Learning about treatment effects in a new target population under transportability assumptions for relative effect measures.

Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are "transportable" across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease.

Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals.

Importance: Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals. Observations: A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable? Conclusions and Relevance: Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

Using Effect Scores to Characterize Heterogeneity of Treatment Effects.

This JAMA Guide to Statistics and Methods article explains effect score analyses, an approach for evaluating the heterogeneity of treatment effects, and examines its use in a study of oxygen-saturation targets in critically ill patients.

Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467-474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty.

Early Mortality After the First Dose of COVID-19 Vaccination: A Target Trial Emulation.

BACKGROUND: Vaccine hesitancy persists alongside concerns about the safety of coronavirus disease 2019 (COVID-19) vaccines. We aimed to examine the effect of COVID-19 vaccination on risk of death among US veterans. METHODS: We conducted a target trial emulation to estimate and compare risk of death up to 60 days under two COVID-19 vaccination strategies: vaccination within 7 days of enrollment versus no vaccination through follow-up. The study cohort included individuals aged ≥18 years enrolled in the Veterans Health Administration system and eligible to receive a COVID-19 vaccination according to guideline recommendations from 1 March 2021 through 1 July 2021. The outcomes of interest included deaths from any cause and excluding a COVID-19 diagnosis. Observations were cloned to both treatment strategies, censored, and weighted to estimate per-protocol effects. RESULTS: We included 3 158 507 veterans. Under the vaccination strategy, 364 993 received vaccine within 7 days. At 60 days, there were 156 deaths per 100 000 veterans under the vaccination strategy versus 185 deaths under the no vaccination strategy, corresponding to an absolute risk difference of -25.9 (95% confidence limit [CL], -59.5 to 2.7) and relative risk of 0.86 (95% CL, .7 to 1.0). When those with a COVID-19 infection in the first 60 days were censored, the absolute risk difference was -20.6 (95% CL, -53.4 to 16.0) with a relative risk of 0.88 (95% CL, .7 to 1.1). CONCLUSIONS: Vaccination against COVID-19 was associated with a lower but not statistically significantly different risk of death in the first 60 days. These results agree with prior scientific knowledge suggesting vaccination is safe with the potential for substantial health benefits.

Cluster Randomized Trials Designed to Support Generalizable Inferences.

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies, we show that all the estimators have low bias but markedly different precision. Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.

Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Systematically missing data in causally interpretable meta-analysis.

Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.

Evidence Synthesis for Complex Interventions Using Meta-Regression Models.

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

A causal roadmap for generating high-quality real-world evidence.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review.

Importance: Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice. Objective: To assess the reporting of observational studies that explicitly aimed to emulate a target trial. Evidence Review: We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation. Findings: A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation. Conclusion and Relevance: In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.

Development of the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) guideline.

BACKGROUND: Observational studies are increasingly used to inform health decision-making when randomised trials are not feasible, ethical or timely. The target trial approach provides a framework to help minimise common biases in observational studies that aim to estimate the causal effect of interventions. Incomplete reporting of studies using the target trial framework limits the ability for clinicians, researchers, patients and other decision-makers to appraise, synthesise and interpret findings to inform clinical and public health practice and policy. This paper describes the methods that we will use to develop the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) reporting guideline. METHODS/DESIGN: The TARGET reporting guideline will be developed in five stages following recommended guidance. The first stage will identify target trial reporting practices by systematically reviewing published studies that explicitly emulated a target trial. The second stage will identify and refine items to be considered for inclusion in the TARGET guideline by consulting content experts using sequential online surveys. The third stage will prioritise and consolidate key items to be included in the TARGET guideline at an in-person consensus meeting of TARGET investigators. The fourth stage will produce and pilot-test both the TARGET guideline and explanation and elaboration document with relevant stakeholders. The fifth stage will disseminate the TARGET guideline and resources via journals, conferences and courses. ETHICS AND DISSEMINATION: Ethical approval for the survey has been attained (HC220536). The TARGET guideline will be disseminated widely in partnership with stakeholders to maximise adoption and improve reporting of these studies.

Long term risk of death and readmission after hospital admission with covid-19 among older adults: retrospective cohort study.

OBJECTIVES: To characterize the long term risk of death and hospital readmission after an index admission with covid-19 among Medicare fee-for-service beneficiaries, and to compare these outcomes with historical control patients admitted to hospital with influenza. DESIGN: Retrospective cohort study. SETTING: United States. PARTICIPANTS: 883 394 Medicare fee-for-service beneficiaries age ≥65 years discharged alive after an index hospital admission with covid-19 between 1 March 2020 and 31 August 2022, compared with 56 409 historical controls discharged alive after a hospital admission with influenza between 1 March 2018 and 31 August 2019. Weighting methods were used to account for differences in observed characteristics. MAIN OUTCOME MEASURES: All cause death within 180 days of discharge. Secondary outcomes included first all cause readmission and a composite of death or readmission within 180 days. RESULTS: The covid-19 cohort compared with the influenza cohort was younger (77.9 v 78.9 years, standardized mean difference -0.12) and had a lower proportion of women (51.7% v 57.3%, -0.11). Both groups had a similar proportion of black beneficiaries (10.3% v 8.1%, 0.07) and beneficiaries with dual Medicaid-Medicare eligibility status (20.1% v 19.2%; 0.02). The covid-19 cohort had a lower comorbidity burden, including atrial fibrillation (24.3% v 29.5%, -0.12), heart failure (43.4% v 49.9%, -0.13), and chronic obstructive pulmonary disease (39.2% v 52.9%, -0.27). After weighting, the covid-19 cohort had a higher risk (ie, cumulative incidence) of all cause death at 30 days (10.9% v 3.9%; standardized risk difference 7.0%, 95% confidence interval 6.8% to 7.2%), 90 days (15.5% v 7.1%; 8.4%, 8.2% to 8.7%), and 180 days (19.1% v 10.5%; 8.6%, 8.3% to 8.9%) compared with the influenza cohort. The covid-19 cohort also experienced a higher risk of hospital readmission at 30 days (16.0% v 11.2%; 4.9%, 4.6% to 5.1%) and 90 days (24.1% v 21.3%; 2.8%, 2.5% to 3.2%) but a similar risk at 180 days (30.6% v 30.6%;-0.1%, -0.5% to 0.3%). Over the study period, the 30 day risk of death for patients discharged after a covid-19 admission decreased from 17.9% to 7.2%. CONCLUSIONS: Medicare beneficiaries who were discharged alive after a covid-19 hospital admission had a higher post-discharge risk of death compared with historical influenza controls; this difference, however, was concentrated in the early post-discharge period. The risk of death for patients discharged after a covid-19 related hospital admission substantially declined over the course of the pandemic.

Assessing the representativeness of cluster randomized trials: Evidence from two large pragmatic trials in United States nursing homes.

BACKGROUND/AIMS: When the randomized clusters in a cluster randomized trial are selected based on characteristics that influence treatment effectiveness, results from the trial may not be directly applicable to the target population. We used data from two large nursing home-based pragmatic cluster randomized trials to compare nursing home and resident characteristics in randomized facilities to eligible non-randomized and ineligible facilities. METHODS: We linked data from the high-dose influenza vaccine trial and the Music & Memory Pragmatic TRIal for Nursing Home Residents with ALzheimer's Disease (METRICaL) to nursing home assessments and Medicare fee-for-service claims. The target population for the high-dose trial comprised Medicare-certified nursing homes; the target population for the METRICaL trial comprised nursing homes in one of four US-based nursing home chains. We used standardized mean differences to compare facility and individual characteristics across the three groups and logistic regression to model the probability of nursing home trial participation. RESULTS: In the high-dose trial, 4476 (29%) of the 15,502 nursing homes in the target population were eligible for the trial, of which 818 (18%) were randomized. Of the 1,361,122 residents, 91,179 (6.7%) were residents of randomized facilities, 463,703 (34.0%) of eligible non-randomized facilities, and 806,205 (59.3%) of ineligible facilities. In the METRICaL trial, 160 (59%) of the 270 nursing homes in the target population were eligible for the trial, of which 80 (50%) were randomized. Of the 20,262 residents, 973 (34.4%) were residents of randomized facilities, 7431 (36.7%) of eligible non-randomized facilities, and 5858 (28.9%) of ineligible facilities. In the high-dose trial, randomized facilities differed from eligible non-randomized and ineligible facilities by the number of beds (132.5 vs 145.9 and 91.9, respectively), for-profit status (91.8% vs 66.8% and 68.8%), belonging to a nursing home chain (85.8% vs 49.9% and 54.7%), and presence of a special care unit (19.8% vs 25.9% and 14.4%). In the METRICaL trial randomized facilities differed from eligible non-randomized and ineligible facilities by the number of beds (103.7 vs 110.5 and 67.0), resource-poor status (4.6% vs 10.0% and 18.8%), and presence of a special care unit (26.3% vs 33.8% and 10.9%). In both trials, the characteristics of residents in randomized facilities were similar across the three groups. CONCLUSION: In both trials, facility-level characteristics of randomized nursing homes differed considerably from those of eligible non-randomized and ineligible facilities, while there was little difference in resident-level characteristics across the three groups. Investigators should assess the characteristics of clusters that participate in cluster randomized trials, not just the individuals within the clusters, when examining the applicability of trial results beyond participating clusters.

Comparative Effectiveness of Percutaneous Microaxial Left Ventricular Assist Device vs Intra-Aortic Balloon Pump or No Mechanical Circulatory Support in Patients With Cardiogenic Shock.

Importance: Recent studies have produced inconsistent findings regarding the outcomes of the percutaneous microaxial left ventricular assist device (LVAD) during acute myocardial infarction with cardiogenic shock (AMICS). Objective: To compare the percutaneous microaxial LVAD vs alternative treatments among patients presenting with AMICS using observational analyses of administrative data. Design, Setting, and Participants: This comparative effectiveness research study used Medicare fee-for-service claims of patients admitted with AMICS undergoing percutaneous coronary intervention from October 1, 2015, through December 31, 2019. Treatment strategies were compared using (1) inverse probability of treatment weighting to estimate the effect of different baseline treatments in the overall population; (2) instrumental variable analysis to determine the effectiveness of the percutaneous microaxial LVAD among patients whose treatment was influenced by cross-sectional institutional practice patterns; (3) an instrumented difference-in-differences analysis to determine the effectiveness of treatment among patients whose treatment was influenced by longitudinal changes in institutional practice patterns; and (4) a grace period approach to determine the effectiveness of initiating the percutaneous microaxial LVAD within 2 days of percutaneous coronary intervention. Analysis took place between March 2021 and December 2022. Interventions: Percutaneous microaxial LVAD vs alternative treatments (including medical therapy and intra-aortic balloon pump). Main Outcomes and Measures: Thirty-day all-cause mortality and readmissions. Results: Of 23 478 patients, 14 264 (60.8%) were male and the mean (SD) age was 73.9 (9.8) years. In the inverse probability of treatment weighting analysis and grace period approaches, treatment with percutaneous microaxial LVAD was associated with a higher risk-adjusted 30-day mortality (risk difference, 14.9%; 95% CI, 12.9%-17.0%). However, patients receiving the percutaneous microaxial LVAD had a higher frequency of factors associated with severe illness, suggesting possible confounding by measures of illness severity not available in the data. In the instrumental variable analysis, 30-day mortality was also higher with percutaneous microaxial LVAD, but patient and hospital characteristics differed across levels of the instrumental variable, suggesting possible confounding by unmeasured variables (risk difference, 13.5%; 95% CI, 3.9%-23.2%). In the instrumented difference-in-differences analysis, the association between the percutaneous microaxial LVAD and mortality was imprecise, and differences in trends in characteristics between hospitals with different percutaneous microaxial LVAD use suggested potential assumption violations. Conclusions: In observational analyses comparing the percutaneous microaxial LVAD to alternative treatments among patients with AMICS, the percutaneous microaxial LVAD was associated with worse outcomes in some analyses, while in other analyses, the association was too imprecise to draw meaningful conclusions. However, the distribution of patient and institutional characteristics between treatment groups or groups defined by institutional differences in treatment use, including changes in use over time, combined with clinical knowledge of illness severity factors not captured in the data, suggested violations of key assumptions that are needed for valid causal inference with different observational analyses. Randomized clinical trials of mechanical support devices will allow valid comparisons across candidate treatment strategies and help resolve ongoing controversies.

Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring.

The noniterative conditional expectation (NICE) parametric g-formula can be used to estimate the causal effect of sustained treatment strategies. In addition to identifiability conditions, the validity of the NICE parametric g-formula generally requires the correct specification of models for time-varying outcomes, treatments, and confounders at each follow-up time point. An informal approach for evaluating model specification is to compare the observed distributions of the outcome, treatments, and confounders with their parametric g-formula estimates under the "natural course." In the presence of loss to follow-up, however, the observed and natural-course risks can differ even if the identifiability conditions of the parametric g-formula hold and there is no model misspecification. Here, we describe 2 approaches for evaluating model specification when using the parametric g-formula in the presence of censoring: 1) comparing factual risks estimated by the g-formula with nonparametric Kaplan-Meier estimates and 2) comparing natural-course risks estimated by inverse probability weighting with those estimated by the g-formula. We also describe how to correctly compute natural-course estimates of time-varying covariate means when using a computationally efficient g-formula algorithm. We evaluate the proposed methods via simulation and implement them to estimate the effects of dietary interventions in 2 cohort studies.

Generalizability of the Necrotizing Enterocolitis Surgery Trial to the Target Population of Eligible Infants.

OBJECTIVE: The objective of this study was to evaluate whether infants randomized in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network Necrotizing Enterocolitis Surgery Trial differed from eligible infants and whether differences affected the generalizability of trial results. STUDY DESIGN: Secondary analysis of infants enrolled in Necrotizing Enterocolitis Surgery Trial (born 2010-2017, with follow-up through 2019) at 20 US academic medical centers and an observational data set of eligible infants through 2013. Infants born ≤1000 g and diagnosed with necrotizing enterocolitis or spontaneous intestinal perforation requiring surgical intervention at ≤8 weeks were eligible. The target population included trial-eligible infants (randomized and nonrandomized) born during the first half of the study with available detailed preoperative data. Using model-based weighting methods, we estimated the effect of initial laparotomy vs peritoneal drain had the target population been randomized. RESULTS: The trial included 308 randomized infants. The target population included 382 (156 randomized and 226 eligible, non-randomized) infants. Compared with the target population, fewer randomized infants had necrotizing enterocolitis (31% vs 47%) or died before discharge (27% vs 41%). However, incidence of the primary composite outcome, death or neurodevelopmental impairment, was similar (69% vs 72%). Effect estimates for initial laparotomy vs drain weighted to the target population were largely unchanged from the original trial after accounting for preoperative diagnosis of necrotizing enterocolitis (adjusted relative risk [95% CI]: 0.85 [0.71-1.03] in target population vs 0.81 [0.64-1.04] in trial) or spontaneous intestinal perforation (1.02 [0.79-1.30] vs 1.11 [0.95-1.31]). CONCLUSION: Despite differences between randomized and eligible infants, estimated treatment effects in the trial and target population were similar, supporting the generalizability of trial results. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT01029353.