Research

Publications

Dong S, Jutkowitz E, Giardina J, Bilinski A. Screening Strategies to Reduce COVID-19 Mortality in Nursing Homes. JAMA Health Forum. 2024;5(4):e240688. doi:10.1001/jamahealthforum.2024.0688

Glynn D, Giardina J, Hatamyar J, Pandya A, Soares M, Kreif N. Integrating decision modeling and machine learning to inform treatment stratification. Health Economics. 2024. doi:10.1002/hec.4834

Bilinski A, Ciaranello A, Fitzpatrick MC, Giardina J, Shah M, Salomon JA, Kendall EA. Estimated Transmission Outcomes and Costs of SARS-CoV-2 Diagnostic Testing, Screening, and Surveillance Strategies Among a Simulated Population of Primary School Students. JAMA Pediatrics. 2022;176(7):679-689. doi:10.1001/jamapediatrics.2022.1326

Giardina J, Bilinski A, Fitzpatrick MC, Kendall EA, Linas BP, Salomon J, Ciaranello AL. Model-estimated association between simulated US elementary school-replated SARS-CoV2 transmission, mitigation intervention, and vaccine coverage across local incidence levels. JAMA Network Open. 2022;5(2): e2147827. doi:10.1001/jamanetworkopen.2021.47827

Valentine KD, Cha T, Giardina J, Marques F, Atlas SJ, Bedair H, Chen AF, Doorly T, Kang J, Leavitt L, Licurse A, O’Brien T, Sequist T, Sepucha K. Assessing the quality of shared decision making for elective orthopedic surgery across a large healthcare system: cross-sectional survey study. BMC Musculoskeletal Disorders. 2021;22(1):1-10. doi:10.1186/s12891-021-04853-x

Aladelokun O, Hanley M, Mu J, Giardina J, Rosenberg DW, Giardina C. Fatty acid metabolism and colon cancer protection by dietary methyl donor restriction. Metabolomics. 2021;17(9):1-11. doi:10.1007/s11306-021-01831-1

Bilinski A, Salomon JA, Giardina J, Ciaranello A, Fitzpatrick MC. Passing the test: a model-based analysis of safe school-reopening strategies. Annals of Internal Medicine. 2021;174(8):1090-1100. doi:10.7326/M21-0600

Giardina J, Cha T, Atlas SJ, Barry MJ, Freiberg AA, Leavitt L, Marques F, Sepucha K. Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data. BMC Medical Informatics and Decision Making. 2020;20(1):1-10. doi:10.1186/s12911-020-01175-1

Working Papers

COVID-19 Statistics, Policy modeling and Epidemiology Collective (C-SPEC). Defining high-value information for COVID-19 decision-making. medRxiv 2020.04.06.20052506 (2020). doi:10.1101/2020.04.06.20052506

Research-in-Progress

*Dissertation Project or Lead Researcher

“Can using heterogeneous treatment effects improve blood pressure treatment decisions?” with Ankur Pandya*

Intensive blood pressure control reduces the risk of cardiovascular events but can have serious adverse events, requires more frequent physician visits, and increases costs. Recent research has estimated heterogeneous treatment effects (HTEs) from intensive treatment with the goal of identifying which patients will benefit the most from intensive care, but this work has not evaluated whether using HTEs to make decisions would lead to improved outcomes. In this study, we assess the use of HTEs within a decision analytic framework, and estimate the value gained from individualizing care using HTEs across a range of decision objectives. We find that HTEs would only improve outcomes in a particular set of circumstances, and in most cases are not precise enough to successfully personalize blood pressure treatment decisions.

“Bayesian Joint Prediction of Risk Factor Trajectories and Disease Incidence in Microsimulation Models,” with Ankur Pandya, Nathaniel Alemayehu, and Sebastien Haneuse*

Microsimulation decision models often simulate disease incidence as a function of risk factors that evolve over time (e.g., blood pressure increasing with age). Existing models, however, typically rely on incidence rates estimated with standard survival analysis techniques, which make implausible assumptions about how risk factors change over time and could lead to biased results. To overcome these limitations, we apply a Bayesian approach that jointly estimates longitudinal risk factor trajectories and disease incidence, leading to more accurate and reliable risk prediction for microsimulation models, especially for models evaluating policies that depend on dynamic risk factors.

“Accounting for Self-Selection When Using Randomized Controlled Trials to Inform Policy Decisions and Clinical Practice Guidelines,” with Ankur Pandya*

Health policy decisions and clinical practice guidelines (CPGs) are often based on the average results of randomized controlled trials. This approach implicitly assumes that the self-selection into treatment encouraged by the policies or guidelines is either the same as the RCT or is not correlated with heterogeneous treatment effects. In many cases this is likely not true, especially when scaling interventions from a small trial, as individuals who self-select into treatment may be more or less likely to benefit from treatment than those who do not. We apply recent advances in instrumental variable analysis to estimate bounds on the variation in treatment effects as a function of the propensity to self-select into treatment and use decision modeling to assess the value of policies and CPGs that increase or decrease treatment uptake given the existence of these heterogeneous effects.