A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint - PubMed (original) (raw)

A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint

Corwin M Zigler et al. Biometrics. 2012 Sep.

Abstract

The literature on potential outcomes has shown that traditional methods for characterizing surrogate endpoints in clinical trials based only on observed quantities can fail to capture causal relationships between treatments, surrogates, and outcomes. Building on the potential-outcomes formulation of a principal surrogate, we introduce a Bayesian method to estimate the causal effect predictiveness (CEP) surface and quantify a candidate surrogate's utility for reliably predicting clinical outcomes. In considering the full joint distribution of all potentially observable quantities, our Bayesian approach has the following features. First, our approach illuminates implicit assumptions embedded in previously-used estimation strategies that have been shown to result in poor performance. Second, our approach provides tools for making explicit and scientifically-interpretable assumptions regarding associations about which observed data are not informative. Through simulations based on an HIV vaccine trial, we found that the Bayesian approach can produce estimates of the CEP surface with improved performance compared to previous methods. Third, our approach can extend principal-surrogate estimation beyond the previously considered setting of a vaccine trial where the candidate surrogate is constant in one arm of the study. We illustrate this extension through an application to an AIDS therapy trial where the candidate surrogate varies in both treatment arms.

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Figures

Figure 1

Posterior means (and 95% intervals) for EDE, EAE+, and EAE− from ACTG 320. Horizontal dotted lines are at 0.

Figure 2

Posterior histograms of PAE for different values of sensitivity parameter φ from ACTG 320.

Figure 3

Posterior mean CEP surface calculated with X set to the sample average from ACTG 320 and φ = 0.4. Scatterplot represents values of {S(0), S(1)} from one iteration from the Gibbs sampler, and dashed line is S(0) = S(1). Contours are placed at estimated relative risk of progression to AIDS or death in treatment vs. control arm.

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A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint - PubMed (original) (raw)