Adjusting for Selection Bias Due to Missing Eligibility Criteria in Emulated Target Trials.

Target trial emulation (TTE) is a popular framework for observational studies based on electronic health records (EHR). A key component of this framework is determining the patient population eligible for inclusion in both a target trial of interest and its observational emulation. Missingness in variables that define eligibility criteria, however, presents a major challenge towards determining the eligible population when emulating a target trial with an observational study.

Importance of variables from different time frames for predicting self-harm using health system data.

Objective: Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is made. Measurements from some time periods may not be available for all patients. Using the framework of algorithm-agnostic variable importance, we study the predictive potential of variables corresponding to different time horizons prior to the index visit and demonstrate the application of variable importance techniques in the biomedical informatics setting.

Importance of variables from different time frames for predicting self-harm using health system data.

Objective: Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is made. Measurements from some time periods may not be available for all patients. Using the framework of algorithm-agnostic variable importance, we study the predictive potential of variables corresponding to different time horizons prior to the index visit and demonstrate the application of variable importance techniques in the biomedical informatics setting.

Differentially Private Outcome-Weighted Learning for Optimal Dynamic Treatment Regime Estimation.

Precision medicine is a framework for developing evidence-based medical recommendations that seeks to determine the optimal sequence of treatments tailored to all of the relevant patient-level characteristics which are observable. Because precision medicine relies on highly sensitive, patient-level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting.

Current use of estrogen-containing oral contraceptives or hormone therapy and risk of COVID-19 infection and hospitalization: a population-based cohort study.

The association between current use of oral contraceptives (OCs) among women younger than 50 years (n = 306 541), and hormone therapy (HT) among women aged 50 years or older (n = 323 203), and coronavirus 2019 (COVID-19) infection and hospitalization was evaluated in this population-based cohort. Current OC/HT use was recorded monthly using prescription dispensing data. COVID-19 infections were identified from March 2020 through February 2021.

Variable selection for individualised treatment rules with discrete outcomes.

An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement.

Preventing severe hypoglycemia in adults with type 2 diabetes (PHT2): Design, delivery and evaluation framework for a randomized controlled trial.

Background: Severe hypoglycemia is a common and feared complication of medications used to lower blood glucose levels in individuals with diabetes. Psychoeducational interventions can prevent severe hypoglycemia in individuals with type 1 diabetes (T1D). We aim to determine the effectiveness of this approach among adults with type 2 diabetes (T2D) at elevated risk for severe hypoglycemia.

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

Objective: The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications.

Methods: EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire-9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14-180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores.

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.

Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment.

Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes.

Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials.

Estimating individualized treatment rules in longitudinal studies with covariate-driven observation times.

The sequential treatment decisions made by physicians to treat chronic diseases are formalized in the statistical literature as dynamic treatment regimes. To date, methods for dynamic treatment regimes have been developed under the assumption that observation times, that is, treatment and outcome monitoring times, are determined by study investigators. That assumption is often not satisfied in electronic health records data in which the outcome, the observation times, and the treatment mechanism are associated with patients' characteristics.

Association Between Diabetes Severity and Risks of COVID-19 Infection and Outcomes.

Background: Little is known about whether diabetes increases the risk of COVID-19 infection and whether measures of diabetes severity are related to COVID-19 outcomes.

Objective: Investigate diabetes severity measures as potential risk factors for COVID-19 infection and COVID-19 outcomes.

Design, Participants, Measures: In integrated healthcare systems in Colorado, Oregon, and Washington, we identified a cohort of adults on February 29, 2020 (n = 1,086,918) and conducted follow-up through February 28, 2021.

Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes.

Significant attention has been given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this approach through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are ideally gathered through the use of Sequential Multiple Assignment Randomized Trials (SMARTs), though longitudinal observational studies are commonly used due to the potentially prohibitive costs of conducting a SMART.

Identifying hypertensive disorders of pregnancy, a comparison of two epidemiologic definitions.

Introduction: Studies of hypertension in pregnancy that use electronic health care data generally identify hypertension using hospital diagnosis codes alone. We sought to compare results from this approach to an approach that included diagnosis codes, antihypertensive medications and blood pressure (BP) values.

Materials And Methods: We conducted a retrospective cohort study of 1,45,739 pregnancies from 2009 to 2014 within an integrated healthcare system.