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Studies of memory trajectories using longitudinal data often result in highly non-representative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained…

Methodology · Statistics 2021-03-23 Maria Josefsson , Michael J. Daniels , Sara Pudas

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by {dropout} and positivity violations. We tackle these problems by generalizing effects of…

Methodology · Statistics 2022-03-16 Kwangho Kim , Edward H. Kennedy , Ashley I. Naimi

Longitudinal data often contains outcomes measured at multiple visits and scientific interest may lie in quantifying the effect of an intervention on an outcome's rate of change. For example, one may wish to study the progression (or…

Methodology · Statistics 2025-10-07 Anja Shahu , Weijie Xia , Ying Wei , Daniel Malinsky

Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can lead to non-identification, inefficiency, and effects with…

Methodology · Statistics 2018-06-20 Edward H. Kennedy

Dementia currently affects about 50 million people worldwide, and this number is rising. Since there is still no cure, the primary focus remains on preventing modifiable risk factors such as cardiovascular factors. It is now recognized that…

Methodology · Statistics 2024-08-14 Léonie Courcoul , Catherine Helmer , Antoine Barbieri , Hélène Jacqmin-Gadda

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we…

Signal Processing · Electrical Eng. & Systems 2018-11-16 Armin Soltan Zadi , Raichel Alex , Rong Zhang , Donald E. Watenpaugh , Khosrow Behbehani

Measuring the effect of patient safety improvement efforts is needed to determine their value but is difficult due to the inherent complexities of hospital operations. In this paper, we show by case study how interrupted time series design…

Applications · Statistics 2018-06-28 Diego A. Martinez , Mehdi Jalalpour , David T. Efron , Scott R. Levin

Alcohol misuse is a key target of public health strategies aimed at reducing cardiovascular risk. The effect of excessive alcohol consumption on blood pressure may vary systematically with individuals' unobserved propensity to engage in…

Methodology · Statistics 2026-03-11 Ashish Patel , Francis J DiTraglia , Stephen Burgess

Mean aortic pressure (MAP) is a major determinant of perfusion in all organs systems. The ability to forecast MAP would enhance the ability of physicians to estimate prognosis of the patient and assist in early detection of hemodynamic…

Machine Learning · Computer Science 2020-10-20 Eliza Huang , Rui Wang , Uma Chandrasekaran , Rose Yu

Standard cardiovascular risk calculators, including the Framingham Risk Score and the ACC/AHA Pooled Cohort Equations, estimate the conditional probability P(CHD | SysBP = s) rather than the interventional quantity P(CHD | do(SysBP = s)).…

Applications · Statistics 2026-05-08 Suchibrata Patra

Recurrent event data are common in clinical studies when participants are followed longitudinally, and are often subject to a terminal event. With the increasing popularity of large pragmatic trials with a heterogeneous source population,…

Methodology · Statistics 2022-12-06 Xinyuan Tian , Maria Ciarleglio , Jiachen Cai , Erich Greene , Denise Esserman , Fan Li , Yize Zhao

It is often of interest in observational studies to measure the causal effect of a treatment on time-to-event outcomes. In a medical setting, observational studies commonly involve patients who initiate medication therapy and others who do…

Methodology · Statistics 2019-10-07 Reagan Mozer , Mark E. Glickman

Background and Objectives: In a significant portion of surgeries, blood pressure (BP) is often measured non-invasively in an intermittent manner. This practice has a risk of missing clinically relevant BP changes between two adjacent…

Longitudinal causal inference is concerned with defining, identifying, and estimating the effect of a time-varying intervention on a time-varying outcome that is indexed by a follow-up time. In an observational study, Robins's generalized…

Methodology · Statistics 2025-10-01 Herbert P. Susmann , Nicholas T. Williams , Richard Liu , Jessica G. Young , Iván Díaz

Causal mediation analysis of observational data is an important tool for investigating the potential causal effects of medications on disease-related risk factors, and on time-to-death (or disease progression) through these risk factors.…

Hypertension is a highly prevalent condition and a major risk factor for cardiovascular disease. The landmark Systolic Blood Pressure Intervention Trial (SPRINT) showed that lowering systolic blood pressure (BP) goals from 140 mmHg to 120…

Applications · Statistics 2025-12-16 Yi Zhao , Chengyun Li , Wanzhu Tu

Randomized clinical trials are considered the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, such trials are often impractical because of ethical and…

Methodology · Statistics 2020-01-20 Anthony D. Scotina , Andrew R. Zullo , Robert J. Smith , Roee Gutman

The association between visit-to-visit systolic blood pressure variability and cardiovascular events has recently received a lot of attention in the cardiovascular literature. But blood pressure variability is usually estimated on a…

Applications · Statistics 2019-01-25 Jessica K. Barrett , Raphael Huille , Richard Parker , Yuichiro Yano , Michael Griswold

Background: A core aspect of epidemiology is determining the impacts of potential public health interventions over time. With long follow-up periods, epidemiologists may need to consider semi-competing events, in which a terminal event,…

We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by…

Machine Learning · Computer Science 2016-12-13 Yanbo Xu , Yanxun Xu , Suchi Saria
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