Donna Spiegelman

Preventive Cardiology

Harvard University

About

Donna Spiegelman works in Preventive Cardiology affiliated with Harvard University.

Donna Spiegelman is the Susan Dwight Bliss Professor of Biostatistics at the Yale School of Public Health and also holds positions as Professor of Statistics and Data Science and Professor of Medicine in Cardiovascular Medicine. She directs the Center for Methods in Implementation and Prevention Science and serves as Assistant Director of Global Oncology at Yale Cancer Center. Her research includes cardiovascular disease epidemiology and prevention, alongside work in cancer, HIV/AIDS, and implementation science.

Research focus

Cardiovascular Disease EpidemiologyCardiometabolic Disease PreventionNutritional Epidemiology in Heart HealthHIV-Associated Cardiovascular ComplicationsImplementation Science for CV Prevention

Impact

850 pubs · 81,711 cites · h-index 147

Training

ScD in Biostatistics and Epidemiology
Harvard School of Public Health
1989
MS in Biostatistics
Harvard School of Public Health
1985
BA in Psychology
Brandeis University
1977

Affiliations

Work Experience

Susan Dwight Bliss Professor of Biostatistics

Yale School of Public Health · 2018 - Present

Professor of Medicine (Cardiovascular Medicine)

Yale School of Medicine · 2018 - Present

Assistant Director, Global Oncology

Yale Cancer Center · 2018 - Present

Professor of Epidemiologic Methods

Harvard T.H. Chan School of Public Health · 1992 - 2018

Post-doctoral Fellow

Channing Laboratory at Harvard Medical School · 1989 - 1992

Publications

Original Research

OCT 26LiuStatistics in Medicine

Calibrated Bayes Analysis of Cluster‐Randomized Trials

In cluster‐randomized trials (CRTs), entire clusters of individuals are randomized to treatment, and outcomes within a cluster are typically correlated. While frequentist approaches are standard practice for CRT analysis, Bayesian methods have emerged as a strong alternative. Previous work has investigated the use of Bayesian hierarchical models for continuous, binary, and count outcomes in CRTs, but these approaches focus on model‐based treatment effect coefficients as the target estimands, which may have ambiguous interpretation under model misspecification and informative cluster size. In this article, we introduce a calibrated Bayesian procedure for estimand‐aligned analysis of CRTs even in the presence of potentially misspecified models. We propose estimators targeting both the cluster‐average treatment effect (cluster‐ATE) and individual‐average treatment effect (individual‐ATE), particularly in scenarios with informative cluster sizes. We additionally explore strategies for summarizing the posterior samples that can achieve the frequentist coverage even under working model misspecification. We provide simulation evidence to demonstrate the model‐robustness property of the proposed estimators in CRTs, and further investigate the impact of covariate adjustment as well as the use of more flexible Bayesian nonparametric working models in the CRT context.

2w agoOpen Access

LiuStatistics in Medicine

Calibrated Bayes Analysis of Cluster‐Randomized Trials

cohort · n=44

AUG 1KiggunduJournal of Healthcare Leadership

Organizational readiness remained high without significant changes, while HTN PLUS clinic leads reported significant increases in overall leadership and proactive leadership.

May aid integrated hypertension care via HIV platforms; leaves open effects on clinical outcomes in resource-limited settings.

14w agoOpen Access

KiggunduJournal of Healthcare Leadership

Organizational readiness remained high without significant changes, while HTN PLUS clinic leads reported significant increases in overall leadership and proactive leadership.

Implication

May aid integrated hypertension care via HIV platforms; leaves open effects on clinical outcomes in resource-limited settings.

Janet W. Rich-Edwards

Preventive Cardiology · Statens Serum Institut

Such null results might lead clinicians to abandon APO history in evaluating individual patients' CVD risk. This, however, would overlook differences between population and individual risk prediction and timing of risk prediction. To improve population risk prediction, an exposure must (1) be strongly associated with the outcome, (2) predict the outcome independent of other risk factors in the model, and (3) be prevalent enough to shift population risk.

Hypertensive Disorders of Pregnancy and 10-Year Cardiovascular Risk Prediction

Journal of the American College of CardiologyStuart et al.Sep 2018

cohort · n=60,379JACC Highlight

MAY 2022StuartJournal of the American College of Cardiology

Hypertensive disorders of pregnancy linked to ~63% higher incident CVD risk, largely mediated by traditional risk factors.

The increased long-term cardiovascular risk associated with hypertensive disorders of pregnancy is largely mediated by the subsequent development of traditional cardiovascular risk factors.

May 2022Open Access

JACC Highlight

Level 3: Cohort Study

StuartJournal of the American College of Cardiology

Hypertensive disorders of pregnancy linked to ~63% higher incident CVD risk, largely mediated by traditional risk factors.

Implication

The increased long-term cardiovascular risk associated with hypertensive disorders of pregnancy is largely mediated by the subsequent development of traditional cardiovascular risk factors.

cohort · n=209,298JACC Highlight

JUL 2017SatijaJournal of the American College of Cardiology

Healthful plant-based diet is linked to ~25% lower CHD risk, while unhealthful plant foods increase risk.

Hypothesis-generating for plant-based diet quality in CHD prevention; randomized trials needed before clinical adoption.

Jul 2017

JACC Highlight

Level 3: Cohort Study

SatijaJournal of the American College of Cardiology

Healthful plant-based diet is linked to ~25% lower CHD risk, while unhealthful plant foods increase risk.

Implication

Hypothesis-generating for plant-based diet quality in CHD prevention; randomized trials needed before clinical adoption.

Original Research

FEB 1TangStatistics in Medicine

Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction

In this paper, we investigate the selection of minimal and efficient covariate adjustment sets for the imputation-based regression calibration method, which corrects for bias due to continuous exposure measurement error. We use directed acyclic graphs to illustrate how subject-matter knowledge aids in selecting these sets. For unbiased measurement error correction, researchers must collect, in both main and validation studies, (I) common causes of both the true exposure and the outcome, and (II) common causes of both measurement error and the outcome. For regression calibration under linear models, at minimum, covariate set (I) must be adjusted for in both the measurement error model (MEM) and the outcome model, while set (II) should be adjusted for in at least the MEM. Adjusting for non-risk factors that are correlates of true exposure or measurement error within the MEM alone improves efficiency. We apply this covariate selection approach to the Health Professionals Follow-up Study, assessing fiber intake's effect on cardiovascular disease. We also highlight potential pitfalls in data-driven MEM building that ignores structural assumptions. Additionally, we extend existing estimators to allow for effect modification. Finally, we caution against using regression calibration to estimate the effect of true nutritional intake through calibrating biomarkers.

40w agoOpen Access

TangStatistics in Medicine

Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction

Original Research

JAN 6LiBiometrics

Improving transportability of regression calibration under the main/external validation study design

In epidemiology, exposures are usually subject to measurement error (ME). Regression calibration with a validation study is widely employed as an analysis method to correct for ME in the main study (MS) due to its broad applicability and simple implementation. However, relying on an external validation study (EVS) carries the risk of introducing bias into the analysis. Specifically, if the parameters of the regression calibration model estimated from the EVS are not transportable to the MS, the subsequent estimator of the parameter will be biased. In this work, we improve the regression calibration method for linear regression models using an external validation study. Unlike the standard approach, our method ensures that the regression calibration model is transportable by estimating the parameters in the ME generating process using the external validation study and obtaining the remaining parameter values in the regression calibration model directly from the MS. This guarantees that parameter values in the regression calibration model will be applicable to the MS. We derived the theoretical properties of our method. The simulation results show that our method effectively reduces bias and maintains nominal confidence interval coverage. We applied this method to data from the Health Professionals Follow-Up Study and the Men's Lifestyle Validation Study to assess the effects of dietary intake on body weight.

44w agoOpen Access

LiBiometrics

Improving transportability of regression calibration under the main/external validation study design

Original Research

SEP 2025Bing

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials

LAGO trials enhance statistical power and reduce risks of failure in maternal-newborn care interventions, suggesting new metrics to optimize outcomes.

Sep 2025Open AccessPreprint

Preprint

Bing

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials

Implication

LAGO trials enhance statistical power and reduce risks of failure in maternal-newborn care interventions, suggesting new metrics to optimize outcomes.

Cross-Sectional Study

SEP 2025TimalsenaBMJ Open

Factors associated with the adoption of the WHO Package of Essential Non-Communicable Diseases (PEN) Protocol 1 in primary healthcare settings in Nepal: a cross-sectional study

Cross-sectional study shows resource availability influences adoption of WHO non-communicable diseases protocol in Nepal.

Sep 2025Open Access

Level 4: Cross-Sectional

TimalsenaBMJ Open

Factors associated with the adoption of the WHO Package of Essential Non-Communicable Diseases (PEN) Protocol 1 in primary healthcare settings in Nepal: a cross-sectional study

Implication

Cross-sectional study shows resource availability influences adoption of WHO non-communicable diseases protocol in Nepal.

Original Research

AUG 2025ChanStatistics in Medicine

Nonparametric Estimation of the Potential Impact Fraction and the Population Attributable Fraction With Individual‐Level and Aggregated Data

Nonparametric methods estimate population attributable fraction in aggregated data, suggesting reduced bias in findings.

Aug 2025Open Access

ChanStatistics in Medicine

Nonparametric Estimation of the Potential Impact Fraction and the Population Attributable Fraction With Individual‐Level and Aggregated Data

Implication

Nonparametric methods estimate population attributable fraction in aggregated data, suggesting reduced bias in findings.

RCT

JUL 2025FangStatistical Methods in Medical Research

Design of egocentric network-based studies to estimate causal effects under interference

Many public health interventions are conducted in settings where individuals are connected and the intervention assigned to some individuals may spill over to other individuals. In these settings, we can assess: (a) the individual effect on the treated, (b) the spillover effect on untreated individuals through an indirect exposure to the intervention, and (c) the overall effect on the whole population. Here, we consider an egocentric network-based randomized design in which a set of index participants is recruited and randomly assigned to treatment, while data are also collected on their untreated network members. Such a design is common in peer education interventions conceived to leverage behavioral influence among peers. Using the potential outcomes framework, we first clarify the assumptions required to rely on an identification strategy that is commonly used in the well-studied two-stage randomized design. Under these assumptions, causal effects can be jointly estimated using a regression model with a block-diagonal structure. We then develop sample size formulas for detecting individual, spillover, and overall effects for single and joint hypothesis tests, and investigate the role of different parameters. Finally, we illustrate the use of our sample size formulas for an egocentric network-based randomized experiment to evaluate a peer education intervention for HIV prevention.

Jul 2025Open Access

Level 2: RCT

FangStatistical Methods in Medical Research

Design of egocentric network-based studies to estimate causal effects under interference

Qualitative Study

JUL 2025MaliBMC Health Services Research

Barriers and facilitators to patient utilization of noncommunicable disease services in primary healthcare facilities in Nepal: a qualitative study

BACKGROUND: The Nepalese government endorsed and implemented the Package of Essential Non-Communicable Disease Interventions (PEN) by the World Health Organization (WHO) to prevent and manage four major non-communicable diseases (NCDs): cardiovascular disease (CVD), diabetes, cancers, and chronic respiratory diseases. This study explored barriers and facilitators to patient utilization of NCD services at primary healthcare facilities in Nepal. METHODS: We conducted a qualitative study with a 35 purposive sample of patients living with one or more NCDs (hypertension, diabetes, chronic obstructive pulmonary disease (COPD/ asthma) who sought healthcare at primary healthcare facilities in 14 randomly selected districts in seven provinces in Nepal that implemented PEN. Trained qualitative researchers conducted in-depth interviews in person in a private setting using a semi-structured interview guide developed based on the Health Belief Model in the local language. The interviews were audio-recorded, transcribed verbatim, coded inductively and deductively, and analyzed by a framework approach using Dedoose software. RESULTS: From the perspectives of patients, key facilitators of service utilization encompassed free medicines, low-cost services, geographical and financial accessibility, less waiting time, positive interactions with health service providers, experiencing improvements in their health conditions, and support from family and peers. Barriers to utilizing services included inadequate health services (e.g., lack of medications and equipment), inaccessibility and affordability, inadequate health-related information from health service providers, low knowledge of NCD care, and lack of reminders or follow-ups. CONCLUSION: Enhancing NCD service utilization is potentially attainable through interventions that address patients’ knowledge, self-motivation, and misconceptions. Furthermore, strengthening the availability and accessibility of crucial services such as laboratory investigations, medications, equipment, and the patient-provider relationship is crucial for the sustainable implementation of PEN.

Jul 2025Open Access

Level 5: Qualitative Study

MaliBMC Health Services Research

Barriers and facilitators to patient utilization of noncommunicable disease services in primary healthcare facilities in Nepal: a qualitative study

Cohort Study

JUL 2025YangStatistics in Medicine

Exposure Measurement Error Correction in Longitudinal Studies With Discrete Outcomes

Environmental epidemiologists are often interested in estimating the effect of time‐varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main study/validation study design is developed. Various estimation procedures are explored. Simulation studies were conducted to assess its performance compared to standard analysis, and we found that the proposed method had good performance in terms of finite sample bias reduction and nominal coverage probability improvement. As an illustrative example, we applied the new method to a study of long‐term exposure to , in relation to the occurrence of anxiety disorders in the Nurses' Health Study II. Failing to correct the error‐prone exposure can lead to an underestimation of the chronic exposure effect of .

Jul 2025Open Access

Level 3: Cohort Study

YangStatistics in Medicine

Exposure Measurement Error Correction in Longitudinal Studies With Discrete Outcomes

Original Research

JUN 2025KhudyakovJournal of the Royal Statistical Society Series C (Applied Statistics)

Statistical methods for cost-effectiveness analysis of left-truncated and right-censored survival data with treatment delays

The incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB) are widely used for cost-effectiveness analysis. We develop methods for estimation and inference for the ICER and INB which use the semiparametric stratified Cox proportional hazard model, allowing for adjustment for risk factors. Since in public health settings, patients often begin treatment after they become eligible, we account for delay times in treatment initiation. Excellent finite sample properties of the proposed estimator are demonstrated in an extensive simulation study under different delay scenarios. We apply the proposed method to evaluate the cost-effectiveness of switching treatments among AIDS patients in Tanzania.

Jun 2025

KhudyakovJournal of the Royal Statistical Society Series C (Applied Statistics)

Statistical methods for cost-effectiveness analysis of left-truncated and right-censored survival data with treatment delays

Original Research

MAY 2025PoudelInternational Journal of Surgery Global Health

Validation of sexually transmitted disease-related stigma and shame scale in Nepalese women

Background: Stigma and shame associated with sexually transmitted diseases (STDs) are known to adversely impact patients’ psychosocial well-being and treatment outcomes. However, there is limited research on this topic among Nepali women. This study aimed to validate a Nepali version of the STD-related stigma and shame scale in Nepali women. Methods: The STD-related stigma and shame scale was translated and modified into Nepali. A total of 426 Nepali women, aged 30–60, were recruited using snowball sampling from two municipalities in the Kavrepalanchok district of Nepal. Data were collected through telephone interviews and recorded using KoboToolbox. Item-total correlation was used to assess item significance, while Pearson correlation examined shared characteristics among subscales. Analysis of variance was employed to evaluate construct validity. Confirmatory factor analysis (CFA) assessed the model fit, and Cronbach’s α was calculated to determine the internal consistency of the translated scale. Results: The internal consistency of the Nepali STD-related stigma and shame scale was good; the corresponding Cronbach’s α for stigma and shame were 0.891 and 0.859, respectively. CFA confirmed the two-factor structure with acceptable model fit: root mean square error of approximation = 0.09, goodness of fit index (GFI) = 0.92, adjusted goodness of fit index = 0.88, comparative fit index = 0.94, Tucker–Lewis index = 0.92, normed fit index = 0.93, and chi-square/degree of freedom ratio (χ 2 /df) = 4.9. Stigma (2.46, P = 0.02) and shame (2.85, P = 0.02) scores were higher among women aged ≥49 years. Those without formal education had greater stigma (1.24, P < 0.01) and shame (2.92, P < 0.01). Women involved in agriculture also scored higher on stigma (2.46, P = 0.01) and shame (2.80, P = 0.03). Conclusions: The internal reliability and model fit of the Nepali STD-related stigma and shame scale were satisfactory. The measure is appropriate for measuring stigma and shame associated with STDs in Nepali women.

May 2025Open Access

PoudelInternational Journal of Surgery Global Health

Validation of sexually transmitted disease-related stigma and shame scale in Nepalese women

Original Research

APR 2025ZhangInternational Journal of Environmental Health Research

Exposure measurement error in air pollution health effect studies: a pooled analysis of personal exposure validation studies in 17 communities across the United States

were 0.46 (95% CI: 0.13, 0.78) and 0.97 (0.35, 1.59), respectively. Significant coefficients were also found for S (0.48; 95% CI: 0.27, 0.68), Cd (0.47; 0.17, 0.76), and Ni (0.17; 0.02, 0.32). Point estimates for calibration coefficients were all below one, indicating that using the nearest monitors as exposure surrogates would attenuate associations with health risks. The measurement error in component-wise analysis highlights the need for incorporating these calibration coefficients into future studies to adjust for such errors adequately.

Apr 2025Open Access

ZhangInternational Journal of Environmental Health Research

Exposure measurement error in air pollution health effect studies: a pooled analysis of personal exposure validation studies in 17 communities across the United States

Human Clinical Trial

APR 2025BingBiometrics

Learn-As-you-GO (LAGO) trials: optimizing treatments and preventing trial failure through ongoing learning

It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary to adapt, tailor, or tweak a complex intervention package in public health implementation trials, especially when the intervention package does not have the desired effect. This article presents conditions under which the resulting analyses remain valid even when the intervention package is adapted while a trial is ongoing. Our results on such Learn-As-you-GO (LAGO) trials extend the theory of LAGO for binary outcomes following a logistic regression model to LAGO for continuous outcomes under flexible conditional mean models. Because the mathematical methods for binary outcomes do not apply to continuous outcomes, the theory presented in this paper is entirely new. We derive point and interval estimators of the intervention effects and ensure the validity of hypothesis tests for an overall intervention effect. We develop a confidence set for the optimal intervention package, which achieves a pre-specified mean outcome while minimizing cost, and confidence bands for the mean outcome under all intervention package compositions. This work will be useful for the design and analysis of large-scale intervention trials where the intervention package is adapted, tailored, or tweaked while the trial is ongoing.

Apr 2025Open Access

Level 3: Clinical Trial

BingBiometrics

Learn-As-you-GO (LAGO) trials: optimizing treatments and preventing trial failure through ongoing learning

Original Research

APR 2025León‐MaldonadoHuman Vaccines & Immunotherapeutics

Barriers and facilitators to a combined strategy of HPV vaccination and cervical cancer screening among Mexican women

HPV-FASTER is an innovative public health intervention combining HPV vaccination and HPV-based screening in adult women at the same visit. FASTER-Tlalpan adapted the combined HPV-FASTER strategy in Tlalpan, Mexico City for women aged 25-45 years. To understand the barriers and facilitators to participation in a combined strategy, we conducted semi-structured interviews with 14 FASTER-Tlalpan participants. We used the constant comparative method for the analysis, as well as the socioecological model to organize the findings. At the intrapersonal level, barriers included the belief that only younger women are at risk for HPV, embarrassment about the pelvic exam, and lack of time, while facilitators were having information regarding the benefit of the combined strategy, perception of time saved by having both procedures at once, feeling reassured about their health, self-esteem regarding their health, and perceived severity of cervical cancer. Interpersonal-level barriers were experiences of stigma and prejudice, and lack of support from partners, while facilitators were family encouragement and peer-to-peer communications. Institutional-level barriers were lack of infrastructure and inconvenient hours at the health center, perceived high time burden, and low quality of care from providers, while facilitators included high-quality care by health center personnel, including partners in the combined strategy, and phone reminders. Community-level facilitators included willingness to participate. Public policy facilitators included mass information campaigns and free procedures. Our findings point to significant barriers which need to be addressed, along with facilitators which can be leveraged to scale up the combined strategy in similar settings.

Apr 2025Open Access

León‐MaldonadoHuman Vaccines & Immunotherapeutics

Barriers and facilitators to a combined strategy of HPV vaccination and cervical cancer screening among Mexican women

RCT

FEB 2025OwenStatistics in Medicine

Power and Sample Size Calculations for Cluster Randomized Hybrid Type 2 Effectiveness‐Implementation Studies

Hybrid studies allow investigators to simultaneously study an intervention effectiveness outcome and an implementation research outcome. In particular, type 2 hybrid studies support research that places equal importance on both outcomes rather than focusing on one and secondarily on the other (i.e., type 1 and type 3 studies). Hybrid 2 studies introduce the statistical issue of multiple testing, complicated by the fact that they are typically also cluster randomized trials. Standard statistical methods do not apply in this scenario. Here, we describe the design methodologies available for validly powering hybrid type 2 studies and producing reliable sample size calculations in a cluster-randomized design with a focus on binary outcomes. Through a literature search, 18 publications were identified that included methods relevant to the design of hybrid 2 studies. Five methods were identified, two of which did not account for clustering but are extended in this article to do so, namely the combined outcomes approach and the single 1-degree of freedom combined test. Procedures for powering hybrid 2 studies using these five methods are described and illustrated using input parameters inspired by a study from the Community Intervention to Reduce CardiovascuLar Disease in Chicago (CIRCL-Chicago) Implementation Research Center. In this illustrative example, the intervention effectiveness outcome was controlled blood pressure, and the implementation outcome was reach. The conjunctive test resulted in higher power than the popular p value adjustment methods, and the newly extended combined outcomes and single 1-DF test were found to be the most powerful among all of the tests.

Feb 2025

Level 2: RCT

OwenStatistics in Medicine

Power and Sample Size Calculations for Cluster Randomized Hybrid Type 2 Effectiveness‐Implementation Studies

Original Research

JAN 2025ChengJournal of the Royal Statistical Society Series C (Applied Statistics)

Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome

We investigate the impact of exposure measurement error on assessing mediation with a survival outcome modelled by Cox regression. We first derive the bias formulas of natural indirect and direct effects with a rare outcome and no exposure-mediator interaction. We then develop several calibration approaches to correct for the measurement error-induced bias, and generalize our methods to accommodate a common outcome and an exposure-mediator interaction. We apply the proposed methods to analyse the Health Professionals Follow-up Study (1986-2016) and evaluate the extent to which reduced body mass index mediates the protective effect of physical activity on risk of cardiovascular diseases.

Jan 2025Open Access

ChengJournal of the Royal Statistical Society Series C (Applied Statistics)

Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome

All publications →

About

Donna Spiegelman works in Preventive Cardiology affiliated with Harvard University.

Research focus

Cardiovascular Disease EpidemiologyCardiometabolic Disease PreventionNutritional Epidemiology in Heart HealthHIV-Associated Cardiovascular ComplicationsImplementation Science for CV Prevention

Impact

850 pubs · 81,711 cites · h-index 147

Training

ScD in Biostatistics and Epidemiology
Harvard School of Public Health
1989
MS in Biostatistics
Harvard School of Public Health
1985
BA in Psychology
Brandeis University
1977

Work Experience

Susan Dwight Bliss Professor of Biostatistics

Yale School of Public Health · 2018 - Present

Professor of Medicine (Cardiovascular Medicine)

Yale School of Medicine · 2018 - Present

Assistant Director, Global Oncology

Yale Cancer Center · 2018 - Present

Professor of Epidemiologic Methods

Harvard T.H. Chan School of Public Health · 1992 - 2018

Post-doctoral Fellow

Channing Laboratory at Harvard Medical School · 1989 - 1992

Publications

Original Research

OCT 26LiuStatistics in Medicine

Calibrated Bayes Analysis of Cluster‐Randomized Trials

2w agoOpen Access

LiuStatistics in Medicine

Calibrated Bayes Analysis of Cluster‐Randomized Trials

cohort · n=44

AUG 1KiggunduJournal of Healthcare Leadership

Organizational readiness remained high without significant changes, while HTN PLUS clinic leads reported significant increases in overall leadership and proactive leadership.

May aid integrated hypertension care via HIV platforms; leaves open effects on clinical outcomes in resource-limited settings.

14w agoOpen Access

KiggunduJournal of Healthcare Leadership

Organizational readiness remained high without significant changes, while HTN PLUS clinic leads reported significant increases in overall leadership and proactive leadership.

Implication

May aid integrated hypertension care via HIV platforms; leaves open effects on clinical outcomes in resource-limited settings.

Janet W. Rich-Edwards

Preventive Cardiology · Statens Serum Institut

Such null results might lead clinicians to abandon APO history in evaluating individual patients' CVD risk. This, however, would overlook differences between population and individual risk prediction and timing of risk prediction. To improve population risk prediction, an exposure must (1) be strongly associated with the outcome, (2) predict the outcome independent of other risk factors in the model, and (3) be prevalent enough to shift population risk.

Hypertensive Disorders of Pregnancy and 10-Year Cardiovascular Risk Prediction

Journal of the American College of CardiologyStuart et al.Sep 2018

cohort · n=60,379JACC Highlight

MAY 2022StuartJournal of the American College of Cardiology

Hypertensive disorders of pregnancy linked to ~63% higher incident CVD risk, largely mediated by traditional risk factors.

The increased long-term cardiovascular risk associated with hypertensive disorders of pregnancy is largely mediated by the subsequent development of traditional cardiovascular risk factors.

May 2022Open Access

JACC Highlight

Level 3: Cohort Study

StuartJournal of the American College of Cardiology

Hypertensive disorders of pregnancy linked to ~63% higher incident CVD risk, largely mediated by traditional risk factors.

Implication

The increased long-term cardiovascular risk associated with hypertensive disorders of pregnancy is largely mediated by the subsequent development of traditional cardiovascular risk factors.

cohort · n=209,298JACC Highlight

JUL 2017SatijaJournal of the American College of Cardiology

Healthful plant-based diet is linked to ~25% lower CHD risk, while unhealthful plant foods increase risk.

Hypothesis-generating for plant-based diet quality in CHD prevention; randomized trials needed before clinical adoption.

Jul 2017

JACC Highlight

Level 3: Cohort Study

SatijaJournal of the American College of Cardiology

Healthful plant-based diet is linked to ~25% lower CHD risk, while unhealthful plant foods increase risk.

Implication

Hypothesis-generating for plant-based diet quality in CHD prevention; randomized trials needed before clinical adoption.

Original Research

FEB 1TangStatistics in Medicine

Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction

40w agoOpen Access

TangStatistics in Medicine

Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction

Original Research

JAN 6LiBiometrics

Improving transportability of regression calibration under the main/external validation study design

44w agoOpen Access

LiBiometrics

Improving transportability of regression calibration under the main/external validation study design

Original Research

SEP 2025Bing

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials

LAGO trials enhance statistical power and reduce risks of failure in maternal-newborn care interventions, suggesting new metrics to optimize outcomes.

Sep 2025Open AccessPreprint

Preprint

Bing

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials

Implication

LAGO trials enhance statistical power and reduce risks of failure in maternal-newborn care interventions, suggesting new metrics to optimize outcomes.