Statistical modeling strategies for linking multiple molecular sources to time-to-event endpoints ; Statistische Modellbildungsstrategien zur Verknüpfung multipler molekularer Quellen unter Berücksichtigung von Time-to-Event Endpunkten
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Media type:
Doctoral Thesis;
Electronic Thesis;
E-Book
Title:
Statistical modeling strategies for linking multiple molecular sources to time-to-event endpoints ; Statistische Modellbildungsstrategien zur Verknüpfung multipler molekularer Quellen unter Berücksichtigung von Time-to-Event Endpunkten
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Description:
Modern microarray and sequencing technologies allow to measure a large number of patients' characteristics at various molecular levels simultaneously, typically ten thousands to more than one million, e.g. single nucleotide polymorphism (SNP), methylation and gene expression. In this thesis, a sequential complementary strategy as a tool for integrative analysis of two different types of genome-wide data sets is developed. The work is motivated by a study on acute myeloid leukemia (AML) patients as current AML molecular-based classification built on single genomic data is not robust enough to predict the prognosis of AML patients. Therefore, multiple sources of molecular measurements and clinical information have to be integrated into a risk prediction model with respect to clinical endpoints such as time-to-event. The sequential complementary strategy is a stepwise procedure by investigating previous knowledge obtained from one of the molecular data sources to then stabilize the statistical model for the next molecular source. An integrative analysis will be complicated by having only a small overlap in the biological samples, i.e. measurements from all levels are available for a small proportion of patients. To deal with such a situation and to allow the use of all available data including the non-overlapping cases, an imputation approach is developed within the sequential complementary strategy. The approach is based on the imputation of the linear predictor of the model using data from one molecular source alone. To impute the linear predictor values, componentwise likelihood-based boosting and aggregated random forest are evaluated as prediction techniques. The sequential complementary strategy is illustrated in a real application to survival data from AML patients considering methylation and gene expression with almost complete overlap in the biological samples. Unfortunately, such an almost complete overlap in the biological samples does not always have to be present. To provide guidance on how to deal ...