Principal Stratification and Attribution Prohibition: Good Ideas Taken Too Far

Joffe, Marshall M.

doi:10.2202/1557-4679.1367

Cited by 41 publications

(58 citation statements)

References 42 publications

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“…A primary goal in these settings is often to better understand a treatment’s effect by drawing inference about effects within subgroups defined by the principal strata. The principal stratification approach is less helpful for decision making, as principal stratum membership is generally not identifiable prior to treatment (Joffe 2011). …”

Section: Introductionmentioning

confidence: 99%

Sharpening Bounds on Principal Effects with Covariates

Long

Hudgens

2013

Biometrics

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Summary Estimation of treatment effects in randomized studies is often hampered by possible selection bias induced by conditioning on or adjusting for a variable measured post-randomization. One approach to obviate such selection bias is to consider inference about treatment effects within principal strata, i.e., principal effects. A challenge with this approach is that without strong assumptions principal effects are not identifiable from the observable data. In settings where such assumptions are dubious, identifiable large sample bounds may be the preferred target of inference. In practice these bounds may be wide and not particularly informative. In this work we consider whether bounds on principal effects can be improved by adjusting for a categorical baseline covariate. Adjusted bounds are considered which are shown to never be wider than the unadjusted bounds. Necessary and sufficient conditions are given for which the adjusted bounds will be sharper (i.e., narrower) than the unadjusted bounds. The methods are illustrated using data from a recent, large study of interventions to prevent mother-to-child transmission of HIV through breastfeeding. Using a baseline covariate indicating low birth weight, the estimated adjusted bounds for the principal effect of interest are 63% narrower than the estimated unadjusted bounds.

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Section: Introductionmentioning

confidence: 99%

Sharpening Bounds on Principal Effects with Covariates

Long

Hudgens

2013

Biometrics

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“…) on P 0 represents a comparison of the marginal distributions of Y for the treatment and control groups on equivalent proportions of subjects who survive beyond the same percentile in the survival distributions on their assigned treatments [24]. In this case, ê(BSACE) is consistent for the target estimand E (Y Second, a limitation of the approach in this manuscript is that nonparametric estimates of h(J) may be unstable unless the follow-up time for survival substantially exceeds the follow-up time J for the longitudinal endpoint for a sufficient number of patients.…”

Section: Discussionmentioning

confidence: 99%

The Balanced Survivor Average Causal Effect

Greene

Joffe

et al. 2013

The International Journal of Biostatistics

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Statistical analysis of longitudinal outcomes is often complicated by the absence of observable values in patients who die prior to their scheduled measurement. In such cases, the longitudinal data are said to be "truncated by death" to emphasize that the longitudinal measurements are not simply missing, but are undefined after death. Recently, the truncation by death problem has been investigated using the framework of principal stratification to define the target estimand as the survivor average causal effect (SACE), which in the context of a two-group randomized clinical trial is the mean difference in the longitudinal outcome between the treatment and control groups for the principal stratum of always-survivors. The SACE is not identified without untestable assumptions. These assumptions have often been formulated in terms of a monotonicity constraint requiring that the treatment does not reduce survival in any patient, in conjunction with assumed values for mean differences in the longitudinal outcome between certain principal strata. In this paper, we introduce an alternative estimand, the balanced-SACE, which is defined as the average causal effect on the longitudinal outcome in a particular subset of the always-survivors that is balanced with respect to the potential survival times under the treatment and control. We propose a simple estimator of the balanced-SACE that compares the longitudinal outcomes between equivalent fractions of the longest surviving patients between the treatment and control groups and does not require a monotonicity assumption. We provide expressions for the large sample bias of the estimator, along with sensitivity analyses and strategies to minimize this bias. We consider statistical inference under a bootstrap resampling procedure.

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“…For example, once efficacy is proved, a larger subpopulation of people may be willing to take the treatment. Effects defined on the subpopulation of compliers are also of limited decision-making utility because individual principal stratum membership is generally unknown prior to treatment assignment (Joffe, 2011). …”

Section: Randomized Studies With Partial Compliancementioning

confidence: 99%

Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

Richardson¹,

Hudgens²,

Gilbert³

et al. 2014

Statist. Sci.

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This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.

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Principal Stratification and Attribution Prohibition: Good Ideas Taken Too Far

Cited by 41 publications

References 42 publications

Sharpening Bounds on Principal Effects with Covariates

Sharpening Bounds on Principal Effects with Covariates

The Balanced Survivor Average Causal Effect

Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

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