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Title: Explained variation for survival and recurrent event data
Author: Alotaibi, Refah Mohammed N.
ISNI:       0000 0004 6423 6266
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2014
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Explained variation measures are used to quantify the amount of information in a model and especially how useful the model might be when predicting future observations. Such measures are useful in guiding model choice for all types of predictive regression models, including linear and generalized linear models and survival analysis. The first part of this thesis considers explained variation for survival data and we investigate how individual observations in a data set can influence the value of various proposed statistics. Influence of a subject is a measure of the effect on estimates of deleting him/her from the data set. Influence on regression coefficients has had much attention but there has not been work in influence for explained variation for survival data analysis or other measures of predictive accuracy. Generally in reasonable size data sets the deletion of a single subject has no effect on conclusions. However, examination of distance between measures with and without the subject can be useful in distinguishing abnormal observations. In the second part of the thesis we investigate how measures of explained variation for survival data can be extended to recurrent event data. We describe an existing rank-based measure and we investigate a new statistic based on observed and expected event count processes. Both methods can be used for all models. Adjustments for missing data are proposed for the count measure and demonstrated through simulation to be effective. We compare the population values of the two statistics and illustrate their use in comparing an array of non-nested models for data on recurrent episodes of infant diarrhea. There is evidence that the rank-based method is robust to ignored random effects and also to the presence of unusual observations. The count-based method more directly compares observed and expected intensities. We assess influence of individual observation on these measures.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available