Truncation Due to Death/Principle Stratification Sample Clauses

Truncation Due to Death/Principle Stratification. The principle stratification approach is commonly applied to compliance issues in random- ized trials [Jin and Xxxxx, 0000, Xxxxxxxxx et al., 2002] but much less often applied to truncation due to death problems. Indeed, although the principle stratification framework was not formalized until 2002 (Xxxxxxxxx and Xxxxx), Imbens and Xxxxx [1997] used prin- ciple stratification-type language in their labeling of complier, never-taker, always taker, and defier groups in their compliance problem. Zhang and Xxxxx first applied the principle stratification framework to the truncation due to death problem [2003], Imai later showed that the bounds derived in the Zhang and Xxxxx paper are sharp bounds [2008], and Zhang, Rubin, and Mealli extended this approach to include Bayesian analyses [2006]. In a journal article summarizing two public lectures, Xxxxx [2006] outlined the principle stratification approach and the kinds of truncation due to death problems to which it is suited. Xxxxxx and Mealli [2007] applied the principle stratification framework to a randomized trial of breast self-examination, with the added complication of noncompliance. Matsuyama and Morita [2006] use principle stratification to estimate the local average treatment effect of chemotherapy in a clinical trial studying non-small-cell lung cancer. MacKenzie et al [2007, 2008] used an implied principle stratification approach to their truncation due to death problem in assessing the impact of trauma-center care on functional outcome, but unfortu- nately insufficient details were provided regarding how the principle strata (and treatment effect) were estimated. The principle stratification approach has also been used to iden- tify surrogate endpoints ([Xxxxxxxxx and Xxxxx, 2002a, Mealli and Xxxxx, 2003, Xxxx and Xxxxxx, 2006] among others). Prior to developing their principle stratification approach, Xxxxxxxxx and Xxxxx [1999] estab- lished problems with standard intention to treat analyses in the presence of noncompliance and resulting missing outcome data. Similarly, Chen, Liu, and Zhang [2005] outline alterna- tive adjustment methods for post-randomization covariates and quantify the potential bias of an estimate using traditional regression adjustment. Kurland, Johnson, and Xxxxx [2007] summarize the different types of research questions that are best suited to unconditional, fully conditional, partly conditional, and joint models of outcome data truncated by death. Xxxxxxx et al mention princ...
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