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. 2009 Apr;172(2):443-465.
doi: 10.1111/j.1467-985X.2009.00585.x.

Analyzing Direct Effects in Randomized Trials with Secondary Interventions: An Application to HIV Prevention Trials

Michael Rosenblum  1 , Nicholas P Jewell, Mark van der Laan, Steve Shiboski, Ariane van der Straten, Nancy Padian
Affiliations

Analyzing Direct Effects in Randomized Trials with Secondary Interventions: An Application to HIV Prevention Trials

Michael Rosenblum et al. J R Stat Soc Ser A Stat Soc. 2009 Apr.

Abstract

The Methods for Improving Reproductive Health in Africa (MIRA) trial is a recently completed randomized trial that investigated the effect of diaphragm and lubricant gel use in reducing HIV infection among susceptible women. 5,045 women were randomly assigned to either the active treatment arm or not. Additionally, all subjects in both arms received intensive condom counselling and provision, the "gold standard" HIV prevention barrier method. There was much lower reported condom use in the intervention arm than in the control arm, making it difficult to answer important public health questions based solely on the intention-to-treat analysis. We adapt an analysis technique from causal inference to estimate the "direct effects" of assignment to the diaphragm arm, adjusting for condom use in an appropriate sense. Issues raised in the MIRA trial apply to other trials of HIV prevention methods, some of which are currently being conducted or designed.

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Figures

Figure 1
Figure 1
Direct Effects Causal Diagram
Figure 2
Figure 2
Direct Effects Causal Diagram with Pre–Treatment Confounder W
Figure 3
Figure 3
Direct Effects Diagram with Confounder as Causal Intermediate
Figure 4
Figure 4
Causal Diagram with Confounder as Causal Intermediate and Unobserved Variable U.
See this image and copyright information in PMC

References

    1. Andersen P, Borgan O, Gill R, Keiding N. Statistical Models Based on Counting Processes. NY, USA: Springer; 1993.
    1. Anderson J, Rietmeijer C, Wilson R. Asking about condom use: is there a standard approach that should be adopted across surveys? Annual meeting of the American Statistical Association, St Louis 1998
    1. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Statist Ass. 1996 June;91(434):444–455.
    1. Bembom O, van der Laan MJ. Data-adaptive selection of the truncation level for inverse-probability-of-treatment-weighted estimators. UC Berkeley Division of Biostatistics Working Paper Series. Working Paper 230. 2008. http://www.bepress.com/ucbbiostat/paper230.
    1. Dawid AP. Causal inference without counterfactuals. J Am Statist Ass. 2000 June;95(450):407–424.

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