Most epidemiologic cohorts are composed of volunteers who do not represent the general population. To enable population inference from cohorts, we and others have proposed utilizing probability survey samples as external references to develop a propensity score (PS) for membership in the cohort versus survey. Herein we develop a unified framework for PS-based weighting (such as inverse PS weighting (IPSW)) and matching methods (such as kernel-weighting (KW) method). We identify a fundamental Strong Exchangeability Assumption (SEA) underlying existing PS-based matching methods whose failure invalidates inference even if the PS-model is correctly specified. We relax the SEA to a Weak Exchangeability Assumption (WEA) for the matching method. Also, we propose IPSW.S and KW.S methods that reduce the variance of PS-based estimators by scaling the survey weights used in the PS estimation. We prove consistency of the IPSW.S and KW.S estimators of population means and prevalences under WEA, and provide asymptotic variances and consistent variance estimators. In simulations, the KW.S and IPSW.S estimators had smallest MSE. In our data example, the original KW estimates had large bias, whereas the KW.S estimates had the smallest MSE.
Many epidemiologic studies forgo probability sampling and turn to nonprobability volunteer-based samples because of cost, response burden, and invasiveness of biological samples. However, finite population inference is difficult to make from the nonprobability samples due to the lack of population representativeness. Aiming for making inferences at the population level using nonprobability samples, various inverse propensity score weighting (IPSW) methods have been studied with the propensity defined by the participation rate of population units in the nonprobability sample. In this paper, we …
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given baseline covariates) targets a nonparametrically nonidentifiable parameter. Consistent estimation of propensity score functions is an important component of various causal inference estimators, including double robust machine learning and inverse probability weighted estimators. This …
Causal inference via propensity scores for case-control studies
Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；Centre de recherche de St. Mary（圣玛丽研究中心）；Research Institute of the McGill University Health Centre（麦吉尔大学健康中心研究所）；SUNY Downstate Health Sciences University（纽约州立大学_downstate_健康科学大学）；Institut national de la recherche scientifique, Centre Armand-Frappier Santé Biotechnologie（国家科研研究所，阿尔芒-弗拉皮耶生物技术与健康中心）；Centre de recherche du CHU de Québec – Université Laval（魁北克大学医院研究中心）
Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioral deficits. However, there is virtually no information in the scientific literature regarding the levels of PAE associated with an increased risk of clinically significant adverse effects. During the period from 1975-1993, several prospective longitudinal cohort studies were conducted in the U.S., in which maternal reports regarding alcohol use were obtained during pregnancy and the cognitive development of the offspring was assessed from early…
The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one …
Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimati…
The pervasive use of prevalent cohort studies on disease duration, increasingly calls for appropriate methodologies to account for the biases that invariably accompany samples formed by such data. It is well-known, for example, that subjects with shorter lifetime are less likely to be present in such studies. Moreover, certain covariate values could be preferentially selected into the sample, being linked to the long-term survivors. The existing methodology for estimation of the propensity score using data collected on prevalent cases requires the correct conditional survival/hazard function g…
Recent changes in housing costs relative to income are likely to affect people's propensity to Housing Affordability Stress (HAS), which is known to have a detrimental effect on a range of health outcomes. The magnitude of these effects may vary between subgroups of the population, in particular across age groups. Estimating these effect sizes from longitudinal data requires Small Domain Estimation (SDE) as available data is generally limited to small sample sizes. In this paper we develop the rationale for smoothing-based SDE using two case studies: (1) transitions into and out of HAS and (2)…
The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an elimination of exposure. Originally, the PAF has been defined for cohort studies of fixed length with a baseline exposure or cross-sectional studies. An extension of the definition to complex time-to-event data is not straightforward. We revise the proposed approaches in literature and provide a clear concept of the PAF for these data situations. The conceptualization is achieved by…
Propensity score methods were proposed by Rosenbaum and Rubin [Biometrika 70 (1983) 41--55] as central tools to help assess the causal effects of interventions. Since their introduction more than two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology and education, especially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require extensive delays before answers could be obtained. In the past few years, the number of published applications using propensit…
Small area ecological studies are commonly used in epidemiology to assess the impact of area level risk factors on health outcomes when data are only available in an aggregated form. However the resulting estimates are often biased due to unmeasured confounders, which typically are not available from the standard administrative registries used for these studies. Extra information on confounders can be provided through external datasets such as surveys or cohorts, where the data are available at the individual level rather than at the area level; however such data typically lack the geographica…
In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference, prediction, or decision problem. In such circumstances, it is convenient to use a graphical model to represent the statistical dependencies, via a set of connected "modules", each relating to a specific data modality, and drawing on specific domain expertise in their development. In principle, given data, the conventional statistical update then allows for coherent uncertainty quantification and information propagation through and across the modules. However, misspecificati…
Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Homelessness, Behavioral Health Problems, and Recidivism
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
Department of Computer Science, Faculty of Electrical Engineering and Computer science, VSB-Technical University of Ostrava（计算机科学系，电气工程与计算机科学学院，VSB技术大学奥斯特拉瓦）；VSB-Technical University of Ostrava（VSB技术大学奥斯特拉瓦）；IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava（IT4Innovations国家超级计算中心，VSB技术大学奥斯特拉瓦）；Department of Applied Mathematics, Faculty of Electrical Engineering and Computer science, VSB-Technical University of Ostrava（应用数学系，电气工程与计算机科学学院，VSB技术大学奥斯特拉瓦）；Department of Surgical Studies, Faculty of Medicine, University of Ostrava（外科研究系，医学院，奥斯特拉瓦大学）；Department of Surgery, University Hospital Ostrava（外科系，奥斯特拉瓦大学医院）
Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score-based methods. An additional source of bias is right-censoring, which occurs when the information on the outcome of interest is not complet…
Inverse Probability Weighting of Count Exposures in the Presence of Missing Data: A Simulation Study
Centre for Longitudinal Studies, UCL Social Research Institute, University College London（长期研究发展中心，UCL社会研究所，伦敦大学学院）；Research Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London（行为科学与健康研究系，流行病学与健康护理研究所，伦敦大学学院）
In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassifica…
Centre for Big Data Research in Health (CBDRH)（健康大数据研究中心）
Causal inference for observational longitudinal studies often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-dependent patient history and time-dependent covariates. To tackle this longitudinal treatment effect estimation problem, we have developed a time-variant causal survival (TCS) model that uses the potential outcomes framework with an ensemble of recurrent subnetworks to estimate the difference in survival probabilities and its confidence interval over time as a function of time-dependent covariates and treatments. Using simulated …
A Plasmode Benchmark Can Refute but Cannot Certify: Projection and Regularity in Causal-Inference Simulation
School of Population and Public Health, University of British Columbia（不列颠哥伦比亚大学公共卫生与人口健康学院）；Centre for Advancing Health Outcomes, St. Paul’s Hospital（圣保罗医院健康成果促进中心）
