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Background This thesis comprises work done in several research areas, including meta-analysis, network meta-analysis and prediction modelling. Below, I briefly provide some background for each of these areas. Meta-analysis of individual patient data (IPD) from randomized controlled trials (RCTs) can potentially be used to identify whether treatment effects substantially differ across clinically important subgroups and to potentially pinpoint the best treatment for each patient. Statistical methods for IPD meta-analysis have been established. However, RCTs often collect information on a large number of patient-level variables (covariates), some of which might be unrelated to the outcome of interest. Including too many covariates in an IPD meta-analysis model might lead to worse estimates, and might hinder interpretation of results. Currently there is a lack of guidance on how to select covariates to include in an IPD meta-analysis model. In addition, there has been growing interest in using data from non-randomized studies (NRS) to complement evidence from RCTs in medical decision-making. This is because, although RCTs are the best source of evidence regarding relative treatment effects, they often employ strict experimental settings, which may hamper their ability to predict outcomes in ‘realworld’ clinical settings. Currently, there is a gap in methods for combining IPD from RCTs and NRS, when aiming to make patient-specific predictions about the real-world effects of medical interventions. Moreover, clinical prediction models are widely used in modern clinical practice. Such models are often developed using IPD from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies, however, often measure different sets of predictors, which may result to systematically missing predictors, i.e., when not all studies collect all predictors of interest. This situation poses challenges in model development. Finally, network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes are of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. In addition, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. Aims This thesis has four research aims. The first aim was to explore whether a systematic approach to the selection of treatmentcovariate interactions in an IPD meta-analysis can lead to better estimates of patient-specific treatment effects. The second aim was to describe a general framework for developing models that combine individual patient data from RCTs and NRS when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. The third aim was to explore approaches that can be used to develop prediction models for continuous outcomes, when not all studies collect all predictors of interest, i.e. resulting in systematically missing predictors. The fourth aim was to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Methods For the first aim, we compared in simulations the standard approach to IPD meta-analysis (no variable selection, all treatment-covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment-covariate interactions. To illustrate our methods, we used dataset from cardiology comparing new generation drug-eluting and bare-metal stents for percutaneous coronary intervention and from psychiatry comparing antidepressant treatment of major depression. For the second aim, we developed six meta-analytical models and a simpler model for making predictions about patients in real world settings. We focused on Bayesian approaches and utilized methods such as shrinkage, calibration of intercept and main effects of covariates, and weighting approaches to account for different study designs. We used a dataset of patients with rheumatoid arthritis obtained from three RCTs and two registries to illustrate our methods. For the third aim, we compared four approaches: a naïve approach, where the model is developed using only predictors measured in all studies; a multiple imputation approach that ignores patient allocation in studies; a multiple imputation approach that accounts for study allocation; and a new approach that develops a prediction model in each study separately using all predictors reported, and then synthesizes all predictions in a multi-study ensemble. For the fourth aim, we worked on developing a new plot that compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatments, while it illustrates absolute, rather than relative, effects of interventions. Results For the first aim, exploring a range of scenarios, we found in simulations that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. We exemplified all methods in two real examples and saw that using more advanced methods may lead to different estimates of relative treatment effects. For the second aim, we developed several evidence-synthesis models. We found that, for our example, models that pool information from both RCTs and non-randomized studies might provide the best predictions for patients in a new setting. For the third aim, we found that in simulations existing multiple imputation methods and our new method outperform the naïve approach. In several scenarios, our method outperformed imputation methods, especially for few studies, when predictor effects were small, and in case of large heterogeneity. For the fourth aim, we developed the Kilim plot which provide a holistic view of the available evidence expressed in terms of absolute treatment effects and their corresponding strength of statistical evidence. Conclusion From the first project, we conclude that variable selection is essential in meta-analyzing IPD from multiple RCTs, especially when there are many reported covariates. Both frequentist and Bayesian variable selection methods can be used, as long as the information regarding study allocation of patients in studies is included in the model. In the second project, we saw that the gain in predictive performance obtained from models combining RCTs and NRS was modest in our clinical example. Nevertheless, the illustration of different modelling approaches and the considerations regarding different cross-validation methods that we provide may be valuable to inform future studies aiming to predict realworld outcomes of competing interventions. Based on the results of the third project, we recommend researchers faced with systematically missing predictors to select among the different methods after using both internal and internal-external cross-validation approaches. We think that our new ensemble method offers a potentially powerful alternative to researchers, and that it might be especially useful in the common case of having IPD from only a handful of studies, reporting different sets of predictors. For the fourth aim, we conclude that the Kilim plot can be a valuable aid in summarizing and communicating results from NMAs on multiple outcomes. It can be especially useful for larger networks, for the case of many outcomes, and when aiming to communicate NMA results with patients and/or clinicians, so as to facilitate every-day clinical practice. |
