Association between increased antenatal vaginal pH and preterm birth rate : a systematic review

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Background: Worldwide, 14.9 million infants (11%) are born preterm each year. Up to 40% of preterm births (PTBs) are associated with genital tract infections. The vaginal pH can reflect changes in the vaginal milieu and, if elevated, indicates an abnormal flora or infection. Objective: The aim of the study was to investigate whether an increased antenatal vaginal pH >4.5 in pre-labour pregnant women is associated with an increased PTB rate <37 completed weeks gestation. Search strategy Key databases included SCOPUS, EMBASE, MEDLINE, PsycInfo and the Cochrane Central Register of Controlled Trials, complemented by hand search, up to January 2017. Selection criteria Primary research reporting vaginal pH assessment in pre-labour pregnant women and PTB rate. Data collection and analysis: Data extraction and appraisal were carried out in a pre-defined standardised manner, applying the Newcastle-Ottawa scale (NOS) and Cochrane risk of bias tool. Analysis included calculation of risk difference (RD) and narrative synthesis. It was decided to abstain from pooling of the studies due to missing information in important moderators. Main results: Of 986 identified records, 30 were included in the systematic review. The risk of bias was considered mostly high (40%) or moderate (37%). Fifteen studies permitted a calculation of RD. Of these, 14 (93%) indicated a positive association between increased antenatal vaginal pH and PTB (RD range: 0.02-0.75). Conclusions: An increased antenatal vaginal pH >4.5 may be associated with a higher risk for PTB. It is recommended to conduct a randomised controlled trial (RCT) to investigate the effectiveness of antenatal pH screening to prevent PTB. Tweetable abstract Pregnant women with an increased vaginal pH >4.5 may be at higher risk to experience preterm birth

An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis

Abstract Background A number of strategies have been proposed to handle missing binary outcome data (MOD) in systematic reviews. However, none of these have been evaluated empirically in a series of published systematic reviews. Methods Using published systematic reviews with network meta-analysis (NMA) from a wide range of health-related fields, we evaluated comparatively the most frequently described Bayesian modelling strategies for MOD in terms of log odds ratio (log OR), between-trial variance, inconsistency factor (i.e. difference between direct and indirect estimates for a comparison), surface under the cumulative ranking (SUCRA) and rankings. We extended the Bayesian random-effects NMA model to incorporate the informative missingness odds ratio (IMOR) parameter, and applied the node-splitting approach to investigate inconsistency locally. We considered both pattern-mixture and selection models, different structures for prior distribution of log IMOR, and different scenarios for MOD. To illustrate level of agreement between different strategies and scenarios, we used Bland-Altman plots. Results Addressing MOD using extreme scenarios and ignoring the uncertainty about the scenarios led to systematically different and more precise log ORs compared to modelling MOD under the missing at random (MAR) assumption. Hierarchical structure of log IMORs led to lower between-trial variance, especially in the case of substantial MOD. Assuming common-within-network or trial-specific log IMORs yielded similar posterior results for all NMA estimates, whereas intervention-specific structure systematically inflated uncertainty around log ORs and SUCRAs. Pattern-mixture model agreed with selection model, particularly under the trial-specific structure; however, selection model systematically reduced precision around log IMORs. Overall, different strategies and scenarios mostly had good agreement in the case of low MOD. Conclusions Addressing MOD using extreme scenarios and/or ignoring the uncertainty about the scenarios may negatively affect NMA estimates. Modelling MOD via the IMOR parameter can ensure bias-adjusted estimates and offer valuable insights into missingness mechanisms. The researcher should seek an expert opinion in order to decide on the structure of log IMOR that best aligns to the condition and interventions studied and to define a proper prior distribution for log IMOR. Our findings also apply to pairwise meta-analyses

Meta-analysis: Fixed-effect model

A meta-analysis, a statistical combination of data from selected studies, is implemented by choosing a priori between 2 popular statistical models: fixed-effect and random-effects models.1 The choice of the appropriate model for the analysis is critical to ensure the credibility of the results and depends on both the goals of the analysis and the assumptions of the models.1 In this section, we introduce these 2 models, describe how to perform a meta-analysis under these models, and apply a real-data example from a published systematic review

Publication bias: Graphical and statistical methods.

Various statistical approaches and visual tools have been developed to detect, estimate, and evaluate the impact of publication bias in meta-analysis results. In this article, we present the most popular statistical methods and graphic tools to address publication bias using an example