A PERMUTATION-BASED CORRECTION FOR PEARSON’S CHI-SQUARE TEST ON DATA WITH AN IMPUTED COMPLEX OUTCOME / A MODIFIED EM ALGORITHM FOR CONTINGENCY TABLE ANALYSIS WITH MISSING DATA

Olson Hunt, Megan J. (2022) A PERMUTATION-BASED CORRECTION FOR PEARSON’S CHI-SQUARE TEST ON DATA WITH AN IMPUTED COMPLEX OUTCOME / A MODIFIED EM ALGORITHM FOR CONTINGENCY TABLE ANALYSIS WITH MISSING DATA. Doctoral thesis, UIN SAIZU Purwokerto.

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Abstract

Studies on human subjects often yield missing data, making progress in this field of inherent public health relevance. Here, two statistical methods are proposed for the analysis of discrete data with missing values. First, when one variable is subject to missingness, it was noted the application of Pearson’s chi-square test to singly�imputed data undermines the variability due to imputation, leading to a type-I error rate larger than the nominal level. This research concerns Pearson’s test on data with an imputed complex outcome, where one of its components suffers from missing values. Imputation in this context may be performed either directly through con�ditional imputation of the complex outcome given covariates, or indirectly through conditional imputation of its missing component given the covariates and the other, observed component. Although the latter imputation scheme is shown to be more efficient, an existing adjustment method cannot be extended to this scenario due to the lack of independence amongst the variables constituting the complex out�come. As a result, a novel permutation-based correction method for Pea

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