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Full-Text Articles in Biostatistics
Addressing Bias In Non-Experimental Studies Assessing Treatment Outcomes In Prostate Cancer, David E. Guy
Addressing Bias In Non-Experimental Studies Assessing Treatment Outcomes In Prostate Cancer, David E. Guy
Electronic Thesis and Dissertation Repository
We evaluated the ability of matching techniques to balance baseline characteristics between treatment groups using non-experimental data. We identified a set of balance diagnostics that assessed key differences in baseline covariates with potential for confounding. These diagnostics were used in a novel systematic approach to developing and evaluating models for use in propensity score matching that optimized balance and data retention. We then compared the performance of propensity score and coarsened exact matching strategies in optimizing balance and data retention, using non-experimental data from a pan-Canadian prostate cancer database. Both matching techniques balanced baseline covariates adequately and retained approximately 70% …
Sample Size Formulas For Estimating Areas Under The Receiver Operating Characteristic Curves With Precision And Assurance, Grace Lu
Electronic Thesis and Dissertation Repository
The area under the receiver operating characteristic curve (AUC) is commonly used to quantify the discriminative ability of tests with ordinal or continuous test data. When planning a study to evaluate a new test, it is important to determine a minimum sample size required to achieve a prespecified precision of estimating AUC. However, conventional sample size formulas do not consider the probability of achieving a prespecified precision, resulting in underestimation of sample sizes. To incorporate the assurance probability, asymptotic sample size formulas were derived using different variance estimators for AUC in this thesis. The precision of AUC estimations was quantified …
Sample Size Formulas For Estimating Risk Ratios With The Modified Poisson Model For Binary Outcomes, Zhenni Xue
Sample Size Formulas For Estimating Risk Ratios With The Modified Poisson Model For Binary Outcomes, Zhenni Xue
Electronic Thesis and Dissertation Repository
Sample size estimation is usually the first step in planning a research study. Too small a study cannot adequately address the objectives, while too large a study may waste resources or unethical. For binary outcomes, several sample size estimation methods are available based on logistic regression models, which focusing on odds ratios. In prospective studies, risk ratios are preferable for ease of interpretation and communication. In this thesis, we compared the power difference between the logistic regression model and the modified Poisson regression model via simulation studies. We then proposed sample size estimation formulas based on the modified Poisson regression …