Multilevel network meta-regression: methods and implementation:<i>in workshop </i>Time to implement multilevel network meta-regression rather than matching adjusted indirect comparisons

PURPOSE: Multi-level network meta-regression (ML-NMR) extends the standard network meta-analysis framework to leverage individual patient data and aggregate data when comparing multiple treatments while adjusting for differences in populations between trials. Unlike previous population adjustment approaches, ML-NMR is applicable in networks of any size, avoids aggregation bias and issues with non-collapsible effect measures, and crucially for decision-making produces estimates in any target population.DESCRIPTION: Workshop attendees will obtain a working knowledge of the ML-NMR method, its advantages, and considerations for implementation. Dr. Jansen will chair the session and introduce ML-NMR in the context of the challenges with existing methods (10 min.). Dr. Phillippo will explain the statistical methods for ML-NMR, highlight advantages relative to existing methods, and provide an overview of how to implement the method using the multinma R package in terms of the syntax and features (15 min.). Ms. Cope will illustrate how these methods can be applied in a case study regarding the comparative efficacy of alternative interventions for triple-class exposed relapsed refractory multiple myeloma. This will include audience participation regarding selection of covariates, alternative time-to-event models, conditional vs. marginal estimates, and target populations for prediction (15 min). Mr. Klijn will describe lessons learned and recommendations for implementation of ML-NMR (15 min). Questions from the audience will be addressed (5 min) and this interactive workshop will be valuable to researchers and industry analysts interested in comparative efficacy research for health technology assessments

Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis

Network meta-analysis combines aggregate data (AgD) from multiple randomised controlled trials, assuming that any effect modifiers are balanced across populations. Individual patient data (IPD) meta-regression is the "gold standard" method to relax this assumption, however IPD are frequently only available in a subset of studies. Multilevel network meta-regression (ML-NMR) extends IPD meta-regression to incorporate AgD studies whilst avoiding aggregation bias, but currently requires the aggregate-level likelihood to have a known closed form. Notably, this prevents application to time-to-event outcomes. We extend ML-NMR to individual-level likelihoods of any form, by integrating the individual-level likelihood function over the AgD covariate distributions to obtain the respective marginal likelihood contributions. We illustrate with two examples of time-to-event outcomes, showing the performance of ML-NMR in a simulated comparison with little loss of precision from a full IPD analysis, and demonstrating flexible modelling of baseline hazards using cubic M-splines with synthetic data on newly diagnosed multiple myeloma. ML-NMR is a general method for synthesising individual and aggregate level data in networks of all sizes. Extension to general likelihoods, including for survival outcomes, greatly increases the applicability of the method. R and Stan code is provided, and the methods are implemented in the multinma R package.Comment: 43 pages, 8 figures (corrected metadata

Multinma: A comprehensive R package for network meta-analysis of survival outcomes with aggregate data, individual patient data, or a mixture of both

IntroductionSurvival or time-to-event outcomes are commonplace in disease areas such as oncology. Healthcare decision makers require estimates of relative efficacy between different treatment options, however treatments of interest are frequently not all compared in head-to-head randomised controlled trials, and so indirect comparison and network meta-analysis (NMA) methods are required to synthesise evidence from a connected network of trials and treatments. An extension of NMA, multilevel network meta-regression (ML-NMR), is increasingly used to account for differences in effect modifiers between populations where individual patient data are available from one or more trials. However, to date there has been no user-friendly software package that can perform NMA or ML-NMR with survival outcomes; instead analysts have needed to rely on complex bespoke modelling code. MethodsA recent update to the multinma R package provides a user-friendly suite of models and tools for synthesising survival outcomes from multiple trials, with aggregate data, individual patient data, or mixtures of both. Models are fitted in a Bayesian framework using Stan. A full range of parametric proportional hazards and accelerated failure time survival distributions are implemented, along with flexible baseline hazard models via M-splines or piecewise exponential hazards with a novel random walk shrinkage prior that avoids overfitting. Shape parameters may be stratified or regressed on treatment arm and/or covariates to relax proportionality. Right, left, and interval censoring, and delayed entry are all supported.ResultsWe present analyses of two case studies using the multinma package. First, we performed a NMA of published aggregate data from a network of treatments for advanced non-small cell lung cancer using flexible M-spline baseline hazards. We introduced treatment effects onto the spline coefficients to account for non-proportional hazards, and produced estimated survival curves in a target population required for further economic modelling.Second, we performed a ML-NMR using a mixture of individual patient data and aggregate data from a network of treatments for newly-diagnosed multiple myeloma. We adjusted for effect-modifying covariates, and produced population-adjusted estimates for target populations of interest to decision-making. Covariate adjustment removed evidence for non-proportional hazards that was present in unadjusted models.ConclusionsThe multinma package makes NMA and ML-NMR methods accessible to a broad audience. The latest update to include a suite of functionality for survival analysis facilitates application of these methods to widespread settings such as oncology, where until now there was no user-friendly software available

Assessing the robustness of recommendations made in a guideline on specialist neonatal respiratory care in babies born preterm with threshold analysis

View from Obiri (Ubirr) Rock, Kakadu National Park, shows rocky ourcrops in distance and plains in mid distance.Crawford, Pauline

Multinma: A comprehensive R package for network meta-analysis of survival outcomes with aggregate data, individual patient data, or a mixture of both

IntroductionSurvival or time-to-event outcomes are commonplace in disease areas such as oncology. Healthcare decision makers require estimates of relative efficacy between different treatment options, however treatments of interest are frequently not all compared in head-to-head randomised controlled trials, and so indirect comparison and network meta-analysis (NMA) methods are required to synthesise evidence from a connected network of trials and treatments. An extension of NMA, multilevel network meta-regression (ML-NMR), is increasingly used to account for differences in effect modifiers between populations where individual patient data are available from one or more trials. However, to date there has been no user-friendly software package that can perform NMA or ML-NMR with survival outcomes; instead analysts have needed to rely on complex bespoke modelling code. MethodsA recent update to the multinma R package provides a user-friendly suite of models and tools for synthesising survival outcomes from multiple trials, with aggregate data, individual patient data, or mixtures of both. Models are fitted in a Bayesian framework using Stan. A full range of parametric proportional hazards and accelerated failure time survival distributions are implemented, along with flexible baseline hazard models via M-splines or piecewise exponential hazards with a novel random walk shrinkage prior that avoids overfitting. Shape parameters may be stratified or regressed on treatment arm and/or covariates to relax proportionality. Right, left, and interval censoring, and delayed entry are all supported.ResultsWe present analyses of two case studies using the multinma package. First, we performed a NMA of published aggregate data from a network of treatments for advanced non-small cell lung cancer using flexible M-spline baseline hazards. We introduced treatment effects onto the spline coefficients to account for non-proportional hazards, and produced estimated survival curves in a target population required for further economic modelling.Second, we performed a ML-NMR using a mixture of individual patient data and aggregate data from a network of treatments for newly-diagnosed multiple myeloma. We adjusted for effect-modifying covariates, and produced population-adjusted estimates for target populations of interest to decision-making. Covariate adjustment removed evidence for non-proportional hazards that was present in unadjusted models.ConclusionsThe multinma package makes NMA and ML-NMR methods accessible to a broad audience. The latest update to include a suite of functionality for survival analysis facilitates application of these methods to widespread settings such as oncology, where until now there was no user-friendly software available

Using individual participant data to improve network meta-analysis projects.

A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics