Topics in Evidence Synthesis

This dissertation considers three different topics related to extracting and merging evidence from heterogeneous sources. This problem is addressed from different angles, from the field of design of experiment to machine learning./nWithin this dissertation, we add to the existing literature in each area by developing novel methodology and software./nAdaptive trial designs can considerably improve upon traditional designs,/nby modifying design aspects of the ongoing trial, like early stopping,/nadding or dropping doses, or changing the sample size./nWe propose a two-stage Bayesian adaptive design for a Phase IIb study aimed at selecting the lowest effective dose for Phase III. In this setting, efficacy has been proved for a high dose in a Phase IIa proof-of-concept study, but the existence of a/nlower but still effective dose is investigated before the scheduled Phase III starts./nIn the first stage patients are randomized to placebo, maximal/ntolerated dose, and one or more additional doses within the dose/nrange. Based on an interim analysis, the study is either stopped for/nfutility or success, or enters the second stage, where newly recruited/npatients are allocated to placebo, some fairly high dose, and one/nadditional dose chosen based on interim data. At the interim analysis/ncriteria based on the predictive probability of success are used to/ndecide on whether to stop or to continue the trial, and, in the latter/ncase, which dose to select for the second stage./nFinally, a dose will be selected as lowest effective dose for Phase III/neither at the end of the first or at the end of the second stage./nThe operating characteristics of the procedure are evaluated via/nsimulations and results are presented for several scenarios comparing/nthe performance of the proposed procedure to those of the non adaptive/ndesign./nThe development of novel therapies in multiple sclerosis (MS) is one area where a range of surrogate/noutcomes are used in various stages of clinical research. While the aim of treatments in MS is to prevent/ndisability, a clinical trial for evaluating a drugs effect on disability progression would require a large/nsample of patients with many years of follow-up. The early stage of MS is characterized by relapses. To/nreduce study size and duration, clinical relapses are accepted as primary endpoints in phase III trials. For/nphase II studies, the primary outcomes are typically lesion counts based on Magnetic Resonance Imaging/n(MRI), as these are considerably more sensitive than clinical measures for detecting MS activity./nRecently, Sormani and colleagues \cite{sormani2010surrogate} provided a systematic review, and/nused weighted regression analyses to examine the role of either MRI lesions or relapses as trial level/nsurrogate outcomes for disability. We build on this work by developing a Bayesian three-level model,/naccommodating the two surrogates and the disability endpoint, and properly taking into account that/ntreatment effects are estimated with errors. Specifically, a combination of treatment effects based on/nMRI lesion count outcomes and clinical relapse, both expressed on the log risk ratio scale, were used to/ndevelop a study level surrogate outcome model for the corresponding treatment effects based on/ndisability progression. While the primary aim for developing this model was to support decision making/nin drug development, the proposed model may also be considered for future validation./nIn Genomics and Epidemiology we deal with a high number of features for each observation. Many well known approaches to drawing inferences in this kind of settings use the topology of the feature space, induced by an appropriate metric, to group observations and summarize their main characteristics to get rid of the noise and to predict an outcome of interest. In the present work we generalize this approach in the context of Loss-Based Estimation. We propose an alternative method for constructing a nonparametric multidimensional regression function. This approach is based on the simple idea of clustering data points in the feature space and then fitting a constant to the outcome. HOPACH-PAM is used for partition. This approach results in the choice of a small number of distinct regions easy to interpret. This is specifically illustrated by simulations from which we can see immediately the superiority of this method on CART. Pre-screening and feature selections methods are also developed to improve the performances and reduce the noise. Software is also available in the R package HOPSLAM (HOpach-Pam Supervised Learning AlgorithM) to make this methodology easily accessible.

Advisor: Jewell, Nicholas P.
Hubbard, Alan E.