Visualizing the target estimand in comparative effectiveness studies with multiple treatments.

Aim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and propose two visualization tools to clarify the target estimand. Materials & methods: We conduct a simulation study to demonstrate, with bivariate ellipses and joy plots, that differences in covariate distributions across treatment groups may affect the external validity of treatment effect estimates. We showcase how these visualization tools can facilitate the interpretation of target estimands in a case study comparing the effectiveness of teriflunomide (TERI), dimethyl fumarate (DMF) and natalizumab (NAT) on manual dexterity in patients with multiple sclerosis. Results: In the simulation study, estimates of the treatment effect greatly differed depending on the target population. For example, when comparing treatment B with C, the estimated treatment effect (and respective standard error) varied from -0.27 (0.03) to -0.37 (0.04) in the type of patients initially receiving treatment B and C, respectively. Visualization of the matched samples revealed that covariate distributions vary for each comparison and cannot be used to target one common treatment effect for the three treatment comparisons. In the case study, the bivariate distribution of age and disease duration varied across the population of patients receiving TERI, DMF or NAT. Although results suggest that DMF and NAT improve manual dexterity at 1 year compared with TERI, the effectiveness of DMF versus NAT differs depending on which target estimand is used. Conclusion: Visualization tools may help to clarify the target population in comparative effectiveness studies and resolve ambiguity about the interpretation of estimated treatment effects.

Handling related publications reporting real-world evidence in network meta-analysis: a case study in multiple sclerosis.

Aim: The presence of two or more publications that report on overlapping patient cohorts poses a challenge for quantitatively synthesizing real-world evidence (RWE) studies. Thus, we evaluated eight approaches for handling such related publications in network meta-analyses (NMA) of RWE studies. Methods: Bayesian NMAs were conducted to estimate the annualized relapse rate (ARR) of disease-modifying therapies in multiple sclerosis. The NMA explored the impact of hierarchically selecting one pivotal study from related publications versus including all of them while adjusting for correlations. Results: When selecting one pivotal study from related publications, the ARR ratios were mostly similar regardless of the pivotal study selected. When including all related publications, there were shifts in the point estimates and the statistical significance. Conclusion: An a priori hierarchy should guide the selection among related publications in NMAs of RWE. Sensitivity analyses modifying the hierarchy should be considered for networks with few or small studies.

Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis.

Real-world data sources offer opportunities to compare the effectiveness of treatments in practical clinical settings. However, relevant outcomes are often recorded selectively and collected at irregular measurement times. It is therefore common to convert the available visits to a standardized schedule with equally spaced visits. Although more advanced imputation methods exist, they are not designed to recover longitudinal outcome trajectories and typically assume that missingness is non-informative. We, therefore, propose an extension of multilevel multiple imputation methods to facilitate the analysis of real-world outcome data that is collected at irregular observation times. We illustrate multilevel multiple imputation in a case study evaluating two disease-modifying therapies for multiple sclerosis in terms of time to confirmed disability progression. This survival outcome is derived from repeated measurements of the Expanded Disability Status Scale, which is collected when patients come to the healthcare center for a clinical visit and for which longitudinal trajectories can be estimated. Subsequently, we perform a simulation study to compare the performance of multilevel multiple imputation to commonly used single imputation methods. Results indicate that multilevel multiple imputation leads to less biased treatment effect estimates and improves the coverage of confidence intervals, even when outcomes are missing not at random.

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions.

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.

Recommendations for the use of propensity score methods in multiple sclerosis research.

BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling.

BACKGROUND: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS). OBJECTIVE: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression. METHODS: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance. RESULTS: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. Overall model discrimination performance was relatively poor (all C-indices ⩽ 0.65) across all models and across different definitions of progression. CONCLUSION: Inconsistency of prognostic factor importance ranking confirmed the relatively poor prediction ability of baseline factors in modeling disease progression in MS. Our findings underline the importance to explore alternative predictors as well as alternative definitions of commonly used endpoints.

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes.

It is widely recommended that any developed-diagnostic or prognostic-prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c-statistic, the observed:expected ratio and the calibration slope. Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to improve estimation of between-study heterogeneity in small samples. Finally, we illustrate all methods using two examples: meta-analysis of the predictive performance of EuroSCORE II and of the Framingham Risk Score. All examples and meta-analysis models have been implemented in our newly developed R package "metamisc".

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

An overview of methods for network meta-analysis using individual participant data: when do benefits arise?

Network meta-analysis (NMA) is a common approach to summarizing relative treatment effects from randomized trials with different treatment comparisons. Most NMAs are based on published aggregate data (AD) and have limited possibilities for investigating the extent of network consistency and between-study heterogeneity. Given that individual participant data (IPD) are considered the gold standard in evidence synthesis, we explored statistical methods for IPD-NMA and investigated their potential advantages and limitations, compared with AD-NMA. We discuss several one-stage random-effects NMA models that account for within-trial imbalances, treatment effect modifiers, missing response data and longitudinal responses. We illustrate all models in a case study of 18 antidepressant trials with a continuous endpoint (the Hamilton Depression Score). All trials suffered from drop-out; missingness of longitudinal responses ranged from 21 to 41% after 6 weeks follow-up. Our results indicate that NMA based on IPD may lead to increased precision of estimated treatment effects. Furthermore, it can help to improve network consistency and explain between-study heterogeneity by adjusting for participant-level effect modifiers and adopting more advanced models for dealing with missing response data. We conclude that implementation of IPD-NMA should be considered when trials are affected by substantial drop-out rate, and when treatment effects are potentially influenced by participant-level covariates.

Practical implications of using real-world evidence (RWE) in comparative effectiveness research: learnings from IMI-GetReal.

In light of increasing attention towards the use of real-world evidence (RWE) in decision making in recent years, this commentary aims to reflect on the experiences gained in accessing and using RWE for comparative effectiveness research as a part of the Innovative Medicines Initiative GetReal Consortium and discuss their implications for RWE use in decision-making.