J D Lewsey1, M S Gilthorpe, K Gulabivala. 1. Biostatistics Unit, Eastman Dental Institute for Oral Health Care Sciences, University College London, UK.
Abstract
OBJECTIVE: To introduce the statistical methodology of meta-analysis within the framework of multilevel modelling (MLM) using an illustrative example. BASIC RESEARCH DESIGN: In meta-analysis it is important that the quantitative pooling of study results should be carried out in conjunction with careful consideration of the variation apparent between studies. If statistical heterogeneity is found to be significant, it is due, at least in part, to clinical heterogeneity. It is possible to account for clinical heterogeneity by including covariates that are thought to be responsible, using meta-regression. CLINICAL SETTING: A total of 38 studies of root canal treatment outcome were identified as being suitable for introducing the meta-analysis methodology. Two covariates were considered for modelling: a 'loose' or 'strict' (loose--incomplete radiographic healing; strict--complete radiographic healing) criterion for judging outcome of treatment and the year in which the study was performed. RESULTS: There was considerable statistical heterogeneity between the study results. The effect of employing loose criteria for judging success significantly increased the probability of success when compared to employing strict criteria. Furthermore, the variance between studies was significantly reduced when this covariate was included in the modelling process when compared to the variation estimated in the model which did not consider covariates. CONCLUSION: MLM is a good facilitator for meta-analysis and meta-regression.
OBJECTIVE: To introduce the statistical methodology of meta-analysis within the framework of multilevel modelling (MLM) using an illustrative example. BASIC RESEARCH DESIGN: In meta-analysis it is important that the quantitative pooling of study results should be carried out in conjunction with careful consideration of the variation apparent between studies. If statistical heterogeneity is found to be significant, it is due, at least in part, to clinical heterogeneity. It is possible to account for clinical heterogeneity by including covariates that are thought to be responsible, using meta-regression. CLINICAL SETTING: A total of 38 studies of root canal treatment outcome were identified as being suitable for introducing the meta-analysis methodology. Two covariates were considered for modelling: a 'loose' or 'strict' (loose--incomplete radiographic healing; strict--complete radiographic healing) criterion for judging outcome of treatment and the year in which the study was performed. RESULTS: There was considerable statistical heterogeneity between the study results. The effect of employing loose criteria for judging success significantly increased the probability of success when compared to employing strict criteria. Furthermore, the variance between studies was significantly reduced when this covariate was included in the modelling process when compared to the variation estimated in the model which did not consider covariates. CONCLUSION:MLM is a good facilitator for meta-analysis and meta-regression.
Authors: Raymond Ng; Fiza Singh; Despina A Papamanou; Xiaoqing Song; Chitrang Patel; Colleen Holewa; Niraj Patel; Vanja Klepac-Ceraj; Carla R Fontana; Ralph Kent; Tom C Pagonis; Philip P Stashenko; Nikolaos S Soukos Journal: J Endod Date: 2011-02 Impact factor: 4.171