Didelez, Vanessa

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Didelez, Vanessa
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Didelez, Vanessa
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    Methodological aspects of emulating target trials for causal evaluation of cancer screening programs using health claims data
    (2024-09-05)
    Braitmaier, Malte
    ;
    Swanson, Sonja
    ;
    Didelez, Vanessa
    ;
    Didelez, Vanessa
    Cancer screening affects cancer-related outcomes by detection at an early – possibly even pre-cancerous – stage, thereby enabling timely treatment initiation or removal of precursors. Ideally, efficacy of cancer screening programs should be assessed in randomized controlled trials before population-wide implementation. In the absence of trial evidence, or when interest lies in real-world effectiveness, observational data must be used to assess the effectiveness of existing programs. However, limitations of observational study designs and data sources must be addressed. Issues relating to unclear research questions or incorrect temporal alignment of study design elements have been identified as a common source of major bias in non-interventional research in recent years. Target trial emulation has been proposed as a framework to formulate clear and precise estimands by defining the study protocol of a hypothetical target trial that would answer the research question at hand and emulating said target trial as best as possible using observational data. As part of this thesis, I developed a detailed study design for the evaluation of the German mammography screening program regarding its effect on breast cancer-related mortality. Furthermore, I conducted an extensive, realistic simulation study to assess the potential of residual immortal time bias due to the coarse granularity of discrete time available in the underlying database. Next, I emulated a target trial to assess the causal effect of screening colonoscopy on the incidence of colorectal cancer. Differing effectiveness by site of the tumor was reported in previous observational studies on screening colonoscopy. I showed in the present thesis that previous observational studies overestimated the effect of screening colonoscopy and that the difference by site was largely a result of design-induced bias. I extended the initial study design to more complex settings with a sustained no screening strategy and to strategies incorporating the quality of colonoscopy. Finally, I conducted substantive sensitivity analyses tailored to the specific study design and research question, e.g. concerning residual confounding bias, strengthening confidence in the validity of findings.
    Dissertation
      280  383
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    Constraint-based causal discovery with tiered background knowledge
    (2025-03-11)
    Bang, Christine
    ;
    Magliacane, Sara
    ;
    Didelez, Vanessa
    This thesis explores how information about temporal structures can improve causal discovery methods. I consider extensions of existing constraint-based causal discovery algorithms: The PC, FCI, and IOD. These algorithms estimate graphs based on (conditional) independence testing, and are originally purely data-driven. However, often we have more information available than only the dependence structure. This I refer to as background knowledge, and this can be used to extend and improve the existing algorithms. In this thesis, I focus on information given by a temporal order, e.g. in which order variables are measured. This entails a special kind of background knowledge, which I refer to as temporal, or tiered, background knowledge. The fact that the use of tiered background knowledge improves causal discovery methods appears evident. The novel contribution of this thesis is a thorough investigation of the ways in which the algorithms are improved. This includes formal results and examples, as well as empirical results using both simulated and real data. The improvements due to tiered background knowledge fall into one of two categories: Informativeness and accuracy. Constraint-based causal discovery suffer from an under-identifiability of the causal structure, since multiple graphs may encode the same dependence structure. An improvement in informativeness means an increased certainty of the causal connections. In practice, the output graphs are often incorrect due to errors arising from independence testing using finite sample data. An improvement in accuracy means that the estimated graphs are less prone to errors. This thesis presents a formalisation of (tiered) background knowledge, and results on how it improves constraint-based causal discovery in different aspects under different assumptions: First, I assume that all relevant variables are observed, and that we are given the correct (conditional) independencies among the observed variables. I give a criterion for when an increase in informativeness is obtained by adding tiered background knowledge. This criterion suggests that adding background knowledge of early tiers yields the largest increase in informativeness, with the overall largest increase for sparse graphs. This is supported by the results of a simulation study. Moreover, I show that the graphical output has some desirable interpretational and computational properties. Second, I relax the assumption of knowing the correct (conditional) independencies among the observed variables. However, I still assume that all relevant variables are observed. I consider the properties of the existing tiered PC (tPC) algorithm. This is an extension of the original PC, which skips some conditional independence tests and orients some (additional) edges, both based on tiered background knowledge. I show how this improves the accuracy. Third, I again assume knowledge of (conditional) independencies among the observed variables. However, I allow for unobserved variables, and I combine multiple overlapping datasets. I describe the existing tiered FCI (tFCI) and introduce the novel tiered IOD (tIOD), which both extend the existing algorithms with tiered background knowledge, similar to the tPC. The output of the IOD algorithm consists of multiple graphs, which implies an additional level of under-identifiability. I show that tiered background knowledge can decrease this number of graphs. Lastly, I discuss how the results presented here relate to similar work, as well as some open problems and possible extensions. All algorithms are provided as pseudo-algorithms, and are accompanied by proofs of soundness, and often also completeness.
    Dissertation
      79  222
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    Causal model selection in epidemiology
    (2021-12-07)
    Lüschen, Janine
    ;
    Pigeot, Iris
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    Didelez, Vanessa
    ;
    Didelez, Vanessa
    In this thesis, I investigate two related types of causal model selection: confounder selection and constraint-based causal discovery. The aim of confounder selection is to determine a valid adjustment set when the interest lies in the causal effect of an exposure or treatment X on an outcome Y. Ideally, this is based on a causal graph representing relevant domain knowledge. Alternative strategies range from simple knowledge-based rules to complex data-driven algorithms. In this thesis, I investigate popular strategies from a graphical perspective. Main results are that structural assumptions cannot be avoided even if no causal graph is drawn, and that `outcome-oriented' strategies often lead to more precise estimates than other methods. The efficiency aspect in then further investigated for the case that the underlying causal graph is known or has been estimated, and the variables jointly follow a multivariate Gaussian distribution. I show that the `optimal' adjustment set yielding the smallest asymptotic variance can be read off using graphical rules and does not depend on the parameters of the distribution. It has an intuitive interpretation in terms of a graphical projection I propose and can be viewed as the target set of backward regression selection. Instead of focussing on a single treatment-outcome pair and its confounding factors, the aim of causal discovery is to infer the causal structure among several variables simultaneously. I propose a modified version of the PC-algorithm for causal discovery that takes temporal background knowledge into account, and show that the new algorithm is sound and complete and has certain stability properties. Further, I formally investigate two recently suggested methods for handling missing values in causal discovery: test-wise deletion and multiple imputation. Finally, I discuss chances and challenges of causal discovery and causal graphical modelling in general.
    Dissertation
      605  812