Improvement and application of dose-response meta-analysis to the relation between glucose and periodontal disease
| Autor: | Tsung-Ying Hsieh, 謝宗穎 |
|---|---|
| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 Background Systematic reviews and meta-analysis are widely used for evidence synthesis. Traditional meta-analysis compare difference in outcomes between two interventions/treatments, but in observational studies, comparisons are sometimes made for groups with different levels of exposure to risk factors. Dose-response meta-analysis transforms the discrete levels of an exposure back to a continuous variable and then estimates a linear or nonlinear relation between the outcome (the response) and continuous exposure (the doses). However, as multiple levels of exposure are usually reported in a study, the outcomes are therefore not independent, and current statistical model for dose-response meta-analysis requires the input of the number of subjects with or without the outcome event at different levels of exposure to calculate the correlations between the outcomes. Objectives The main objective of this dissertation is to develop an alternative approach by imputing missing information for correlations between outcomes. The specific objectives are: 1. Developing generalized least squares (GLS) models for fixed effects dose-response analysis, when the correlations between outcomes of different exposure levels are unknown. 2. Implementing GLS models in R software package for known and unknown correlation structures between the outcomes. 3. Testing the relation between glucose levels and the risk of periodontal diseases by the means of proposed GLS models Source of Data Systematic reviews and data extraction are undertaken for the relation between glucose and periodontal disease. Another dataset from literature on the relation between alcohol and vascular disease data are also used to illustrate the application of the statistical method. Results 1. Generalized least square method and restricted cubic splines model are applied to analyze the relation between glucose and periodontal disease: the increase in glucose level could lead to the increasing of the OR of the periodontal disease. 2. Fixed effects and random effects generalized least square method implemented in R yields the same results obtained by using the software package Stata or SAS. 3. The result obtained by fixing the correlations between log(OR) of the exposure level at 0.5, is very similar to those obtained by the actual number of the cases and controls. Conclusions The generalized least squares method proposed by Greenland and Longnecker requires the input of the correlations of the log(OR) between the exposure level rank when pooling the results, and the restricted cubic splines model proposed by Orsini can efficiently estimate the nonlinear relationship between the dose and response. Researchers can gain a greater sample size using our approach. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|