碩士 / 淡江大學 / 統計學系碩士班 / 102 / Case-control studies have been widely applied to investigate rare diseases. When confounding variables are difficult to be quantified, matching designs are used to adjust the confounding effects to reduce the bias in practice. Matched case-control studies often use the logistic regression model to fit the relationship between the risk factors and binary response variable. As a result of highly stratum-effect parameters, Breslow & Day (1980) adopted the conditional approach to eliminate the intercepts. Chen & Wang (2013) proposed a moment-type goodness-of-fit test. This test statistic was constructed based on the discrepancy between the conditional maximum likelihood estimator and nonparametric maximum likelihood estimator of any measurable function. Chen & Wang (2013) set the measurable function as the square of the covariates. This study considers the measurable function to be the polynomial function, indicator function and logarithmic function. Further, the performances of the different testing functions of the moment-type goodness-of-fit test are assessed through simulation studies. Finally, a real dataset is used to illustrate the implement of the proposed method.
| Identifer | oai:union.ndltd.org:TW/102TKU05337003 |
| Date | January 2014 |
| Creators | Hsin-Fang Jiang, 江欣芳 |
| Contributors | 陳麗菁 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 39 |
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