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- Creators: Marine Biological Laboratory Archives
Description
Understanding how adherence affects outcomes is crucial when developing and assigning interventions. However, interventions are often evaluated by conducting randomized experiments and estimating intent-to-treat effects, which ignore actual treatment received. Dose-response effects can supplement intent-to-treat effects when participants are offered the full dose but many only receive a partial dose due to nonadherence. Using these data, we can estimate the magnitude of the treatment effect at different levels of adherence, which serve as a proxy for different levels of treatment. In this dissertation, I conducted Monte Carlo simulations to evaluate when linear dose-response effects can be accurately and precisely estimated in randomized experiments comparing a no-treatment control condition to a treatment condition with partial adherence. Specifically, I evaluated the performance of confounder adjustment and instrumental variable methods when their assumptions were met (Study 1) and when their assumptions were violated (Study 2). In Study 1, the confounder adjustment and instrumental variable methods provided unbiased estimates of the dose-response effect across sample sizes (200, 500, 2,000) and adherence distributions (uniform, right skewed, left skewed). The adherence distribution affected power for the instrumental variable method. In Study 2, the confounder adjustment method provided unbiased or minimally biased estimates of the dose-response effect under no or weak (but not moderate or strong) unobserved confounding. The instrumental variable method provided extremely biased estimates of the dose-response effect under violations of the exclusion restriction (no direct effect of treatment assignment on the outcome), though less severe violations of the exclusion restriction should be investigated.
ContributorsMazza, Gina L (Author) / Grimm, Kevin J. (Thesis advisor) / West, Stephen G. (Thesis advisor) / Mackinnon, David P (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2018
Description
This research explores tests for statistical suppression. Suppression is a statistical phenomenon whereby the magnitude of an effect becomes larger when another variable is added to the regression equation. From a causal perspective, suppression occurs when there is inconsistent mediation or negative confounding. Several different estimators for suppression are evaluated conceptually and in a statistical simulation study where we impose suppression and non-suppression conditions. For each estimator without an existing standard error formula, one was derived in order to conduct significance tests and build confidence intervals. Overall, two of the estimators were biased and had poor coverage, one worked well but had inflated type-I error rates when the population model was complete mediation. As a result of analyzing these three tests, a fourth was considered in the late stages of the project and showed promising results that address concerns of the other tests. When the tests were applied to real data, they gave similar results and were consistent.
ContributorsMuniz, Felix (Author) / Mackinnon, David P (Thesis advisor) / Anderson, Samantha F. (Committee member) / McNeish, Daniel M (Committee member) / Arizona State University (Publisher)
Created2020
ContributorsMarine Biological Laboratory Archives (Publisher) / Arizona Board of Regents (Publisher)
Created1979-09-11 to 1979-09-13