By Dankmar Bohning, Sasivimol Rattanasiri, Ronny Kuhnert
Delivering trustworthy details on an intervention impact, meta-analysis is a robust statistical software for reading and mixing effects from person reports. Meta-Analysis of Binary info utilizing Profile chance makes a speciality of the research and modeling of a meta-analysis with separately pooled facts (MAIPD). It offers a unifying method of modeling a remedy impression in a meta-analysis of medical trials with binary results.
After illustrating the meta-analytic state of affairs of an MAIPD with a number of examples, the authors introduce the profile probability version and expand it to deal with unobserved heterogeneity. They describe parts of log-linear modeling, methods for locating the profile greatest chance estimator, and substitute ways to the profile probability strategy. The authors additionally talk about tips to version covariate details and unobserved heterogeneity at the same time and use the profile chance option to estimate odds ratios. the ultimate chapters examine quantifying heterogeneity in an MAIPD and convey how meta-analysis should be utilized to the surveillance of scrapie.
Containing new advancements no longer to be had within the present literature, besides easy-to-follow inferences and algorithms, this booklet allows clinicians to successfully research MAIPDs.
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Additional info for Meta-analysis of Binary Data Using Profile Likelihood
Distinct from the comparison of mortality rates between groups of centers is the wide range in mortality by treatment group across the centers. Among 21 centers (the 1st to the 21st center) whose results favored propranolol, mortality rates in patients randomized to propranolol ranged from 0 to 13%, and from 6 to 21% in the patients randomized to receive a placebo. 9 Respiratory tract infections in treatment and control group of 22 trials following Turner et al. (2000) Trial Treatment Control i xTi nTi xC i nC i 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 7 4 20 1 10 2 12 1 1 22 25 31 9 22 0 31 4 31 7 3 14 3 47 38 96 14 48 101 161 28 19 49 162 200 39 193 45 131 75 220 55 91 25 65 25 24 37 11 26 13 38 29 9 44 30 40 10 40 4 60 12 42 26 17 23 6 54 41 95 17 49 84 170 60 20 47 160 185 41 185 46 140 75 225 57 92 23 68 placebo and propranolol in the last 10 centers (the 22nd to the 31st center) favoring placebo.
1 showing clear evidence of homogeneity, making further testing for heterogeneity unnecessary. 6. 6) evidence of heterogeneity. 2) and θˆP M LE cannot be the PNMLE. In fact, it is clear that the PNMLE will have more than one support point. For the general construction of the PNMLE, numerical algorithms are required. 3 The PNMLE via the EM algorithm A major tool for constructing the maximum likelihood estimates is the EM algorithm (Dempster et al. (1977), McLachlan and Krishnan (1997)). 9) where yij = 1, if center i belongs to subpopulation j, and 0 otherwise.
However, for mixture models this just means that we have to face the same problems. Profile likelihood ratios will not have standard χ2 distributions, so that choices for the number of components, solely based upon the likelihood ratio, might be misleading. Then should be accompanied by other selection criteria such as the Bayesian Information Criterion (BIC) which has proved to be a valuable selection criterion in other settings. McLachlan and Peel (2000) discuss and compare various selection criteria.
Meta-analysis of Binary Data Using Profile Likelihood by Dankmar Bohning, Sasivimol Rattanasiri, Ronny Kuhnert