Publications

Classic Hodgkin lymphoma is a highly curable lymphoma that affects primarily younger patients. The therapeutic landscape has evolved and generally consists of varying combinations of chemotherapy and immunotherapy as well as radiation in selected cases. Although most patients are cured of their lymphoma, there is a risk for late treatment-related cardiotoxicity that affects long-term survival and quality of life in this population. Careful consideration of baseline cardiac function and risk factors should be undertaken prior to proceeding with anthracycline-based therapies or thoracic radiation, as adjuvant cardiac-focused efforts may serve to mitigate the risk for cardiovascular dysfunction in this population. This review outlines the evidence supporting current recommendations for assessing baseline cardiotoxicity risk, implementing risk reduction strategies and treatment modifications, the role of multidisciplinary evaluation in high-risk patients, and strategies for long-term cardiac monitoring to minimize treatment-related cardiac morbidity and mortality.

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.

BACKGROUND: In randomized trials, the intention-to-treat effect is the effect of assignment to treatment strategies. The concept of assignment may not be clearly defined when using observational data to emulate a target trial.

AIMS: We aimed to assess the practical implications of using data on prescription versus dispensation as analogues of treatment assignment in observational analyses.

METHODS: We used the primary care-derived Swedish Primary Care Cardiovascular Database of individuals with newly diagnosed hypertension between 2006 and 2014 and linked registers. We compared the effect of two antihypertensive drug classes on the five-year risk of cancer and ischemic heart disease. Treatment assignment was first mapped using prescription data, and then dispensation data. With unique confounding structures, we sequentially adjusted for different classes of risk factor due to uncertainty over the choice of relevant confounders for prescription vs. dispensation.

RESULTS: 7770 individuals were eligible when assignment was defined using prescription compared with 5964 when defined using dispensation. For both cancer and ischemic heart disease outcomes, both higher and lower relative risks of the outcome were consistent with our data. Effect estimates did not vary with the choice of prescription or dispensation data as analogues of assignment, nor with sequential adjustment for class of risk factor.

CONCLUSION: The mapping of prescription or dispensation data to treatment assignment influences the size and characteristics of the study population and the structure of confounding. However, we found no clear numerical differences in effect estimates in this study. Further investigation is required in other settings.

Trial engagement effects are effects of trial participation on the outcome that are not mediated by treatment assignment. Most work on extending (generalizing or transporting) causal inferences from a randomized trial to a target population has, explicitly or implicitly, assumed that trial engagement effects are absent, allowing evidence about the effects of the treatments examined in trials to be applied to nonexperimental settings. Here, we define novel causal estimands and present identification results for generalizability and transportability analyses in the presence of trial engagement effects. Our approach allows for trial engagement effects under assumptions of no causal interaction between trial participation and treatment assignment on the absolute or relative scales. We show that under these assumptions, even in the presence of trial engagement effects, the trial data can be combined with covariate data from the target population to identify average treatment effects in the context of usual care as implemented in the target population (i.e., outside the experimental setting). The identifying observed data functionals under these no-interaction assumptions are the same as those obtained under the stronger identifiability conditions that have been invoked in prior work. Therefore, our results suggest a new interpretation for previously proposed generalizability and transportability estimators. This interpretation may be useful in analyses under causal structures where background knowledge suggests that trial engagement effects are present but interactions between trial participation and treatment are negligible.

BACKGROUND: The appropriate duration of anticoagulation for venous thromboembolism (VTE) in patients who have a transient provoking factor (e.g., surgery, trauma, or immobility) and concomitant enduring risk factors is uncertain.

METHODS: In this single-center, double-blind, randomized trial, adults with VTE after the occurrence of a transient provoking factor who had at least one enduring risk factor and had completed at least 3 months of anticoagulation were assigned to receive oral apixaban (at a dose of 2.5 mg twice daily) or placebo for 12 months. The primary efficacy outcome was the first symptomatic recurrent VTE. The primary safety outcome was the first episode of major bleeding according to the criteria of the International Society on Thrombosis and Hemostasis.

RESULTS: A total of 600 patients underwent randomization (mean age, 59.5 years; female sex, 57.0%; non-White race, 19.2%). The trial population had a broad range of provoking factors and enduring risk factors. Symptomatic recurrent VTE occurred in 4 of the 300 patients (1.3%) in the apixaban group and in 30 of the 300 patients (10.0%) in the placebo group (hazard ratio, 0.13; 95% confidence interval [CI], 0.04 to 0.36; P<0.001). Major bleeding occurred in 1 patient in the apixaban group and none in the placebo group. Clinically relevant nonmajor bleeding was observed in 14 of 294 patients (4.8%) in the apixaban group and in 5 of 294 patients (1.7%) in the placebo group (hazard ratio, 2.68; 95% CI, 0.96 to 7.43; P = 0.06). One patient in the apixaban group and 3 patients in the placebo group died, with no deaths attributed to cardiovascular or hemorrhagic causes. Nonhemorrhagic, nonfatal adverse events occurred in 6 patients (2.0%) in each group.

CONCLUSIONS: Among patients with provoked VTE and enduring risk factors, low-intensity therapy with apixaban for 12 months resulted in a lower risk of symptomatic recurrent VTE than placebo, with a low risk of major bleeding. (Funded by Bristol-Myers Squibb-Pfizer Alliance; HI-PRO ClinicalTrials.gov number, NCT04168203.).

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.