The POR model does not assume homogeneity of ORs, but merely specifies a relationship between the ORs of the two tests. We propose a model, the proportional odds ratio (POR) model, which makes no assumptions about the shape of OR p, a baseline function capturing the way OR changes across papers. Also the collection of tests studied may change from one paper to the other, hence incomplete matched groups. Some of the papers may have studied more than one test, hence the results are not independent. Assume there are two or more tests available for the disease, where each test has been studied in one or more papers. British Dental Journal 194:73-78.Consider a meta-analysis where a 'head-to-head' comparison of diagnostic tests for a disease of interest is intended. Part 8: systematic reviews and meta-analyses. Petrie A, Bulman JS, Osborn JF (2003) Further statistics in dentistry.Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses.The Annals of Mathematical Statistics 21:607-11. Freeman MF, Tukey JW (1950) Transformations related to the angular and the square root.DerSimonian R, Laird N (1986) Meta-analysis in clinical trials.Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009) Introduction to meta-analysis.The results of the different studies, with 95% CI, and the pooled proportions with 95% CI are shown in a forest plot: See Meta-analysis: introduction for interpretation of the different publication bias tests. When heterogeneity is present the random effects model should be the preferred model. See Meta-analysis: introduction for interpretation of the heterogeneity statistics Cohran's Q and I 2. with wider confidence interval), but the results from the two models usually agree where there is no heterogeneity. The random effects model will tend to give a more conservative estimate (i.e. The pooled proportion with 95% CI is given both for the Fixed effects model and the Random effects model. The program lists the proportions (expressed as a percentage), with their 95% CI, found in the individual studies included in the meta-analysis. Funnel plot: creates a funnel plot to check for the existence of publication bias.Diamonds for pooled effects: option to represent the pooled effects using a diamond (the location of the diamond represents the estimated effect size and the width of the diamond reflects the precision of the estimate).Plot pooled effect - random effect model: option to include the pooled effect under the random effects model in the forest plot.Plot pooled effect - fixed effects model: option to include the pooled effect under the fixed effects model in the forest plot.You can choose the fixed effect model weights or random effect model weights. Marker size relative to study weight: option to have the size of the markers that represent the effects of the studies vary in size according to the weights assigned to the different studies.Number of positive cases: a variable containing the number of positive cases in the different studies.Ī filter to include only a selected subgroup of studies in the meta-analysis.įilter: a filter to include only a selected subgroup of cases in the graph. Total number of cases: a variable containing the total number of cases in the different studies. Studies: a variable containing an identification of the different studies. The dialog box for "Meta-analysis: proportion" can then be completed as follows: The data of different studies can be entered as follows in the spreadsheet: MedCalc uses a Freeman-Tukey transformation (arcsine square root transformation Freeman and Tukey, 1950) to calculate the weighted summary Proportion under the fixed and random effects model (DerSimonian & Laird, 1986). For a short overview of meta-analysis in MedCalc, see Meta-analysis: introduction.
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