Hossain, Md. Belal (2011) Statistical methodology for ordinal data in metaanalysis. [Thesis (PhD/Research)] (Unpublished)

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Abstract
Metaanalysis combines results from several independent studies. Different methods are available to carry out metaanalyses 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 mean difference (SMD) are used in metaanalysis. However, there are many medical and health studies in which the outcome variables are measured on an ordinal categorical scale with more than two categories. These categories are nonnumerically valued, usually levels. In a typical ordinal categorical data there may be L categories C1;C2; : : : ;CL (C1 is the best and CL the worst or vice versa) and J comparison groups G1;G2; : : : ;GJ . Hence the count data for such studies are represented by a J × L contingency table. As a special case when there are two comparison groups in randomised controlled trials (RCTs), we set a 2 × L contingency table. As a result, the ordinary OR, log OR or RR can not be used directly without splitting the 2 × L (L > 2) contingency table into 2 × 2 tables. 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 specific outcome category not for the whole study. The global odds ratio is a measure of ratios of the quadrant probabilities (J = L ≥ 4). Whereas in RCTs there are only two comparison groups (J = 2) namely the treatment and control groups. The cumulative odds ratios provide a comparison of pairs of levels of the explanatory variable with respect to their entire conditional distribution of the dependent variable. As a result, these measures are not appropriate in metaanalysis with RCTs. The data from studies with several ordered categories are analysed by various methods in metaanalysis. 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 odds model (Whitehead et al., 2001) requires a proportionality assumption and there is no well defined variance estimate of the pooled estimator for the sample size weight method (Edwardes and Baltzan, 2000) that uses general odds ratio (ORG) as an effect measure. Therefore we need a method in metaanalysis 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 metaanalysis (Agresti, 1980). For confidence intervals (CI) of the individual study effects and metaanalysis we employ independent multinomial distribution approach. A general fixed and a random effects models are developed using GOR in metaanalysis 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 metaanalysis for ordinal data. Following Saleh (2006) a quasiempirical Bayes method (QEBM) is developed using predicted generalised method identifies the extreme studies and improves the metaanalysis in the presence of heterogeneity. Three different metaanalyses 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 metaanalysis. Three alternative methods for combining results from binary outcomes are presented for metaanalysis. The first method is a sample size weight method (Edwardes and Baltzan, 2000) for binary outcomes using ORG. The other two methods employ GOR as an effect measure for binary outcomes in metaanalysis. We present results by analysing six RCTs from metaanalysis of D1 versus D2 gastrectomy for gastric adenocarcinoma (Memon et al., 2011). This study also proposes GOR as an effect measure and presents method in metaanalysis for latent continuous outcomes. GOR is simple and it has straightforward interpretation. It can be used for more than two treatment groups as well. Hence GOR is a very useful effect measure in metaanalysis not only for multilevel ordinal categorical outcomes but also for binary and latent continuous outcomes.
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Item Type:  Thesis (PhD/Research) 

Item Status:  Live Archive 
Additional Information:  Doctor of Philosophy (PhD) thesis. 
Depositing User:  ePrints Administrator 
Faculty / Department / School:  Historic  Faculty of Sciences  Department of Maths and Computing 
Date Deposited:  13 Feb 2012 05:38 
Last Modified:  03 Jul 2013 01:03 
Uncontrolled Keywords:  metaanalysis; statistical methodology; ordinal data 
Fields of Research (FOR2008):  11 Medical and Health Sciences > 1103 Clinical Sciences > 110399 Clinical Sciences not elsewhere classified 
URI:  http://eprints.usq.edu.au/id/eprint/20764 
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