Michael O. Harhay
VerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 2008–2026
About
Michael O. Harhay, PhD, is an Associate Professor of Epidemiology in Biostatistics and Epidemiology at the Perelman School of Medicine, University of Pennsylvania. He serves as a Scientific Advisor for Statistical Evaluation at the Penn Medicine Nudge Unit within the Center for Health Care Transformation and Innovation, and is the Founding Director of the Center for Clinical Trials Innovation at Penn Medicine. His research expertise includes clinical epidemiology, medical statistics, and evidence synthesis. Dr. Harhay has a comprehensive educational background with degrees in Neuroscience and Philosophy from Muhlenberg College, and multiple advanced degrees from the University of Pennsylvania, including a PhD in Epidemiology. His work involves applying Bayesian statistics and other advanced statistical methods to clinical research, with a focus on improving trial design, analysis, and interpretation in critical care and other medical fields.
Research topics
- Medicine
- Internal medicine
- Intensive care medicine
- Data Mining
- Computer Science
- Nursing
- Virology
- Emergency medicine
- Family medicine
- Mathematics
- Psychiatry
Selected publications
Trials · 2026-02-25
articleOpen accessBACKGROUND: Depressive symptoms and anxiety are highly prevalent among people with chronic obstructive pulmonary disease (COPD), strongly associated with poor outcomes, and rarely recognized or treated. Integrating families into interventions may amplify supportive care treatment effects and overcome common challenges, yet this strategy is understudied. The Supporting Evidence-based Responses to Emotional Needs in Emphysema (SERENE) trial's main objective is to identify the mechanisms through which a family-partnered Coping Skills Training (CST) reduces depressive symptoms among patients with COPD, testing five putative mechanisms: family relationship quality, patient and caregiver self-efficacy, patient loneliness, and caregiver psychological distress. METHODS: SERENE will enroll 375 patient-support person (i.e., family caregiver) dyads from two academic health systems. Eligible patients have documented COPD, elevated levels of depressive symptoms (i.e. PHQ-8 scores ≥ 8), and age ≥ 18 years old. Ineligible participants are those with new or changing behavioral health treatments or behavioral health emergencies. After enrollment by research staff, we randomize dyads in a 2:1 ratio to receive either a 12-week CST program or a 12-week COPD education program, respectively. Both are delivered to the dyads via phone or videoconferencing sessions and, therefore, arm assignment is not blinded to staff nor participants. We will test whether randomization to receipt of CST leads to improvements in patients' depressive symptoms and test mechanisms of efficacy. The primary efficacy outcome is PHQ-9 scores 14 weeks following enrollment. Our five putative mechanisms, corresponding to those previously specified, are measured with the Family Emotional Involvement and Criticism Scale, the General Self-efficacy Scale, the UCLA Loneliness Scale, the PHQ-9, and the Generalized Anxiety Disorder-7. We will measure secondary outcomes through 12 months. Those performing data analyses will remain blinded to group assignments at the individual level until completion of the primary analyses. The study team identifies and reports serious adverse events (i.e., suicidal behaviors or psychiatric hospitalizations). DISCUSSION: SERENE will determine how scalable supportive care interventions that strengthen existing social networks, including the crucial support of family caregivers, improve outcomes in COPD. The results will lay the foundation for paradigm-shifting approaches to managing COPD and similar illnesses through family-directed supportive care interventions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT06600126. Registered 6 September 2024, https://clinicaltrials.gov/study/NCT06600126 . The first enrollment occurred on 30 September 2024.
The Lancet Neurology · 2026-04-17 · 1 citations
articleOpen accessBACKGROUND: precursor supplementation with nicotinamide riboside, which have each shown benefits in animal and early clinical studies, on cardiopulmonary fitness in individuals with Friedreich's ataxia. METHODS: . Stage 1 analysis tested the difference between each active treatment versus the control group, and stage 2 analysis (if combination therapy was effective) tested the difference between combination treatment and exercise alone; family-wise type 1 error was maintained <0·05. Analyses were by intention-to-treat. Adverse events were recorded systematically. This trial is registered with ClinicalTrials.gov (NCT04192136) and is complete. FINDINGS: =0·0299) for nicotinamide riboside and exercise in combination. Combination therapy was not statistically different from exercise alone (difference -0·05 ([95% CI -0·10 to 0·21]; p=0·49). Adverse events were all mild or moderate, and included gastrointestinal symptoms, falls, upper respiratory infections, and skin rashes. At least one moderate adverse event of interest in these categories was reported by seven (41%) participants in the control group; six (35%) in the nicotinamide riboside and no exercise group; three (19%) in the placebo and exercise group; and four (25%) in the nicotinamide plus exercise group. INTERPRETATION: The combination of nicotinamide riboside plus exercise for 12 weeks was safe and increased cardiopulmonary fitness in children and adults with Friedreich's ataxia. Longer studies are needed to establish whether adding nicotinamide riboside to exercise could be considered as part of a long-term, comprehensive treatment approach. FUNDING: US National Institutes of Health and Friedreich's Ataxia Research Alliance.
Figshare · 2026-01-01
articleOpen accessAdditional file 1: ACCORD Guidelines. Checklist for reporting consensus methods. Round 1 Executive Summary. Round 2 Executive Summary. Table S1. Selected comments from executive summaries.
Figshare · 2026-01-01
otherOpen accessAbstract Background Estimands are increasingly used in randomised trials to clarify research objectives. The ICH E9(R1) addendum sets out five attributes necessary to describe a well-defined estimand. However, the addendum was primarily developed for individually randomised trials. There is growing recognition that estimand descriptions for cluster randomised trials, where groups of individuals are randomised, may require specification of additional considerations. We conducted a Delphi study to assess stakeholder views on additional items for inclusion in a consensus extension of the ICH E9(R1) for cluster randomised trials. Methods We invited experts in estimands and cluster randomised trials to participate in a modified Delphi process to identify critical items for describing estimands in cluster randomised trials. The research team generated an initial list of eight items and definitions. Across three Delphi rounds, panellists scored items, suggested additional items, and provided open-ended rationales for responses. The consensus threshold was set as ≥ 70% of respondents rating an attribute as “essential” (i.e. score of ≥ 7 on a 9-point Likert scale) and < 15% of respondents rating the item as “not important” (i.e. a score of ≤ 3). Results Seventy-three (52%) invited individuals participated in Round 1. Response rates were 85% in Round 2 and 95% in Round 3. Panellists included largely statisticians (62, 85%) and clinical trialists (18, 25%). After Round 1, one additional item was added for Round 2 inclusion. After Round 3, five items met consensus criteria: how individuals and clusters are weighted, population of clusters, exposure time of clusters and individuals to the intervention, whether treatment effects are marginal or cluster-specific, and handling of cluster-level intercurrent events. Conclusions This Delphi identified expert consensus around the importance of several key items for defining estimands in cluster randomised trials. These results can inform the development of consensus guidance outlining the set of attributes to describe when defining estimands for cluster randomised trials.
Figshare · 2026-01-01
otherOpen accessAbstract Background Estimands are increasingly used in randomised trials to clarify research objectives. The ICH E9(R1) addendum sets out five attributes necessary to describe a well-defined estimand. However, the addendum was primarily developed for individually randomised trials. There is growing recognition that estimand descriptions for cluster randomised trials, where groups of individuals are randomised, may require specification of additional considerations. We conducted a Delphi study to assess stakeholder views on additional items for inclusion in a consensus extension of the ICH E9(R1) for cluster randomised trials. Methods We invited experts in estimands and cluster randomised trials to participate in a modified Delphi process to identify critical items for describing estimands in cluster randomised trials. The research team generated an initial list of eight items and definitions. Across three Delphi rounds, panellists scored items, suggested additional items, and provided open-ended rationales for responses. The consensus threshold was set as ≥ 70% of respondents rating an attribute as “essential” (i.e. score of ≥ 7 on a 9-point Likert scale) and < 15% of respondents rating the item as “not important” (i.e. a score of ≤ 3). Results Seventy-three (52%) invited individuals participated in Round 1. Response rates were 85% in Round 2 and 95% in Round 3. Panellists included largely statisticians (62, 85%) and clinical trialists (18, 25%). After Round 1, one additional item was added for Round 2 inclusion. After Round 3, five items met consensus criteria: how individuals and clusters are weighted, population of clusters, exposure time of clusters and individuals to the intervention, whether treatment effects are marginal or cluster-specific, and handling of cluster-level intercurrent events. Conclusions This Delphi identified expert consensus around the importance of several key items for defining estimands in cluster randomised trials. These results can inform the development of consensus guidance outlining the set of attributes to describe when defining estimands for cluster randomised trials.
International Journal of Epidemiology · 2026-04-17
preprintOpen accessCluster-randomized trials (CRTs) are a well-established class of designs for evaluating community-based interventions. An essential task in planning these trials is determining the number of clusters and cluster sizes needed to achieve sufficient statistical power for detecting a clinically relevant effect size. While methods for evaluating the average treatment effect (ATE) for the entire study population are well-established, sample size methods for testing heterogeneity of treatment effects (HTEs), i.e. treatment-covariate interaction or difference in subpopulation-specific treatment effects, in CRTs have only recently been developed. For pre-specified analyses of HTEs in CRTs, effect-modifying covariates should, ideally, be accompanied by sample size or power calculations to ensure the trial has adequate power for the planned analyses. Power analysis for testing HTEs is more complex than for ATEs due to the additional design parameters that must be specified. Power and sample size formulas for testing HTEs via linear mixed-effects models have been separately derived for different cluster-randomized designs, including single and multi-period parallel designs, crossover designs, and stepped-wedge designs, and for continuous and binary outcomes. This tutorial provides a consolidated reference guide for these methods and enhances their accessibility through an online R Shiny Calculator. We further discuss key considerations for conducting sample size and power calculations to test pre-specified HTE hypotheses in CRTs, highlighting the importance of specifying advanced estimates of intracluster correlation coefficients for both outcomes and covariates and their implications for power. The sample size methodology and calculator functionality are demonstrated through a real CRT example.
Open MIND · 2026-01-01
articleAdditional file 1: ACCORD Guidelines. Checklist for reporting consensus methods. Round 1 Executive Summary. Round 2 Executive Summary. Table S1. Selected comments from executive summaries.
Journal of General Internal Medicine · 2026-04-29
articleOpen accessBACKGROUND: Medicaid-insured individuals and those dually eligible for Medicare and Medicaid face high rates of adverse post-hospital outcomes, driven in part by fragmented care transitions and unmet social needs. Despite evidence supporting the use of home health services post-hospitalization, fewer than 20% of Medicaid-insured patients receive a referral. Thrive is an evidence-based transitional care model designed to improve post-discharge care for Medicaid-insured adults and duals by integrating traditional home health services with enhanced social and clinical support. OBJECTIVE: To examine whether implementing Thrive increased the number of patients referred to home health services by discharge planners and to evaluate the acceptability, appropriateness, and feasibility of Thrive and implementation strategy helpfulness. DESIGN: 24-month type 1 hybrid effectiveness-implementation stepped-wedge cluster-randomized trial among discharge planners working on medicine services at a single hospital in the northeastern United States. PARTICIPANTS: 14 discharge planners. INTERVENTION: Discharge planners were randomly assigned a date to begin Thrive referrals. They received initial training, bi-weekly reminders during their intervention step, and monthly clinical updates after all planners became actively involved in referrals. MAIN MEASURE: The primary outcome was referral to home health services. Secondary outcomes included acceptability, appropriateness, and feasibility of Thrive and helpfulness of implementation strategies. KEY RESULTS: Discharge planners who were trained to refer to Thrive were nearly twice as likely to make a home health referral compared to those who were not trained (OR = 1.98; 95% CI: 1.32-2.98; p = .001). Planners found Thrive to be acceptable, appropriate, and feasible, and rated in-person training and reminders from their manager the most helpful implementation strategies. CONCLUSIONS: The introduction of the Thrive transitional care program increased home health referrals. Expanding the use of home health services for Medicaid-insured individuals and duals has the potential to enhance care transitions and improve the quality of life following a hospital discharge. TRIAL REGISTRATION: ClinicalTrials.gov NCT05714605, date of registration: 2/6/2023; https://clinicaltrials.gov/ct2/show/NCT05714605.
Designing Future Cardiac Arrest Trials to Accommodate Heterogeneous Treatment Effects
JACC Advances · 2026-01-28
articleOpen accessAmerican Journal of Respiratory and Critical Care Medicine · 2025-07-01 · 3 citations
articleOpen accessSenior authorAbstract Selecting the optimal methodological framework for evidence synthesis presents a fundamental challenge in contemporary clinical research. In critical care, in which many interventions yield inconclusive results under traditional P value–based analyses, complementary analytical approaches can enhance our understanding of trial data. Although frequentist statistics remain predominant and Bayesian methods have recently experienced a resurgence of interest, the evidential (or likelihood) framework offers a methodological perspective that potentially bridges these two inferential paradigms. In this Concise Translational Review, we introduce readers to the evidential approach. To present the evidential approach as an analytical tool for critical care trials, we demonstrate its application using data from two mechanical ventilation trials (ART [the Alveolar Recruitment Trial], N = 1,010; and STAMINA [Strategy for Community Acquired Pneumonia Trial], N = 214) and one trial evaluating balanced solutions (BaSICS [Balanced Solutions in Intensive Care Study], N = 10,520). We focus on how concepts and terminology translate across paradigms, the framework’s measures of effect (i.e., likelihood ratios, support values, and support intervals), proposals for its use in sequential analysis and trial monitoring, and how to report results from this framework in research articles. We propose that the evidential framework provides a clinically intuitive approach to trial interpretation by focusing on the relative evidence between competing hypotheses, thereby offering additional and complementary insights that align with clinical reasoning processes. To facilitate implementation by the scientific community, we have developed an interactive Shiny (open-source web-based) application (https://fzampier.shinyapps.io/Likelihood_Shiny/).
Recent grants
NIH · $747k · 2018–2023
NIH · $2.2M · 2023–2028
NIH · $71k · 2017
NIH · $214k · 2020
NIH · $861k · 2021
Frequent coauthors
- 105 shared
Scott D. Halpern
University of Pennsylvania
- 36 shared
Mark E. Mikkelsen
Weill Cornell Medicine
- 28 shared
Meera N. Harhay
Drexel University
- 28 shared
Dylan S. Small
- 25 shared
Ernest K. Manders
University of Pittsburgh
- 25 shared
Ewan C. Goligher
- 25 shared
Harry S. Nayar
- 25 shared
Naveen K. Ahuja
Rutgers, The State University of New Jersey
Labs
Michael O. Harhay LabPI
Education
- 2016
PhD, Biostatistics, Epidemiology and Informatics
University of Pennsylvania
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