Unmeasured confounding is a well-known obstacle in causal inference. In recent years, negative controls have received increasing attention as a important tool to address concerns about the problem.... Show moreUnmeasured confounding is a well-known obstacle in causal inference. In recent years, negative controls have received increasing attention as a important tool to address concerns about the problem. The literature on the topic has expanded rapidly and several authors have advocated the more routine use of negative controls in epidemiological practice. In this article, we review concepts and methodologies based on negative controls for detection and correction of unmeasured confounding bias. We argue that negative controls may lack both specificity and sensitivity to detect unmeasured confounding and that proving the null hypothesis of a null negative control association is impossible. We focus our discussion on the control outcome calibration approach, the difference-in-difference approach, and the double-negative control approach as methods for confounding correction. For each of these methods, we highlight their assumptions and illustrate the potential impact of violations thereof. Given the potentially large impact of assumption violations, it may sometimes be desirable to replace strong conditions for exact identification with weaker, easily verifiable conditions, even when these imply at most partial identification of unmeasured confounding. Future research in this area may broaden the applicability of negative controls and in turn make them better suited for routine use in epidemiological practice. At present, however, the applicability of negative controls should be carefully judged on a case-by-case basis. Show less
The persistent scarcity of donor liver grafts necessitates prioritization of patients based on expected future survival without transplantation. The goal of this thesis was to improve survival... Show moreThe persistent scarcity of donor liver grafts necessitates prioritization of patients based on expected future survival without transplantation. The goal of this thesis was to improve survival prediction models for patients on the LT waiting list. Through advancements in prediction models, liver grafts can be allocated in the best way possible. Show less
This thesis describes studies on methods for answering questions about causality, specifically so-called what-if questions, in the presence of methodological obstacles such as confounding, missing... Show moreThis thesis describes studies on methods for answering questions about causality, specifically so-called what-if questions, in the presence of methodological obstacles such as confounding, missing data, and measurement error. Show less
Background: Case-control designs are an important yet commonly misunderstood tool in the epidemiologist's arsenal for causal inference. We reconsider classical concepts, assumptions and principles... Show moreBackground: Case-control designs are an important yet commonly misunderstood tool in the epidemiologist's arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results: We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion: The modern epidemiologist's arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities. Show less
This thesis investigates the effectiveness and safety of treatments in patients with cardiovascular and kidney disease. Routinely collected healthcare data provide an immense opportunity to... Show moreThis thesis investigates the effectiveness and safety of treatments in patients with cardiovascular and kidney disease. Routinely collected healthcare data provide an immense opportunity to investigate such questions in populations underrepresented in clinical trials, such as patients with advanced chronic kidney disease (CKD).The first part of this thesis deals with how to appropriately use routinely collected data to answer causal questions. It illustrates what study designs eliminate commonly occurring biases, namely immortal time and prevalent user bias, and how to use propensity scores to correctly adjust for confounding in the setting of time-fixed and time-varying treatments.The second part investigates the effectiveness and safety of various treatments. For instance, the effectiveness of beta-blockers in patients with heart failure and advanced CKD is investigated. Renin-angiotensin system inhibitors (RASi) are an especially widely used medication class in CKD patients. The relationship between the magnitude of renal function decline - which is commonly observed after initiation of these drugs - with mortality and cardiorenal outcomes is investigated. In addition, comparative effectiveness study of RASi and calcium channel blockers among patients with advanced CKD is performed. In the last two chapters, a target trial is explicitly emulated to investigate the effect of stopping or continuing RASi and the optimal timing to start dialysis in patients with advanced chronic kidney disease. Show less
Vries, B.B.L.P. de; Smeden, M. van; Groenwold, R.H.H. 2021
Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum... Show moreJoint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425-436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor). Show less
Background DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes... Show moreBackground DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. Results By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g.,NFKBIE,CDCA7(L), andNLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. Conclusion We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation. Show less
This thesis investigates the validity and usefulness of physician's preference-based instrumental variable analysis in clinical epidemiological studies. Chapter 2 describes a survey amongst general... Show moreThis thesis investigates the validity and usefulness of physician's preference-based instrumental variable analysis in clinical epidemiological studies. Chapter 2 describes a survey amongst general practitioners, showing substantial variation in prescribing preference and showing prescribing patterns which suggest the stochastic monotonicity assumption may be plausible for physician's preference as an instrumental variable. Chapter 3 describes an application of physician's preference-based instrumental variable analysis in a moderate-sized study, showing uninformatively wide confidence intervals which limit the usefulness of instrumental variable analysis in this setting. Chapter 4 focuses on the bias-variance trade-off of instrumental variable analysis in comparison to conventional analyses, using simulations and theoretical derivations. Chapter 5 compares instrumental variable and conventional estimates of the effect of third versus second generation oral contraceptives on occurrence of venous thromboembolism. The similarity of these estimates under different sets of assumptions suggests major confounding is unlikely. Chapter 6 contains a suggestion for an additional step for reporting of instrumental variable analyses. The focus shifts to Mendelian randomisation in the second part of the thesis. Chapter 7 reviews the methodological approaches used in Mendelian randomisation studies and the quality of reporting. Chapter 8 shows that collider-stratification bias may exist in Mendelian randomisation studies in elderly populations. Show less
