Extending inferences from a randomized trial to a new target population

Dahabreh, Issa J; Robertson, Sarah E.; Steingrimsson, Jon A.; Stuart, Elizabeth A.; Hernán, Miguel A.

doi:10.48550/arxiv.1805.00550

Cited by 4 publications

(23 citation statements)

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“…A number of recent contributions [1][2][3][4][5][6][7] have discussed methods for addressing problems related to selective study participation [8] in randomized trials. These methods can be used to extend (i.e., generalize or transport [9]) causal inferences from a randomized trial to a target population.…”

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confidence: 99%

“…The methods require baseline covariate, treatment, and outcome data from individuals participating in the trial and baseline covariate data from non-randomized individuals. Estimation of potential outcome means in the target population typically requires models for the probability of trial participation [1], the expectation of the outcome under each treatment among trial participants [5], or both (to improve robustness [3,7]). Prior work has largely focused on identifiability conditions and estimation approaches, without a clear connection to study design principles, obscuring the fact that different study designs determine which causal quantities can be identified and have implications for identifying and estimating the conditional probability of trial participation.…”

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Extending inferences from a randomized trial to a target population

Dahabreh

Hernán

2019

Eur J Epidemiol

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Extending (generalizing or transporting) causal inferences from a randomized trial to a target population requires "generalizability" or "transportability" assumptions, which state that randomized and non-randomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. Instead, our methods directly parameterize violations of the assumptions using bias functions. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of non-randomized individuals, as well as to nested trial designs, where a clinical trial is embedded within a cohort sampled from the target population. We illustrate the methods using data from a clinical trial comparing treatments for chronic hepatitis C infection.

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Extending inferences from a randomized trial to a target population

Dahabreh

Hernán

2019

Eur J Epidemiol

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“…or the outcome m(Z, γ) is correctly specified. Moreover, when both models are correctly specified, it achieves the non-parametric efficiency bound, under the non-parametric model where no assumptions are made about the outcome and selection model beyond the transportability and positivity assumptions (Zhang et al, 2016;Shu and Tan, 2018;Dahabreh et al, 2018).…”

Section: The First Part Ofmentioning

confidence: 96%

“…This means that no patient, based on his/her characteristics Z, is excluded from participating in each ones of the trials. Under these conditions and using that the flexible dosing trial is randomized, an estimator θ2 for θ 2 is obtained by (Zhang et al, 2016;Shu and Tan, 2018;Dahabreh et al, 2018) 1. fitting a parametric model π(Z, γ) for the probability of participation in the flexible dosing trial P (T = 1|X); e.g. , π(X, γ) = logit −1 (γ 0 + γ 1 Z), This semi-parametric estimator is consistent when either the model for the selection π(Z, γ)…”

Section: The First Part Ofmentioning

confidence: 99%

“…In view of this, we will transport data from the low dose arm of the fixed dosing trial to the flexible dosing trial by using similar statistical techniques as for transporting inferences from trial participants to new target populations (Zhang et al, 2016;Dahabreh et al, 2018). We formulate the statistical problem in terms of potential outcomes (Rubin, 1974) and discuss the plausibility of the assumptions required to transport the mean of the potential outcomes under a given treatment regimen from one trial to another.…”

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Efficient, Doubly Robust Estimation of the Effect of Dose Switching for Switchers in a Randomised Clinical Trial

Van Lancker,

Vandebosch,

Vansteelandt

2020

Preprint

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Motivated by a clinical trial conducted by Janssen Pharmaceuticals in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose is unsatisfactory because the latter patients are usually in a better health condition. Because the available information in the considered trial is too scarce to enable a reliable adjustment, we will instead transport data from a fixed dosing trial that has been conducted concurrently on the same target, albeit not in an identical patient population. In particular, we will propose an estimator which relies on an outcome model and a propensity score model for the association between study and patient characteristics. The proposed estimator is asymptotically unbiased if at least one of both models is correctly specified, and efficient (under the model defined by the restrictions on

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A Review of Generalizability and Transportability

Degtiar,

Rose

2021

Preprint

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When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.

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Extending inferences from a randomized trial to a new target population

Cited by 4 publications

References 0 publications

Extending inferences from a randomized trial to a target population

Extending inferences from a randomized trial to a target population

Efficient, Doubly Robust Estimation of the Effect of Dose Switching for Switchers in a Randomised Clinical Trial

A Review of Generalizability and Transportability

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