulative testing for positive shift in τ 2 . In practice, use of CMA requires at least 15-20 studies.

Are the effects of blood pressure lowering treatment diminishing?: meta-regression analyses

Background

Previous studies have suggested that the effects of medical interventions tend to diminish over time. We investigated whether the effects of blood pressure lowering treatment on all-cause mortality and stroke have diminished over time.

Methods

We conducted meta-regression analyses. We extracted the target trials from two recently published comprehensive systematic reviews and meta-analyses. Adopted variables were relative risk (RR) of all-cause mortality and stroke, trial start year, mean age, sample size, baseline systolic blood pressure (SBP), difference in attained SBP reduction between intervention groups and control groups (SBP difference), and regional dummies. We implemented single meta-regressions, in which the dependent variable was the log of RR and the explanatory variable was each of other adopted variables. We also conducted multiple meta-regressions, in which the dependent variable was the log of RR and explanatory variables were all of other adopted variables. Our variable of greatest interest was trial start year.

Results

The included reviews assessed 85 trials with a total of 343,126 participants. Although trial start year was positively associated with the log of RR in the results of single meta-regressions, it lost significance in multiple meta-regressions for both all-cause mortality and stroke.

Conclusions

The effects of blood pressure lowering treatment on all-cause mortality and stroke have not diminished over time.

Electronic supplementary material

The online version of this article (10.1186/s40885-018-0101-9) contains supplementary material, which is available to authorized users.

Sequential change detection and monitoring of temporal trends in random-effects meta-analysis

Temporal changes in magnitude of effect sizes reported in many areas of research are a threat to the credibility of the results and conclusions of meta-analysis. Numerous sequential methods for meta-analysis have been proposed to detect changes and monitor trends in effect sizes so that meta-analysis can be updated when necessary and interpreted based on the time it was conducted. The difficulties of sequential meta-analysis under the random-effects model are caused by dependencies in increments introduced by the estimation of the heterogeneity parameter τ2 . In this paper, we propose the use of a retrospective cumulative sum (CUSUM)-type test with bootstrap critical values. This method allows retrospective analysis of the past trajectory of cumulative effects in random-effects meta-analysis and its visualization on a chart similar to CUSUM chart. Simulation results show that the new method demonstrates good control of Type I error regardless of the number or size of the studies and the amount of heterogeneity. Application of the new method is illustrated on two examples of medical meta-analyses. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.