Conducting Meta-Analyses in R with the metafor Package

The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.

The meaning of adherence when behavioral risk patterns vary : obscured use- and method-effectiveness in HIV-prevention trials

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Conducting Meta-Analyses in R with the metafor Package

The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias

The poolr Package for Combining Independent and Dependent p Values

The poolr package provides an implementation of a variety of methods for pooling (i.e., combining) p values, including Fisher's method, Stouffer's method, the inverse chisquare method, the binomial test, the Bonferroni method, and Tippett's method. More importantly, the methods can be adjusted to account for dependence among the tests from which the p values have been derived assuming multivariate normality among the test statistics. All methods can be adjusted based on an estimate of the effective number of tests or by using an empirically-derived null distribution based on pseudo replicates that mimics a proper permutation test. For the Fisher, Stouffer, and inverse chi-square methods, the test statistics can also be directly generalized to account for dependence, leading to Brown's method, Strube's method, and the generalized inverse chi-square method. In this paper, we describe the various methods, discuss their implementation in the package, illustrate their use based on several examples, and compare the poolr package with several other packages that can be used to combine p values

Evidence That Environmental and Genetic Risks for Psychotic Disorder May Operate by Impacting on Connections Between Core Symptoms of Perceptual Alteration and Delusional Ideation

Background: Relational models of psychopathology propose that symptoms are dynamically connected and hypothesize that genetic and environmental influences moderate the strength of these symptom connections. Previous findings suggest that the interplay between hallucinations and delusions may play a crucial role in the development of psychotic disorder. The current study examined whether the connection between hallucinations and delusions is impacted by proxy genetic and environmental risk factors. Methods: Hallucinations and delusions at baseline and at 3-year follow-up were assessed in a sample of 1054 healthy siblings and 918 parents of 1109 patients with psychosis, and in 589 healthy controls (no familial psychosis risk). Environmental factors assessed were cannabis use, childhood trauma, and urbanicity during childhood. Logistic regression analyses tested whether familial psychosis risk predicted increased risk of delusions, given presence of hallucinations. Moderating effects of environmental factors on the hallucination-delusion association were tested in a similar fashion, restricted to the control and sibling groups. Results: The risk of delusions, given hallucinations, was associated with proxy genetic risk: 53% in parents, 47% in siblings, and 36% in controls. The hallucination-delusion association was stronger in those reporting cannabis use (risk difference: 32%) and childhood trauma (risk difference: 15%) although not all associations were statistically conclusive (respectively: p = .037; p = .054). A directionally similar but nonsignificant effect was found for urb anicity during childhood (risk difference: 14%, p = .357). Conclusion: The strength of the connection between delusions and hallucinations is associated with familial and environmental risks for psychotic disorder, suggesting that specific symptom connections in the early psychosis psychopathology network are informative of underlying mechanisms

Two new methods to fit models for network meta-analysis with random inconsistency effects.

BACKGROUND: Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. METHODS: The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. RESULTS: We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. CONCLUSIONS: The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported

Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models

The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43–0.48] and resistant infections by 7.2 [14.00–0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call “adjustable cycling/mixing”. In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that “adjustable cycling” is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that “adjustable cycling” suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings

Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study

Background The purpose of this simulation study is to assess the performance of multiple imputation compared to complete case analysis when assumptions of missing data mechanisms are violated. Methods The authors performed a stochastic simulation study to assess the performance of Complete Case (CC) analysis and Multiple Imputation (MI) with different missing data mechanisms (missing completely at random (MCAR), at random (MAR), and not at random (MNAR)). The study focused on the point estimation of regression coefficients and standard errors. Results When data were MAR conditional on Y, CC analysis resulted in biased regression coefficients; they were all underestimated in our scenarios. In these scenarios, analysis after MI gave correct estimates. Yet, in case of MNAR MI yielded biased regression coefficients, while CC analysis performed well.Conclusion The authors demonstrated that MI was only superior to CC analysis in case of MCAR or MAR. In some scenarios CC may be superior over MI. Often it is not feasible to identify the reason why data in a given dataset are missing. Therefore, emphasis should be put on reporting the extent of missing values, the method used to address them, and the assumptions that were made about the mechanism that caused missing data