Statistical methodology for ordinal data in meta-analysis
PhD Thesis
Title | Statistical methodology for ordinal data in meta-analysis |
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Type | PhD Thesis |
Authors | |
Author | Hossain, Md. Belal |
Supervisor | Khan, Shahjahan |
Institution of Origin | University of Southern Queensland |
Qualification Name | Doctor of Philosophy |
Number of Pages | 168 |
Year | 2011 |
Abstract | Meta-analysis combines results from several independent studies. Different methods are available to carry out meta-analyses for binary and continuous outcomes. The effect measures used for binary outcomes are odds ratio (OR), relative risk (RR), risk difference (RD), arcsine difference (AS), hazard ratio (HR) etc. For continuous outcomes mean difference (MD) and standardised Among other effect measures for ordinal data there are local and global odds ratios (Dale, 1984), cumulative odds ratios, continuation odds ratio (Agresti, 2010) etc. The local odds ratio measures local association for a The data from studies with several ordered categories are analysed by various methods in meta-analysis. Some methods require specic model assumptions while others collapse the 2 × L (L > 2) contingency table into 2 × 2 tables for measuring the effect size. For example, the proportional Therefore we need a method in meta-analysis that can be used for estimating the effect size without any loss of information by merging categories and is not restricted to any model assumptions. We propose generalised odds ratio (GOR) as an effect measure for ordinal categorical outcomes in meta-analysis (Agresti, 1980). For confidence intervals (CI) of the individual study effects and meta-analysis we employ independent multinomial distribution approach. A general fixed and a random effects models are developed using GOR in meta-analysis for ordinal categorical outcomes. Heterogeneity is one of the most problematic aspects in many metaanalyses. We have demonstrated a method to remedy the problem of heterogeneity in meta-analysis for ordinal data. Following Saleh (2006) a quasiempirical Bayes method (QEBM) is developed using predicted generalised method identifies the extreme studies and improves the meta-analysis in the presence of heterogeneity. Three different meta-analyses on several studies with different degree of heterogeneity are presented. The first example is of individual patients data (IPD) on tacrine trials with Alzheimer's disease, the second example is of misoprostol trials with insignificant heterogeneity and the third example is from simulation studies with significant heterogeneity. The three examples clearly illustrate detailed implementation process and usefulness of the proposed method. We apply and compare GOR with OR as an effect measure for binary outcomes in meta-analysis. Three alternative methods for combining results from binary outcomes are presented for meta-analysis. The first method is a sample size weight method (Edwardes and Baltzan, 2000) for binary outcomes This study also proposes GOR as an effect measure and presents method in meta-analysis for latent continuous outcomes. GOR is simple and it has straightforward interpretation. It can be used for more than two treatment |
Keywords | meta-analysis; statistical methodology; ordinal data |
ANZSRC Field of Research 2020 | 320299. Clinical sciences not elsewhere classified |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/q12vz/statistical-methodology-for-ordinal-data-in-meta-analysis
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