When variables that are treatment effect modifiers also influence the decision to participate in a clinical trial, the average effect among trial participants will differ from the effect in other populations of trial-eligible individuals. In this tutorial, we consider methods for transporting inferences about a time-fixed treatment from trial participants to a new target population of trial-eligible individuals, using data from a completed randomized trial along with baseline covariate data from a sample of nonparticipants. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the finite-sample performance of different methods in a simulation study and provide example code to implement the methods in software. We illustrate the application of the methods to the Coronary Artery Surgery Study, a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. Lastly, we discuss issues that arise when using the methods in applied transportability analyses.

We consider methods for causal inference in randomized trials nested within cohorts of trial‐eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite‐sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial‐eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.

We examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the targetpopulation, and non-nested trial designs, including composite dataset designs, where a randomized trial is combined with a separately obtained sample of non-randomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling non-randomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation.

Summary Background Dehydration due to diarrhoea is a leading cause of child death worldwide, yet no clinical tools for assessing dehydration have been validated in resource-limited settings. The Dehydration: Assessing Kids Accurately (DHAKA) score was derived for assessing dehydration in children with diarrhoea in a low-income country setting. In this study, we aimed to externally validate the DHAKA score in a new population of children and compare its accuracy and reliability to the current Integrated Management of Childhood Illness (IMCI) algorithm. Methods DHAKA was a prospective cohort study done in children younger than 60 months presenting to the International Centre for Diarrhoeal Disease Research, Bangladesh, with acute diarrhoea (defined by WHO as three or more loose stools per day for less than 14 days). Local nurses assessed children and classified their dehydration status using both the DHAKA score and the IMCI algorithm. Serial weights were obtained and dehydration status was established by percentage weight change with rehydration. We did regression analyses to validate the DHAKA score and compared the accuracy and reliability of the DHAKA score and IMCI algorithm with receiver operator characteristic (ROC) curves and the weighted κ statistic. This study was registered with ClinicalTrials.gov, number NCT02007733. Findings Between March 22, 2015, and May 15, 2015, 496 patients were included in our primary analyses. On the basis of our criterion standard, 242 (49%) of 496 children had no dehydration, 184 (37%) of 496 had some dehydration, and 70 (14%) of 496 had severe dehydration. In multivariable regression analyses, each 1-point increase in the DHAKA score predicted an increase of 0·6% in the percentage dehydration of the child and increased the odds of both some and severe dehydration by a factor of 1·4. Both the accuracy and reliability of the DHAKA score were significantly greater than those of the IMCI algorithm. Interpretation The DHAKA score is the first clinical tool for assessing dehydration in children with acute diarrhoea to be externally validated in a low-income country. Further validation studies in a diverse range of settings and paediatric populations are warranted. Funding National Institutes of Health Fogarty International Center.

We take steps toward causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one trial at a time and pooling all trials. We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that the assumptions that allow inferences to be transported from all trials in the collection to the same target population have implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address nonadherence in the trials. Last, we illustrate the proposed methods using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis Trial.

Pediatric diarrheal disease is a significant source of morbidity and mortality in the developing world. While several studies have demonstrated an increased incidence of diarrheal illness in boys compared with girls in low-and middle-income countries (LMIC), the reasons for this difference are unclear. This secondary analysis of the dehydration: assessing kids accurately (DHAKA) derivation and validation studies included children aged <5 years old with acute diarrhea in Dhaka, Bangladesh. The dehydration status was established by percentage weight change with rehydration. Multivariable regression was used to compare percent dehydration, while controlling for differences in age and nutritional status. In this cohort, a total of 1396 children were analyzed; 785 were male (56.2%) and 611 were female (43.8%). Girls presenting with diarrhea were older than boys (median age 17 months vs. 15 months, p = 0.02) and had significantly more malnutrition than boys, even when controlled for age (mean mid-upper arm circumference 134.2 mm vs. 136.4 mm, p < 0.01). The mean percent dehydration did not differ between boys and girls after controlling for age and nutrition status (p = 0.25). Although girls did have higher rates of malnutrition than boys, measures of diarrhea severity were similar between the two groups, arguing against a cultural bias in care-seeking behavior that favors boys.

We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.

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