Software to Conduct a Meta-Analysis and Network Meta-Analysis

Statistical software for meta-analysis (MA) and network meta-analysis (NMA) have become indispensable for researchers. The aim of this chapter is to introduce key features of MA and NMA software to compare the effectiveness of interventions. Commonly used or routinely maintained statistical software are reviewed, including commercial and open-sourced programs such as Stata, R and Excel plug-ins. It does not provide a comprehensive overview of all features available in the software covered. Rather, it focuses on the essential features required to carry out an MA or NMA . This chapter begins with a review of key considerations when implementing an MA or NMA , then presents a summary of the software. Key features of each software option are discussed.

MultiBUGS: A Parallel Implementation of the BUGS Modelling Framework for Faster Bayesian Inference

MultiBUGS is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelises evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelises sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelise the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational cores.

Software and package applicating for network meta-analysis: A usage-based comparative study

OBJECTIVE

To compare and analyze the characteristics and functions of software applications for network meta-analysis (NMA).

METHODS

PubMed, EMbase, The Cochrane Library, the official websites of Bayesian inference Using Gibbs Sampling (BUGS), Stata and R, and Google were searched to collect the software and packages for performing NMA; software and packages published up to March 2016 were included. After collecting the software, packages, and their user guides, we used the software and packages to calculate a typical example. All characteristics, functions, and computed results were compared and analyzed.

RESULTS

Ten types of software were included, including programming and non-programming software. They were developed mainly based on Bayesian or frequentist theory. Most types of software have the characteristics of easy operation, easy mastery, exact calculation, or excellent graphing. However, there was no single software that performed accurate calculations with superior graphing; this could only be achieved through the combination of two or more types of software.

CONCLUSION

This study suggests that the user should choose the appropriate software according to personal programming basis, operational habits, and financial ability. Then, the choice of the combination of BUGS and R (or Stata) software to perform the NMA is considered.

An efficient technique for Bayesian modeling of family data using the BUGS software

Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement with the BUGS (Bayesian inference Using Gibbs Sampling) software because of the high-dimensional covariance matrix of the random effects. This paper describes an efficient parameterization that utilizes the singular value decomposition of the covariance matrix of random effects, includes the BUGS code for such implementation, and extends the parameterization to generalized linear mixed models. The implementation is evaluated using simulated data and an example from a large family-based study is presented with a comparison to other existing methods.

A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models

In the context of state-space modeling, conventional usage of the deviance information criterion (DIC) evaluates the ability of the model to predict an observation at time t given the underlying state at time t. Motivated by the failure of conventional DIC to clearly choose between competing multivariate nonlinear Bayesian state-space models for coho salmon population dynamics, and the computational challenge of alternatives, this work proposes a one-step-ahead DIC, DICp, where prediction is conditional on the state at the previous time point. Simulations revealed that DICp worked well for choosing between state-space models with different process or observation equations. In contrast, conventional DIC could be grossly misleading, with a strong preference for the wrong model. This can be explained by its failure to account for inflated estimates of process error arising from the model mis-specification. DICp is not based on a true conditional likelihood, but is shown to have interpretation as a pseudo-DIC in which the compensatory behavior of the inflated process errors is eliminated. It can be easily calculated using the DIC monitors within popular BUGS software when the process and observation equations are conjugate. The improved performance of DICp is demonstrated by application to the multi-stage modeling of coho salmon abundance in Lobster Creek, Oregon.

Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis

Meta-analysis is often undertaken in two stages, with each study analysed separately in stage 1 and estimates combined across studies in stage 2. The study-specific estimates are assumed to arise from normal distributions with known variances equal to their corresponding estimates. In contrast, a one-stage analysis estimates all parameters simultaneously. A Bayesian one-stage approach offers additional advantages, such as the acknowledgement of uncertainty in all parameters and greater flexibility. However, there are situations when a two-stage strategy is compelling, e.g. when study-specific analyses are complex and/or time consuming. We present a novel method for fitting the full Bayesian model in two stages, hence benefiting from its advantages while retaining the convenience and flexibility of a two-stage approach. Using Markov chain Monte Carlo methods, posteriors for the parameters of interest are derived separately for each study. These are then used as proposal distributions in a computationally efficient second stage. We illustrate these ideas on a small binomial data set; we also analyse motivating data on the growth and rupture of abdominal aortic aneurysms. The two-stage Bayesian approach closely reproduces a one-stage analysis when it can be undertaken, but can also be easily carried out when a one-stage approach is difficult or impossible.

A review of Bayesian state-space modelling of capture-recapture-recovery data

Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture-recapture-recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture-recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.