A Dimensional Approach for Multivariate Discrete Time Survival Data

The dimensional approach to data sets aims to provide a more accurate interpretation of the relationships between variables and to reveal the underlying patterns among them. In this thesis, the survival data set, consisting of childhood adversities (CAs) and correlated adult psychiatric disorders, was analyzed using the Logistic Reduced Rank Regression (LRRR) approach. This method allows dimensional investigation of CAs and disorders while considering the correlations between the disorders. The data set was also analyzed using the traditional Logistic Regression (LR) approach, and the results showed that the LRRR outperforms LR by providing more information about the data set. It was observed that parental mental illness (PMI) and physical illness (PIllness) adversities experienced during childhood strongly influence the development of disorders, as evident with both LR and LRRR approaches. However, since LR assumed the same effect of these adversities for each disease, it failed to...Show moreThe dimensional approach to data sets aims to provide a more accurate interpretation of the relationships between variables and to reveal the underlying patterns among them. In this thesis, the survival data set, consisting of childhood adversities (CAs) and correlated adult psychiatric disorders, was analyzed using the Logistic Reduced Rank Regression (LRRR) approach. This method allows dimensional investigation of CAs and disorders while considering the correlations between the disorders. The data set was also analyzed using the traditional Logistic Regression (LR) approach, and the results showed that the LRRR outperforms LR by providing more information about the data set. It was observed that parental mental illness (PMI) and physical illness (PIllness) adversities experienced during childhood strongly influence the development of disorders, as evident with both LR and LRRR approaches. However, since LR assumed the same effect of these adversities for each disease, it failed to identify which disorders were more affected by them. The effects of PMI and PIllness were found to have a greater impact on the development of posttraumatic stress disorder (PTS) when analyzed using the LRRR approach. This dimensional approach, which provides more information on the data set, does have limitations. Biplots, used for dimensional analysis, are easier to interpret in two-dimensional models. However, when dealing with high-dimensional models, constructing the biplots with pairwise dimensions becomes necessary, which in turn makes the simultaneous examination of biplots challenging. Furthermore, the missing data in the data set must follow the assumption of being missing completely at random (MCAR). If the missing data does not meet this assumption, it needs to be addressed through advanced techniques such as multiple imputation. Failure to handle the missing data appropriately may lead to limitations in the effectiveness of this method. In future research, cross-validation can be employed to assess the generalization ability of the model and enhance related analyses.Show less

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A Dimensional Approach for Multivariate Discrete Time Survival Data