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• W2254197557 abstract "This thesis deals with statistical issues in the analysis of dependent failure time data under complex observation schemes. These observation schemes may yield right-censored, interval-censored and current status data and may also involve response-dependent selection of individuals. The contexts in which these complications arise include family studies, clinical trials, and population studies. Chapter 2 is devoted to the development and study of statistical methods for family studies, motivated by work conducted in the Centre for Prognosis Studies in the Rheumatic Disease at the University of Toronto. Rheumatologists at this centre are interested in studying the nature of within-family dependence in the occurrence of psoriatic arthritis (PsA) to gain insight into the genetic basis for this disease. Families are sampled by selecting members from a clinical registry of PsA patients maintained at the centre and recruiting their respective consenting family members; the member of the registry leading to the sampling of the family is called the proband. Information on the disease onset time for non-probands may be collected by recall or a review of medical records, but some non-probands simply provide their disease status at the time of assessment. As a result family members may provide a combination of observed or right-censored onset times, and current status information. Gaussian copula-based models are studied as a means of flexibly characterizing the within-family association in disease onset times. Likelihood and composite likelihood procedures are also investigated where the latter, like the estimating function approach, reduces the need to specify high-order dependencies and computational burden. Valid analysis of this type of data must address the response-biased sampling scheme which renders at least one affected family member (proband) with a right-truncated onset time. This right-truncation scheme, combined with the low incidence of disease among non-probands, means there is little information about the marginal onset time distribution from the family data alone, so we exploit auxiliary data from an independent sample of independent individuals to enhance the information on the parameters in the marginal age of onset distribution. For composite likelihood approaches, we consider simultaneous and two-stage" @default.
• W2254197557 created "2016-06-24" @default.
• W2254197557 creator A5080765053 @default.
• W2254197557 date "2015-05-14" @default.
• W2254197557 modified "2023-09-24" @default.
• W2254197557 title "Life History Analysis with Response-Dependent Observation" @default.
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