Sharpening Bounds on Principal Effects with Covariates

Long, Dustin; Hudgens, Michael G.

doi:10.1111/biom.12103

Cited by 27 publications

(35 citation statements)

References 20 publications

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“…Grilli and Mealli [2008] use a bounds approach in the context of observational studies. Long and Hudgens [2013] provide some technical conditions under which certain covariates tighten bounds. We extend this work by investigating how an analyst should best select and combine different baseline covariates to tighten bounds in practice.…”

Section: Introductionmentioning

confidence: 99%

Bounding, An Accessible Method for Estimating Principal Causal Effects, Examined and Explained

Miratrix¹,

Furey

Feller

et al. 2017

Journal of Research on Educational Effectiveness

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Estimating treatment effects for subgroups defined by post-treatment behavior (i.e., estimating causal effects in a principal stratification framework) can be technically challenging and heavily reliant on strong assumptions. We investigate an alternative path: using bounds to identify ranges of possible effects that are consistent with the data. This simple approach relies on fewer assumptions and yet can result in policy-relevant findings. As we show, covariates can be used to substantially tighten bounds in a straightforward manner. Via simulation, we demonstrate which types of covariates are maximally beneficial. We conclude with an analysis of a multi-site experimental study of Early College High Schools. When examining the program's impact on students completing the ninth grade "on-track" for college, we find little impact for ECHS students who would otherwise attend a high quality high school, but substantial effects for those who would not. This suggests potential benefit in expanding these programs in areas primarily served by lower quality schools.

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Section: Introductionmentioning

confidence: 99%

Bounding, An Accessible Method for Estimating Principal Causal Effects, Examined and Explained

Miratrix¹,

Furey

Feller

et al. 2017

Journal of Research on Educational Effectiveness

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show abstract

“…While in many applications the measured differences S may have some variability about 0, sometimes this scatter may be assumed to be random measurement error, in which case it is of interest to assess the ‘de-noised’ variable S as a principal surrogate that does have S (0) = 0 for all subjects. Another advantage of this “difference biomarker” scenario is that the baseline biomarker may be predictive of S , which aides identifiability and efficiency of estimation of the CEP surface and mCEP curve via the baseline immunogenicity predictor (BIP) augmented trial design (Follmann, 2006; Gilbert and Hudgens, 2008; Qin et al, 2008; Huang and Gilbert, 2011; Huang, Gilbert, and Wolfson, 2013; Long and Hudgens, 2013, Gabriel and Gilbert, 2014). …”

Section: Principal Surrogate Assessment: Subgroup Effect Modificatimentioning

confidence: 99%

Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition

Gilbert¹,

Gabriel

Huang³

et al. 2015

Journal of Causal Inference

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A common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification framework for addressing this problem based on data from a single randomized clinical efficacy trial, a variety of definitions and criteria for a good surrogate endpoint have been proposed, all based on or closely related to the “principal effects” or “causal effect predictiveness (CEP)” surface. We discuss CEP-based criteria for a useful surrogate endpoint, including (1) the meaning and relative importance of proposed criteria including average causal necessity (ACN), average causal sufficiency (ACS), and large clinical effect modification; (2) the relationship between these criteria and the Prentice definition of a valid surrogate endpoint; and (3) the relationship between these criteria and the consistency criterion (i.e., assurance against the “surrogate paradox”). This includes the result that ACN plus a strong version of ACS generally do not imply the Prentice definition nor the consistency criterion, but they do have these implications in special cases. Moreover, the converse does not hold except in a special case with a binary candidate surrogate. The results highlight that assumptions about the treatment effect on the clinical endpoint before the candidate surrogate is measured are influential for the ability to draw conclusions about the Prentice definition or consistency. In addition, we emphasize that in some scenarios that occur commonly in practice, the principal strata sub-populations for inference are identifiable from the observable data, in which cases the principal stratification framework has relatively high utility for the purpose of effect modification analysis, and is closely connected to the treatment marker selection problem. The results are illustrated with application to a vaccine efficacy trial, where ACN and ACS for an antibody marker are found to be consistent with the data and hence support the Prentice definition and consistency.

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“…Zhang and Rubin (2003) derived large sample bounds on the SACE under ranked average score assumptions, which are the shortest bounds possible without further assumptions. Long and Hudgens (2013) sharpened the bounds by using covariates. Yang and Small (2016) extended the ranked average score assumptions to further utilize survival information at a time point after the measurement of the HRQOL outcome.…”

Section: Introductionmentioning

confidence: 99%

Using Survival Information in Truncation by Death Problems without the Monotonicity Assumption

Yang

Ding²

2018

Biometrics

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In some randomized clinical trials, patients may die before the measurement time point of their outcomes. Even though randomization generates comparable treatment and control groups, the remaining survivors often differ significantly in background variables that are prognostic to the outcomes. This is called the truncation by death problem. Under the potential outcomes framework, the only well-defined causal effect on the outcome is within the subgroup of patients who would always survive under both treatment and control. Because the definition of the subgroup depends on the potential values of the survival status that could not be observed jointly, without making strong parametric assumptions, we cannot identify the causal effect of interest and consequently can only obtain bounds of it. Unfortunately, however, many bounds are too wide to be useful. We propose to use detailed survival information before and after the measurement time point of the outcomes to sharpen the bounds of the subgroup causal effect. Because survival times contain useful information about the final outcome, carefully utilizing them could improve statistical inference without imposing strong parametric assumptions. Moreover, we propose to use a copula model to relax the commonly-invoked but often doubtful monotonicity assumption that the treatment extends the survival time for all patients.

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Sharpening Bounds on Principal Effects with Covariates

Cited by 27 publications

References 20 publications

Bounding, An Accessible Method for Estimating Principal Causal Effects, Examined and Explained

Bounding, An Accessible Method for Estimating Principal Causal Effects, Examined and Explained

Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition

Using Survival Information in Truncation by Death Problems without the Monotonicity Assumption

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