%0 Journal Article %T Minimum MSE Weighted Estimator to Make Inferences for a Common Risk Ratio across Sparse Meta-Analysis Data %A Chukiat Viwatwongkasem %A Sutthisak Srisawad %A Pichitpong Soontornpipit %A Jutatip Sillabutra %A Pratana Satitvipawee %A Prasong Kitidamrongsuk %A Hathaikan Chootrakool %J Open Journal of Statistics %P 49-69 %@ 2161-7198 %D 2022 %I Scientific Research Publishing %R 10.4236/ojs.2022.121004 %X The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problems when the number of events in the experimental or control group is zero in sparse data of a 2 กม 2 table. The adjusted log-risk ratio estimator with the continuity correction points \"\" based upon the minimum Bayes risk with respect to the uniform prior density over (0, 1) and the Euclidean loss function is proposed. Secondly, the interest is to find the optimal weights \"\"of the pooled estimate \"\" that minimize the mean square error (MSE) of \"\" subject to the constraint on \"\" where \"\", \"\", \"\". Finally, the performance of this minimum MSE weighted estimator adjusted with various values of points