About

Christopher H. Schmid, PhD, is a Professor of Biostatistics and a former Chair of the Department of Biostatistics at Brown University. He is a founding member of the Center for Evidence Synthesis in Health at the Brown School of Public Health. His research focuses on methods and applications for meta-analysis, particularly Bayesian methods and software, as well as predictive models derived from combining data from different sources. He has a special interest in clinical trials, including N-of-1 trials and single-person multiple crossover studies, and has published extensively on meta-regression, multivariate methods, network meta-analysis, and the integration of N-of-1 studies. Dr. Schmid has contributed to the development of national standards for systematic reviews through the Institute of Medicine and has collaborated with medical and public health scientists on numerous applications. He holds a PhD in Statistics from Harvard University and a BA in Mathematics from Haverford College. Prior to his current position, he was on the faculty at Tufts University School of Medicine, where he served as Director of the Biostatistics Research Center and as Associate Director of the Clinical and Translational Science program.

Research topics

• Medicine
• Computer Science
• Applied psychology
• Medical physics
• Traditional medicine
• Genetics
• Environmental health
• Surgery
• Botany
• Biology
• Pathology
• Psychology

Selected publications

• Untangling Dialysis Received in a Nursing Home from Home Hemodialysis in the Community

  Journal of the American Society of Nephrology · 2026-04-02 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Selected Impacts of Urban Heat Islands on Emergency Medical Services Utilization in Rhode Island

  Western Journal of Emergency Medicine · 2026-04-14

  articleOpen access

  Introduction: Excessive environmental heat exposure is clearly associated with an increased likelihood that individual patients will suffer adverse health outcomes. Such heat exposure also strains healthcare systems via increased utilization, a burden which can challenge systems’ capacities. Health impacts vary geographically with urban heat islands potentially contributing to higher temperatures and greater health risks. However, those most vulnerable to this exposure are not well identified. Our objective in this novel study was to compare and quantify differences in emergency medical services (EMS) use by selected patients during hot days in Rhode Island. Patients were recruited from low socioeconomic residential locations, stratified by whether they accessed EMS from within one of the state’s “urban heat islands,” or from other locations without “heat island” effects. We also compared selected patient demographic characteristics, and other EMS run data, between events associated with EMS access from these two types of areas. Methods: This retrospective, cross-sectional cohort study evaluated how the probability of an EMS encounter varied in response to daily mean temperature and the urban heat island status of the encounter location. We aggregated EMS dispatch data, daily mean temperature, urban heat island classification and the Area Deprivation Index of the encounter location. A quasi-Poisson regression model assessed the relationship between EMS encounter frequency and potential risk factors including daily temperature, urban heat island status, year, day of the week, sex, age, and relevant interaction terms. The model was restricted to low socioeconomic, residential encounter locations to reduce confounding (noted elsewhere by year) and focus on the target population. The primary outcome was the rate ratio (RR) of EMS encounters for urban heat island locations vs locations without an urban heat island effect, in response to summer temperatures. Secondary outcomes included RRs of EMS encounters stratified by age, sex, weekday vs weekend, and year. Results: Higher temperatures were associated with increased EMS call rates across all demographic subgroups. A 5 °F (2.8 °C) increase in mean daily temperature was associated with an increase in an overall EMS encounter rate of 1.5% (RR, 1.015; 95% CI, 1.005-1.031, P = .004). On a weekday in 2021, at 75 °F degrees, 68 EMS encounters would be predicted for the residential, low socioeconomic status locations in the state while at 95 °F, 73 EMS encounters would be expected. The EMS rates were consistently higher in urban heat islands across all study years, after accounting for daily temperature, year, day of the week, demographic characteristics, population size and interactions between age, sex, urban heat island and weekday vs weekend. The largest relative increase in EMS encounters was observed in 2019, with rates 34% higher in urban heat islands compared to locations without an urban heat island effect (RR, 1.34; 95% CI, 1.27-1.42). The smallest increase occurred in 2020 (RR, 1.12; 95% CI, 1.06-1.18). Conclusion: In residential and low socioeconomic locations, living in an urban heat island increased the probability of an EMS encounter, highlighting potential compounding effects of social and environmental vulnerability. As climate change intensifies extreme heat events, locationally targeted interventions may be critical in reducing heat-related health impacts.

  PublisherDOI

• End-Stage Renal Disease Treatment Choices Model and Use of Home Dialysis and Kidney Transplant

  JAMA Health Forum · 2026-04-24

  articleOpen access

  Importance: To increase the use of home dialysis and kidney transplant, the Centers for Medicare & Medicaid Services launched the End-Stage Renal Disease Treatment Choices (ETC) model, a mandatory, randomized pay-for-performance program applied to 30% of US hospital referral regions. Its impact after 4 years of implementation is uncertain. Objective: To assess the ETC model's impact on home dialysis, kidney transplant, and transplant waitlist, as well as measure the rate of financial penalties. Design, Setting, and Participants: This retrospective cross-sectional study used traditional Medicare claims and enrollment data for beneficiaries with kidney failure linked to concurrent transplant data from the United Network for Organ Sharing from January 1, 2017 (4 years before model implementation), to September 30, 2024 (3.75 years postimplementation). Exposures: Receiving dialysis treatment in a region randomly assigned to the ETC model. Main Outcomes and Measures: Primary outcomes were rates of home dialysis, kidney transplant, and transplant waitlist, as well as facility-level financial penalization. Facility-level financial penalties were assessed using Centers for Medicare & Medicaid Services-published performance data. Results: The study population included 795 232 persons with kidney failure (mean [SD] age, 61.8 [14.4] years; 41.5% female), reflecting 20 729 696 person-months from January 1, 2017, to September 30, 2024. The rate of home dialysis increased from 12.8% to 16.7% of attributed patient-months in ETC regions (change of 3.9 percentage points [pp]) and from 13.7% to 17.3% in control regions (change of 3.7 pp), yielding an adjusted differences-in-differences of -0.1 pp (95% CI, -0.6 to 0.5 pp). The number of kidney transplants per 1000 patient-months increased from 3.3 to 4.5 in ETC regions (change of 1.2) and from 3.4 to 4.4 in control regions (change of 1.0), resulting in a differences-in-differences of 0.2 pp (95% CI, -0.1 to 0.4 pp). The percentage of patients per month on the transplant waitlist decreased from 16.1% to 15.5% in ETC regions (change of -0.5 pp) and from 17.7% to 16.7% in control regions (change of -1.0 pp). The adjusted differences-in-differences for transplant waitlist was 0.6 pp (95% CI, -0.3 to 1.6 pp). The proportion of ETC facilities receiving financial penalties increased from 13.8% in 2021 to 25.1% in 2023. Subgroup analyses showed no meaningful differential effects of the model. Conclusions and Relevance: This cross-sectional study shows that after nearly 4 years, the ETC model was not associated with meaningful increases in home dialysis, kidney transplant, or transplant waitlist, while the proportion of facilities receiving financial penalties increased. Future value-based payment models may need to move beyond narrowly targeted financial incentives to address the broader structural and patient-level barriers that influence access to complex specialty care.

  PublisherOA PDFDOI

• Meta-analysis models relaxing the random effects normality assumption: methodological systematic review and simulation study

  Research Square · 2025-04-15 · 1 citations

  preprintOpen access
  PublisherOA PDFDOI

• WHERE YOU LIVE MATTERS: SOCIOECONOMIC DISPARITIES IN FERTILITY PRESERVATION WITHIN A MANDATED STATE

  Fertility and Sterility · 2025-12-01

  article
  PublisherDOI

• Meta-analysis models relaxing the random-effects normality assumption: methodological systematic review and simulation study

  BMC Medical Research Methodology · 2025-10-16

  articleOpen access

  Random-effects meta-analysis is widely used for synthesizing the studies of a systematic review assuming a normal distribution for the study-specific effects. However, this assumption might not always be plausible. Alternative options have been suggested but not used in published meta-analyses. We conducted a systematic review to identify articles that proposed alternative meta-analysis models assuming non-normal distributions for the random effects, such as skewed or semi-parametric distributions. Subsequently, we performed a simulation study to evaluate the performance of the identified models and to compare them with the normal model. We considered 22 scenarios varying the amount of random-effects variance, the number of included studies, and the shape of the true distribution: normal, skew-normal, and mixture of two normal distributions. For each scenario, we generated 1000 meta-analyses datasets. To investigate additional aspects of the alternative models, we also applied them at three extracted simulated datasets representing three scenarios with different true distributions. We identified in total 27 articles suggesting 24 alternative models that can be classified into three broad categories: models based on long-tail and skewed distributions, on mixtures of distributions, and on Dirichlet process priors (DP). We compared 15 models in our simulation study implemented in the Frequentist or Bayesian framework. Results revealed small differences in bias between the different models but larger differences in the level of coverage probability. Scenarios with large random-effects variance, lead to more inaccurate estimates of the mean of the random-effects distribution. However, mixture and semi-parametric models revealed latent underlying clustering of studies and assisted to form subgroups of common characteristics. The three simulated datasets demonstrated similar patterns with the simulation study for the bias of the mean of the random-effects distribution. Focusing only on the mean of the random-effects distribution in meta-analysis can be misleading when substantial heterogeneity is suspected or outliers are present. In such cases, identifying the factors that differentiate the studies and looking at the prediction intervals can be very informative. Based on our simulation, investigators could have the normal model as their starting point and consider alternative models as sensitivity analysis in view of seemingly non-normal data.

  PublisherOA PDFDOI

• Performance of Dialysis Facilities after Health-Equity Scoring Incentive

  New England Journal of Medicine · 2025-04-23 · 2 citations

  letterOpen access
  PublisherOA PDFDOI

• Evaluating clinical utility of multi-category outcome risk prediction models

  medRxiv · 2025-04-28

  preprintOpen access

  Abstract Diagnostic models are typically evaluated by assessing their calibration and discrimination; however, neither criterion assesses the practical consequences of using a model. Decision Curve Analysis (DCA) is a method for measuring clinical utility for binary outcome models over a range of risk thresholds. While the utility of polytomous outcome models can be assessed by applying DCA to different dichotomizations of their categories, no method exists to synthesize the binary measures into a single value. This paper illustrates DCA for polytomous outcomes and extends its concepts to develop a summary utility measure for polytomous outcome models. We apply this method to three ordinal logistic regression models, including the NIRUDAK and DHAKA models for predicting dehydration severity in patients over and under five years of age, respectively. Combining the concepts of Standardized Net Benefit (sNB) and Weighted Area Under the Net Benefit Curve, we propose the Weighted Area Under the sNB Curve ( wAUC sNB ), which can be determined for every dichotomization of a polytomous outcome. Next, we propose an average of wAUC sNB s weighted by the relative clinical importance of each dichotomized outcome. We term these weights importance weights and define this new measure as the Integrated Weighted Area Under the sNB Curve ( IwAUC sNB ). We apply binary DCA to the dehydration models, discuss its limitations, and apply the Integrated wAUC sNB to evaluate the average utility of each model. Finally, we compare these models to criteria from the World Health Organization (WHO) and observe how the results vary for different distributional assumptions of the risk thresholds. Applied to the NIRUDAK, DHAKA, and WHO models, the Integrated wAUC sNB demonstrated that both the DHAKA and NIRUDAK models could classify individuals as benefiting from treatment better than the WHO algorithms and either of the reference strategies of treating everyone or no one.

  PublisherOA PDFDOI

• Incorporating Preprints in Systematic Reviews: A Preliminary Study of a Novel Method for Rapid Evidence Synthesis

  medRxiv · 2025-07-16

  preprintOpen access

  Objectives: By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies. Materials and Methods: We propose a two-stage method to include preprints in meta-analyses. In Stage A, preprints are integrated through restriction or imputation and weighted by a confidence score reflecting their publication likelihood. In Stage B, we assess and adjust for potential publication or reporting biases. Results: This preliminary study employed a two-stage procedure validated with two COVID-19 treatment case studies. For hydroxychloroquine, the relative risk (RR) was 1.06 [95% CI: 0.62, 1.80], suggesting no mortality benefit over placebo. For corticosteroids, the RR was 0.88 [95% CI: 0.62, 1.27], which, while not statistically significant, aligns with evidence supporting a mortality benefit. Discussion: Our research aims to bridge a significant methodological gap by providing a solution for timely evidence synthesis, particularly in the face of the overwhelming number of publications surrounding COVID-19. Conclusion: This preliminary study presents a method to efficiently synthesize COVID-19 research, including non-peer-reviewed preprints, to support clinical and policy decisions amidst the information surge.

  PublisherOA PDFDOI

• Study Design and Rationale for The Breathe Easier With Tadalafil Therapy for Exercise‐Related Dyspnea in COPD‐PH (BETTER COPD‐PH)

  Pulmonary Circulation · 2025-09-29 · 1 citations

  articleOpen access

  Dyspnea, a debilitating symptom of COPD, worsens health-related quality of life (HRQL), reduces daily physical activity, increases health care utilization, and is more closely associated with survival than airflow limitation. Thus, having treatments that reduce dyspnea in COPD is important. Pulmonary hypertension (PH) is a common complication of COPD that is associated with severe dyspnea, more frequent COPD exacerbations, and increased mortality. Multiple causes of PH, including a reduction in bioavailable vasodilator nitric oxide (NO), are associated with COPD (COPD-PH). Phosphodiesterase type-5 inhibitor (PDE5i) therapy restores NO signaling and improves hemodynamics and dyspnea in patients with Group 1 Pulmonary Arterial Hypertension, but has not been proven effective in COPD-PH. In a prior study (ClinicalTrials. gov identifier: NCT01862536), we investigated effects of 12 months of oral PDE5i therapy with tadalafil on 6-min walk distance (6MWD) in a multi-center, randomized, placebo-controlled trial funded by the Department of Veterans Affairs. While tadalafil did not change 6MWD at 12 months, the treatment group experienced clinically meaningful improvements in patient-reported dyspnea and HRQL at 6 months. Because of the importance of mitigating dyspnea in COPD-PH, we developed a new study protocol examining the effect of PDE-5i therapy in COPD-PH, with a reduction in dyspnea the primary outcome. In the current study (NCT05937854), we will conduct a prospective, randomized, double-blind, multi-center clinical trial to evaluate the effects of 6 months of maximally tolerated therapy with tadalafil (target dose 40 mg/day) versus placebo on dyspnea, as measured by University of California San Diego Shortness of Breath Questionnaire.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01HS008532

  NIH · $422k · 1998

• NIH Grant R33RR017109

  NIH · $1.2M · 2006

• NIH Grant R01HS010064

  NIH · $698k · 2002

• NIH Grant R01HS013328

  NIH · $542k · 2006

• NIH Grant R01HS018574

  NIH · $1.2M · 2013

Frequent coauthors

• Issa J Dahabreh

  315 shared

• John P. A. Ioannidis

  Stanford University

  302 shared

• Xin Sun

  West China Hospital of Sichuan University

  256 shared

• Ravi Varadhan

  256 shared

• Geert J. M. G. van der Heijden

  University of Amsterdam

  256 shared

• Joel Gagnier

  Western University

  256 shared

• Stefan Schandelmaier

  University of Basel

  256 shared

• Rodney A. Hayward

  VA Center for Clinical Management Research

  256 shared

Education

• Ph.D.

  Harvard University

  1991

• B.A.

  Haverford College

  1983

Awards & honors

• Fellow of the American Statistical Association (ASA)
• Elected member of the Society for Research Synthesis Methodo…
• Founding Editor of the journal Research Synthesis Methods
• Chair of the Section on Health Policy Statistics at ASA