Emulation of Target Trials to Study the Effectiveness and Safety of Medical Interventions

Ideally, clinical guidelines would be informed by well-designed randomized experiments. However, it is generally not possible to conduct a randomized trial for every clinically relevant decision. Decision makers therefore often have to rely on observational data. Guidelines that rely on observational data due to the absence of randomized trials benefit when the analysis mimics the analysis of a hypothetical target trial. This can be achieved by explicitly formulating the protocol of the target trial, and thoroughly discussing the feasibility of the conditions that must be met in order to validly emulate the target trial using observational data. In chapter one, we discuss the emulation of trials that compare the effects of different timing strategies, that is, strategies that vary the frequency of delivery of a medical intervention or procedures, and provide an application to surveillance for colorectal cancer. In chapter two, we discuss a study design that attempts to avoid bias by comparing initiators of the treatment of interest with initiators of an “active comparator” that is believed to be inactive for the outcome, in order to emulate a randomized trial that compares the treatment of interest with an inactive comparator. In chapter three, we describe a new method that combines randomized trial data and external information to emulate a different target trial. We apply this method to a randomized trial of postmenopausal hormone therapy in order to emulate a trial of a joint intervention on hormone therapy and statin therapy

Methods to Estimate the Comparative Effectiveness of Clinical Strategies that Administer the Same Intervention at Different Times

Clinical guidelines that rely on observational data due to the absence of data from randomized trials benefit when the observational data or its analysis emulates trial data or its analysis. In this paper, we review a methodology for emulating trials that compare the effects of different timing strategies, that is, strategies that vary the frequency of delivery of a medical intervention or procedure. We review trial emulation for comparing (i) single applications of the procedure at different times, (ii) fixed schedules of application, and (iii) schedules adapted to the evolving clinical characteristics of the patients. For illustration, we describe an application in which we estimate the effect of surveillance colonoscopies in patients who had an adenoma detected during the Norwegian Colorectal Cancer Prevention (NORCCAP) trial

Searching for signals of Dark Matter produced with top quark pairs using the ATLAS detector

© 2019 Anders Vilhelm HuitfeldtUnderstanding the nature of Dark Matter is a key goal in modern physics. The observed gravita-tional interactions of galaxies and galactic clusters, along with theories of structure formation in the early universe, indicate the existence of Dark Matter. Evidence of the specific nature of Dark Matter remains elusive however. Particle collider experiments search for evidence of Dark Matter production within energetic proton collisions. One strategy employed in this field is to make minimal assumptions about new particles and couplings to Standard Model particles, in order to explore the range of possibilities without being overly constrained by narrow assumptions. This thesis focuses on the assumption that Dark Matter couples strongly to the heavier quarks, which motivates searching for processes where it is produced in association with pairs of top quarks. An analysis is presented on the 2015 and 2016 “Run 2" dataset taken with the ATLAS detector, consisting of 36.1 fb -1 of proton-proton collisions at the Large Hadron Collider. This analysis studies the hypothesis of Dark Matter production in conjunction with hadronically decaying top quarks. No excess above the estimated Standard Model backgrounds is observed, and constraints on the allowed cross-sections are presented. When making minimal assumptions about the nature of Dark Matter, scalar mediator masses below 20 GeV are excluded. These results are then translated to more specific and complete Two Higgs Doublet models that feature for example in Supersymmetry that also predict the same final states, and constraints on the parameter space of these models are presented

Re: Generalizing Study Results: A Potential Outcomes Perspective

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On the collapsibility of measures of effect in the counterfactual causal framework

The relationship between collapsibility and confounding has been subject to an extensive and ongoing discussion in the methodological literature. We discuss two subtly different definitions of collapsibility, and show that by considering causal effect measures based on counterfactual variables (rather than measures of association based on observed variables) it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure, from the components that are due to structural bias such as confounding. We provide new weights such that the causal risk ratio is collapsible over arbitrary baseline covariates. In the absence of confounding, these weights may be used for standardization of the risk ratio

Shall we count the living or the dead?

In the 1958 paper "Shall we count the living or the dead", Mindel C. Sheps proposed a principled solution to the familiar problem of asymmetry of the relative risk. We provide causal models to clarify the scope and limitations of Sheps' line of reasoning, and show that her preferred variant of the relative risk will be stable between patient groups under certain biologically interpretable conditions. Such stability is useful when findings from an intervention study must be generalized to support clinical decisions in patients whose risk profile differs from the participants in the study. We show that Sheps' approach is consistent with a substantial body of psychological and philosophical research on how human reasoners carry causal information from one context to another, and that it can be implemented in practice using van der Laan et al's Switch Relative Risk, or equivalently, using Baker and Jackson's Generalized Relative Risk Reduction (GRRR).Comment: Major updat