Analyses of multi-source data, such as data from multi-center randomized trials, individual participant data meta-analyses, or pooled analyses of observational studies, combine information to estimate an overall average treatment effect. However, if average treatment effects vary across data sources, commonly used approaches for multi-source analyses may not have a clear causal interpretation with respect to a target population of interest. In this paper, we provide identification and estimation of average treatment effects in a target population underlying one of the data sources in a point treatment setting for failure time outcomes potentially subject to right-censoring. We do not assume the absence of effect heterogeneity and hence our results are valid, under certain assumptions, when average treatment effects vary across data sources. We derive the efficient influence functions for source-specific average treatment effects using multi-source data under two different sets of assumptions, and propose a novel doubly robust estimator for our estimand. We evaluate the finite-sample performance of our estimator in simulation studies, and apply our methods to data from the HALT-C multi-center trials.
Publications
2025
IMPORTANCE: When randomized trials are unavailable or not feasible, observational studies can be used to answer causal questions about the comparative effects of interventions by attempting to emulate a hypothetical pragmatic randomized trial (target trial). Published guidance to aid reporting of these studies is not available.
OBJECTIVE: To develop consensus-based guidance for reporting observational studies performed to estimate causal effects by explicitly emulating a target trial.
DESIGN, SETTING, AND PARTICIPANTS: The Transparent Reporting of Observational Studies Emulating a Target Trial (TARGET) guideline was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework. The development included (1) a systematic review of reporting practices in published studies that had explicitly aimed to emulate a target trial; (2) a 2-round online survey (August 2023 to March 2024; 18 expert participants from 6 countries) to assess the importance of candidate items selected from previous research and to identify additional items; (3) a 3-day expert consensus meeting (June 2024; 18 panelists) to refine the scope of the guideline and draft the checklist; and (4) pilot of the draft checklist with stakeholders (n = 108; September 2024 to February 2025). The checklist was further refined based on feedback on successive drafts.
FINDINGS: The 21-item TARGET checklist is organized into 6 sections (abstract, introduction, methods, results, discussion, other information). TARGET provides guidance for reporting observational studies of interventions explicitly emulating a parallel group, individually randomized target trial, with adjustment for baseline confounders. Key recommendations are to (1) identify the study as an observational emulation of a target trial; (2) summarize the causal question and reason for emulating a target trial, (3) clearly specify the target trial protocol (ie, the causal estimand, identifying assumptions, data analysis plan) and how it was mapped to the observational data, and (4) report the estimate obtained for each causal estimand, its precision, and findings from additional analyses to assess the sensitivity of the estimates to assumptions, and design and analysis choices.
CONCLUSIONS AND RELEVANCE: Application of the TARGET guideline recommendations aims to improve reporting transparency and peer review and help researchers, clinicians, and other readers interpret and apply the results.
IMPORTANCE: When randomized trials are unavailable or not feasible, observational studies can be used to answer causal questions about the comparative effects of interventions by attempting to emulate a hypothetical pragmatic randomized trial (target trial). Published guidance to aid reporting of these studies is not available.
OBJECTIVE: To develop consensus based guidance for reporting observational studies performed to estimate causal effects by explicitly emulating a target trial.
DESIGN, SETTING, AND PARTICIPANTS: The Transparent Reporting of Observational Studies Emulating a Target Trial (TARGET) guideline was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework. The development included (1) a systematic review of reporting practices in published studies that had explicitly aimed to emulate a target trial; (2) a two round online survey (August 2023 to March 2024; 18 expert participants from six countries) to assess the importance of candidate items selected from previous research and to identify additional items; (3) a three day, expert consensus meeting (June 2024; 18 panelists) to refine the scope of the guideline and draft the checklist; and (4) pilot of the draft checklist with stakeholders (n=108; September 2024 to February 2025). The checklist was further refined based on feedback on successive drafts.
FINDINGS: The 21-item TARGET checklist is organized into six sections (abstract, introduction, methods, results, discussion, other information). TARGET provides guidance for reporting observational studies of interventions explicitly emulating a parallel group, individually randomized target trial, with adjustment for baseline confounders. Key recommendations are to (1) identify the study as an observational emulation of a target trial; (2) summarize the causal question and reason for emulating a target trial; (3) clearly specify the target trial protocol (ie, the causal estimand, identifying assumptions, data analysis plan) and how it was mapped to the observational data; and (4) report the estimate obtained for each causal estimand, its precision, and findings from additional analyses to assess the sensitivity of the estimates to assumptions, and design and analysis choices.
CONCLUSIONS AND RELEVANCE: Application of the TARGET guideline recommendations aims to improve reporting transparency and peer review and help researchers, clinicians, and other readers interpret and apply the results.
BACKGROUND: Patients with cardiovascular (CV) diseases are increasingly frail but rarely represented in trials. Understanding effect modification by frailty on CV trials is critical as it could help define treatment strategies in frail patients.
OBJECTIVES: This meta-analysis aims to assess the implications of frailty on CV outcomes in clinical trials.
METHODS: Randomized controlled trials examining the effects of frailty in the context of CV trials were included (CRD42024528279). Outcomes included a composite of major adverse cardiac events (MACE), all-cause mortality, CV mortality, hospitalizations, and frailty-specific outcomes (physical, quality of life, and frailty scores). HRs and 95% CIs were pooled for clinical endpoints, and standardized mean differences (SMDs) were calculated for frailty-specific outcomes.
RESULTS: Thirty unique randomized controlled trials were included with a pooled total of 87,711 participants. Frail patients had a significantly increased risk of MACE (HR: 2.33 [95% CI: 1.87-2.91], P < 0.001, I2 = 83%), all-cause mortality (HR: 2.34 [95% CI: 1.80-3.05], P < 0.01, I2 = 75%), CV mortality (HR: 1.76 [95% CI: 1.60-1.93], P < 0.001, I2 = 0%), and hospitalizations (HR: 2.38 [95% CI: 1.65-3.43], P < 0.001, I2 = 92%) compared to nonfrail patients. In the frailest group, trial interventions decreased MACE (HR: 0.81 [95% CI: 0.74-0.88], P < 0.001, I2 = 0%) and hospitalization (HR: 0.81 [95% CI: 0.72-0.90], P < 0.001, I2 = 0%) risks with no significant difference in mortality risk (P > 0.05) compared with the control group. Trial interventions significantly improved physical (SMD: 0.15, 0.04-0.26) and quality of life (SMD: 0.15, 0.09-0.21) but not frailty scores (P > 0.05).
CONCLUSIONS: While frailty prognosticated a higher risk of CV events and mortality, frailty did not reduce treatment efficacy. CV trial interventions appear beneficial even in the frailest group.
Hypertensive heart disease (HHD) is a major contributor to cardiovascular (CV) morbidity and mortality. Once primarily seen in older adults, recent data suggest a rising burden among younger populations. National Center for Health Statistics mortality data for United States adults aged 15 to 44 from 1999 to 2024 were analyzed. Age-adjusted mortality rates were calculated overall and by demographic subgroup, including sex, race, ethnicity, age group, rural and urban residence, state, and Census region. The proportion of HHD mortality relative to other CV disease (CVD) deaths were examined. Joinpoint regression identified annual percent changes and inflection points. From 1999 to 2024, there were 119,264 HHD-related deaths among young adults. HHD mortality rose from 1.3 (95% CI, 1.23-1.36) to 6.3 (95% CI, 6.12-6.40), with the sharpest increase from 2018 to 2021. Males experienced greater HHD mortality over the study period (increasing from 1.76 to 9.13 deaths per 100,000 person-years) than females (0.76-3.31 deaths per 100,000 person-years). Differences were also noted by race and ethnicity, with non-Hispanic Black individuals experiencing greater HHD mortality that non-Hispanic White and Hispanic individuals. Age-related and geographic differences were also observed. The proportionate HHD mortality increased from 3.8% in 1999 to 16.8% in 2024. Sustained increases in HHD mortality were observed after the COVID-19 pandemic relative to prepandemic levels. HHD-related mortality among young adults in the United States has risen significantly, with differences noted by sex, race and ethnicity, age, rural and urban residence, state, and Census region. The growing share of HHD deaths among CVD deaths in young adults signals its increasing role in premature CVD mortality. In conclusion, these trends underscore the urgent need for early prevention, equitable care, and targeted strategies to reduce HHD in young adults.
The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research and builds upon the original 7-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and interobserver variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.