Title Participants Abstract "Assumption-lean cox regression" "Stijn Vansteelandt, Oliver Dukes, Kelly Van Lancker, Torben Martinussen" "Inference for the conditional association between an exposure and a time-to-event endpoint, given covariates, is routinely based on partial likelihood estimators for hazard ratios indexing Cox proportional hazards models. This approach is flexible and makes testing straightforward, but is nonetheless not entirely satisfactory. First, there is no good understanding of what it infers when the model is misspecified. Second, it is common to employ variable selection procedures when deciding which model to use. However, the bias and uncertainty that imperfect variable selection adds to the analysis is rarely acknowledged, rendering standard inferences biased and overly optimistic. To remedy this, we propose a nonparametric estimand which reduces to the main exposure effect parameter in a (partially linear) Cox model when that model is correct, but continues to capture the (conditional) association of interest in a well understood way, even when this model is misspecified in an arbitrary manner. We achieve an assumption-lean inference for this estimand based on its influence function under the nonparametric model. This has the further advantage that it makes the proposed approach amenable to the use of data-adaptive procedures (e.g., variable selection, machine learning), which we find to work well in simulation studies and a data analysis. for this article are available online." "Dealing with time‐dependent exposures and confounding when defining and estimating attributable fractions : revisiting estimands and estimators" "Johan Steen, Pawel Morzywolek, Wim Van Biesen, Johan Decruyenaere, Stijn Vansteelandt" "The population‐attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time‐dependent bias in the face of a time‐dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time‐dependent bias, and moreover succeed to adjust for time‐dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time‐dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting‐based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real‐life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital‐acquired infections." "Instrumental variable estimation of the causal hazard ratio" "Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, Stijn Vansteelandt" "Cox's proportional hazards model is one of the most popular statistical models to evaluate associations of exposure with a censored failure time outcome. When confounding factors are not fully observed, the exposure hazard ratio estimated using a Cox model is subject to unmeasured confounding bias. To address this, we propose a novel approach for the identification and estimation of the causal hazard ratio in the presence of unmeasured confounding factors. Our approach is based on a binary instrumental variable, and an additional no-interaction assumption in a first-stage regression of the treatment on the IV and unmeasured confounders. We propose, to the best of our knowledge, the first consistent estimator of the (population) causal hazard ratio within an instrumental variable framework. A version of our estimator admits a closed-form representation. We derive the asymptotic distribution of our estimator and provide a consistent estimator for its asymptotic variance. Our approach is illustrated via simulation studies and a data application." "Rejoinder to discussions on 'Instrumental variable estimation of the causal hazard ratio'" "Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, Stijn Vansteelandt" "Causal inference in survival analysis using longitudinal observational data : sequential trials and marginal structural models" "Ruth H. Keogh, Jon Michael Gran, Shaun R. Seaman, Gwyneth Davies, Stijn Vansteelandt" "Coaching doctors to improve ethical decision-making in adult hospitalised patients potentially receiving excessive treatment : study protocol for a stepped wedge cluster randomised controlled trial" "Dominique Benoit, Stijn Vanheule, Frank Manesse, Frederik Anseel, Geert De Soete, Katrijn Goethals, An Lievrouw, Stijn Vansteelandt, Erik De Haan, Ruth Piers, [missing] CODE Study Grp" "BackgroundFast medical progress poses a significant challenge to doctors, who are asked to find the right balance between life-prolonging and palliative care. Literature indicates room for enhancing openness to discuss ethical sensitive issues within and between teams, and improving decision-making for benefit of the patient at end-of-life. MethodsStepped wedge cluster randomized trial design, run across 10 different departments of the Ghent University Hospital between January 2022 and January 2023. Dutch speaking adult patients and one of their relatives will be included for data collection. All 10 departments were randomly assigned to start a 4-month coaching period. Junior and senior doctors will be coached through observation and debrief by a first coach of the interdisciplinary meetings and individual coaching by the second coach to enhance self-reflection and empowering leadership and managing group dynamics with regard to ethical decision-making. Nurses, junior doctors and senior doctors anonymously report perceptions of excessive treatment via the electronic patient file. Once a patient is identified by two or more different clinicians, an email is sent to the second coach and the doctor in charge of the patient. All nurses, junior and senior doctors will be invited to fill out the ethical decision making climate questionnaire at the start and end of the 12-months study period. Primary endpoints are (1) incidence of written do-not-intubate and resuscitate orders in patients potentially receiving excessive treatment and (2) quality of ethical decision-making climate. Secondary endpoints are patient and family well-being and reports on quality of care and communication; and clinician well-being. Tertiairy endpoints are quantitative and qualitative data of doctor leadership quality. DiscussionThis is the first randomized control trial exploring the effects of coaching doctors in self-reflection and empowering leadership, and in the management of team dynamics, with regard to ethical decision-making about patients potentially receiving excessive treatment." "The role of loneliness on hearing ability and dementia : a novel mediation approach" "Ruth A. Hackett, Tat Thang Vo, Stijn Vansteelandt, Hilary Davies-Kershaw" "Background: To determine the potential mediating role of loneliness in the relationship between hearing ability and dementia. Methods: Design: Longitudinal observational study. Setting: English Longitudinal Study of Ageing (ELSA). Participants: Individuals aged 50 and older ( N = 4232). Measurements: Self-reported hearing ability and loneliness were assessed from Wave 2 (2004-2005) to Wave 7 (2014-2015) of ELSA. Dementia cases were ascertained via self- or carer-report or dementia medication at these waves. The medeff command in Stata version 17 was used to do cross-section mediation analysis between hearing ability, loneliness, and dementia (Waves 3-7). Path-specific effects proportional (cause- specific) hazard models were then used to investigate longitudinal mediation (Waves 2-7). Results: In cross-sectional analyses in Wave 7 alone, loneliness only mediated 5.4% of the total effects of limited hearing on dementia (indirect effects = increased risk of 0.06%; 95% CI: 0.002%-0.15%) under limited hearing and 0.04% (95% CI: 0.001%-0.11%) under normal hearing). In longitudinal analyses, there was no statistical evidence of a mediating role for loneliness in explaining the relationship between hearing ability and time-to-dementia (indirect effect estimate hazard ratio = 1.01 (95% CI: 0.99-1.05). Conclusion: In this community-dwelling sample of English adults, there is a lack of evidence that loneliness mediates the relationship between hearing ability and dementia in both cross-sectional and longitudinal analyses. However, as the number of dementia cases in this cohort was low, replication in other cohorts with larger sample sizes is required to confirm the absence of a mediated effect via loneliness." "Synthetic data : can we trust statistical estimators?" "Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Stijn Vansteelandt, Thomas Demeester" "A small sample correction for factor score regression" "Jasper Bogaert, Wen Wei Loh, Yves Rosseel" "Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores." "Semiparametric estimation of probabilistic index models : efficiency and bias" "Karel Vermeulen, Jan De Neve, Gustavo Amorim, Olivier Thas, Stijn Vansteelandt" "Many well-known rank tests can be viewed as score tests under probabilistic index models (PIMs), that is, regression models for the conditional probability that the outcome of one randomly chosen subject exceeds the outcome of another independently chosen subject. PIMs provide a natural regression framework for nonparametric rank tests. In addition, PIMs supplement these tests with effect sizes and ease the development of more flexible tests, such as tests that allow for covariate adjustment. Inferences for PIMs are currently based on an estimator, referred to as the standard estimator, that is derived heuristically. By appealing to semiparametric theory and a Hoeffding decomposition, we rigorously derive the class of all consistent and asymptotically normal estimators for the parameters indexing a PIM. We identify the (locally) semiparametric efficient estimator in this class, and derive a second estimator with a smaller second-order finite-sample bias. The properties of the estimators are evaluated theoretically and empirically. The heuristic standard estimator turns out to be the preferred estimator in practice, because it is computationally superior to both the efficient and the bias-reduced estimators, and only suffers from a minor loss in efficiency. We also propose a partition strategy to further improve the computational performance of the standard estimator."