Analysis of Misclassified Categorical Response via Incomplete Surrogate Variables and Likelihood Method

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dc.contributor.advisorShen, Hua
dc.contributor.authorYu, Zheng
dc.contributor.committeememberShen, Hua
dc.contributor.committeememberDe Leon, Alexander R.
dc.contributor.committeememberKopciuk, Karen A.
dc.dateWinter Conferral
dc.date.accessioned2023-02-11T00:32:11Z
dc.date.embargolift2023-02-22
dc.date.issued2020-12-20
dc.description.abstractMisclassification of a dependent categorical variable often occurs in observational studies due to imperfect measuring procedures, and it may result in potential threats to the validity of the analytic results. We first investigate the consequences of naively ignoring the misclassification issue in response variable on parameter estimation using a range of naive methods and ad hoc methods. Then we develop a robust algorithm utilizing the surrogate variables to enable the estimation of the covariate effects in regression models under the framework of latent variable models in the absence of validation data. The resulting estimates are utilized in prediction and estimation of the average treatment effect (ATE). The estimation methods of ATE examined include outcome regression, G-computation, propensity score (PS) stratification, inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW). Variance estimation of ATE is obtained through bootstrap method. Moreover, we extend the algorithm to cope with the complication that some of the surrogate measurements are missing. Simulation studies represent of various scenarios are conducted to assess the performances of the proposed methods with a binary latent response variable. Based on the simulation studies, we show that the proposed method outperforms other approaches and corrects for both problems of misclassification and missingness simultaneously for a binary response variable, ensuring valid statistical inferences. An application to the stimulating study on breast cancer is given for illustration. Discussion and future work are outlined in the end.
dc.identifier.citationYu, Z. (2020). Analysis of Misclassified Categorical Response via Incomplete Surrogate Variables and Likelihood Method (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttp://hdl.handle.net/1880/115857
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/40751
dc.language.isoenen
dc.language.isoEnglish
dc.publisher.facultyGraduate Studiesen
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en
dc.subject.classificationSocial Sciences
dc.titleAnalysis of Misclassified Categorical Response via Incomplete Surrogate Variables and Likelihood Method
dc.typemaster thesis
thesis.degree.disciplineMathematics & Statistics
thesis.degree.grantorUniversity of Calgaryen
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
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