メタ分析のためのデータを用いた個々の研究の正規母平均のPretest推定量
メタ分析は公表された複数の研究の結果を統合する統計的手法の一つである.メタ分析では各研究の結果が共通平均を持つと仮定し,各研究の結果に基づき共通平均を推定することが多い.しかし各研究が共通平均を持たない場合は,与えられた個々の研究の推定値を更新した値を結果として示すことがある.本稿は,メタ分析の枠組みで個々の研究の正規母平均をPretest推定量に基づいて推定する方法について総説する.Pretest推定量のバイアス,平均二乗誤差と分散に関する新しい内容も与える.また,Rパッケージ:meta.shrinkage の使用方法について述べる.最後に,解剖学実習中における目の症状データへの適用例につい...
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Published in | 日本統計学会誌 Vol. 54; no. 2; pp. 73 - 108 |
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Main Authors | , , , , |
Format | Journal Article |
Language | Japanese |
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一般社団法人 日本統計学会
04.03.2025
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Online Access | Get full text |
ISSN | 0389-5602 2189-1478 |
DOI | 10.11329/jjssj.54.73 |
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Abstract | メタ分析は公表された複数の研究の結果を統合する統計的手法の一つである.メタ分析では各研究の結果が共通平均を持つと仮定し,各研究の結果に基づき共通平均を推定することが多い.しかし各研究が共通平均を持たない場合は,与えられた個々の研究の推定値を更新した値を結果として示すことがある.本稿は,メタ分析の枠組みで個々の研究の正規母平均をPretest推定量に基づいて推定する方法について総説する.Pretest推定量のバイアス,平均二乗誤差と分散に関する新しい内容も与える.また,Rパッケージ:meta.shrinkage の使用方法について述べる.最後に,解剖学実習中における目の症状データへの適用例についても報告する. |
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AbstractList | メタ分析は公表された複数の研究の結果を統合する統計的手法の一つである.メタ分析では各研究の結果が共通平均を持つと仮定し,各研究の結果に基づき共通平均を推定することが多い.しかし各研究が共通平均を持たない場合は,与えられた個々の研究の推定値を更新した値を結果として示すことがある.本稿は,メタ分析の枠組みで個々の研究の正規母平均をPretest推定量に基づいて推定する方法について総説する.Pretest推定量のバイアス,平均二乗誤差と分散に関する新しい内容も与える.また,Rパッケージ:meta.shrinkage の使用方法について述べる.最後に,解剖学実習中における目の症状データへの適用例についても報告する. |
Author | 森, 美穂子 今野, 良彦 江村, 剛志 武冨, 奈菜美 渡辺(張), 元宗 |
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Recommended confidence intervals for two independent binomial proportions, Statistical Methods in Medical Research, 24(2), 224–254. Cargnin, S., Shin, J. I., Genazzani, A. A., Nottegar, A. and Terrazzino, S. (2020). Comparative efficacy and safety of trastuzumab biosimilars to the reference drug: a systematic review and meta-analysis of randomized clinical trials, Cancer Chemotherapy and Pharmacology, 86(5), 577–588. Higgins, J. P., Thompson, S. G. and Spiegelhalter, D. J. (2009). A re-evaluation of random-effects meta-analysis, Journal of the Royal Statistical Society Series A: Statistics in Society, 172(1), 137–159. 梅津佑太 (2023).「周辺回帰モデルにおけるスパース正則化法」『日本統計学会誌』53(1), 91–110. Wei, Y. and Higgins, J. P. (2013). Estimating within-study covariances in multivariate meta-analysis with multiple outcomes, Statistics in Medicine, 32(7), 1191–1205. van der Pas, S., Salomond, J. B. and Schmidt-Hieber, J. (2016). 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References_xml | – reference: Cavalcanti, D. R., Oliveira, T. and de Oliveira Santini, F. (2022). Drivers of digital transformation adoption: A weight and meta-analysis, Heliyon, 8(2), e08911. – reference: Taketomi, N., Chang, Y. T., Konno, Y., Mori, M. and Emura, T. (2024). Confidence interval for normal means in meta-analysis based on a pretest estimator, Japanese Journal of Statistics and Data Science, 7, 537–568. – reference: Casella, G. and Berger, R. L. (2002). Statistical Inference, 2nd edition, Duxbury Press. – reference: Lehmann, E. L. and Casella, G. (1998). Theory of Point Estimation, 2nd edition, Springer New York. – reference: Schmid, C. (2001). Using Bayesian inference to perform meta-analysis, Evaluation and the Health Professions, 24(2), 165–189. – reference: Shih, J. H., Konno, Y., Chang, Y. T. and Emura, T. (2023). A class of general pretest estimators for the univariate normal mean, Communications in Statistics - Theory and Methods, 52(8), 2538–2561. – reference: Shao, J. (2003). Mathematical Statistics, Springer Science & Business Media, New York, NY, USA. – reference: van der Pas, S., Salomond, J. B. and Schmidt-Hieber, J. (2016). Conditions for posterior contraction in the sparse normal means problem, Electronic Journal of Statistics, 10, 976–1000. – reference: Mori, M. and Kakuma, T. (2022). Development of a prediction model for subjective physical symptoms induced during systematic anatomy practice among medical students, The Kurume Medical Journal, 69(3,4), 209–216. – reference: Fagerland, M. W., Lydersen, S. and Laake, P. (2015). Recommended confidence intervals for two independent binomial proportions, Statistical Methods in Medical Research, 24(2), 224–254. – reference: Emura, T., Michimae, H. and Matsui, S. (2022). Dynamic risk prediction via a joint frailty-copula model and IPD meta-analysis: Building web applications, Entropy, 24(5), 589. – reference: Jayadi, K., Abduh, A. and Basri, M. (2022). A meta-analysis of multicultural education paradigm in Indonesia, Heliyon, 8(1), e08828. – reference: Lehmann, E. L. (2010). Elements of Large-Sample Theory, 2nd edition, Springer Science & Business Media, Berlin, Germany. – reference: Khan, S. and Saleh, A. K. M. E. (2001). On the comparison of the pre-test and shrinkage estimators for the univariate normal mean, Statistical Papers, 42, 451–473. – reference: Kontopantelis, E. and Reeves, D. (2012). Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: A simulation study, Statistical Methods in Medical Research, 21(4), 409–426. – reference: Matsunaga, S., Kishi, T. and Iwata, N. (2015). Memantine monotherapy for Alzheimer's disease: a systematic review and meta-analysis, Plos One, 10(4), e0123289. – reference: Taketomi, N., Michimae, H., Chang, Y. T. and Emura, T. (2022). meta.shrinkage: An R package for meta-analyses for simultaneously estimating individual means, Algorithms, 15, 26. – reference: Taketomi, N. and Emura, T. (2023b). meta.shrinkage: Meta-Analyses for Simultaneously Estimating Individual Means, R package version 0.1.4, https://CRAN.R-project.org/package=meta.shrinkage. – reference: White, M. K., Maher, S. M., Rizio, A. A. and Bjorner, J. B. (2018). A meta-analytic review of measurement equivalence study findings of the SF-36® and SF-12® Health Surveys across electronic modes compared to paper administration, Quality of Life Research, 27(7), 1757–1767. – reference: Villatoro-García, J. A., Martorell-Marugán, J., Toro-Domínguez, D., Román-Montoya, Y., Femia, P. and CarmonaSáez, P. (2022). DExMA: An R package for performing gene expression meta-analysis with missing genes, Mathematics, 10(18), 3376. – reference: Taketomi, N. and Emura, T. (2023a). Consistency of the estimator for the common mean in fixed-effect meta-analyses, Axioms, 12(5), 503. – reference: Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning, 2nd edition, Springer New York. – reference: Higgins, J. P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J. and Welch, V. A. (editors) (2023). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023), Cochrane, Available from www.training.cochrane.org/handbook. – reference: Shih, J. H., Konno, Y., Chang, Y. T. and Emura, T. (2022). Copula-based estimation methods for a common mean vector for bivariate meta-analyses, Symmetry, 14(2), 186. – reference: Mu, T. Y., Zhu, Q. Y., Chen, L. S., Dong, D., Xu, J. Y., Xu, R. X. and Shen, C. Z. (2023). Traditional Chinese medicine constitution types of high-normal blood pressure: A meta-analysis, Heliyon, 9(2), e13438. – reference: Oba, K., Paoletti, X., Bang, Y. J., Bleiberg, H., Burzykowski, T., Fuse, N., Michiels, S., Morita, S., Ohashi, Y., Pignon, J. P., Rougier, P., Sakamoto, J., Sargent, D., Sasako, M., Shitara, K., Tsuburaya, A., Van Cutsem, E. and Buyse, M. (2013). Role of chemotherapy for advanced/recurrent gastric cancer: An individual-patient-data meta-analysis, European Journal of Cancer, 49(7), 1565–77. – reference: Röver, C. and Friede, T. (2020). Dynamically borrowing strength from another study through shrinkage estimation, Statistical Methods in Medical Research, 29(1), 293–308. – reference: Muehlhausen, W., Doll, H., Quadri, N., Fordham, B., O'Donohoe, P., Dogar, N. and Wild, D. J. (2015). Equivalence of electronic and paper administration of patient-reported outcome measures: a systematic review and meta-analysis of studies conducted between 2007 and 2013, Health and Quality of Life Outcomes, 13, 167. – reference: Cargnin, S., Shin, J. I., Genazzani, A. A., Nottegar, A. and Terrazzino, S. (2020). Comparative efficacy and safety of trastuzumab biosimilars to the reference drug: a systematic review and meta-analysis of randomized clinical trials, Cancer Chemotherapy and Pharmacology, 86(5), 577–588. – reference: Wei, Y. and Higgins, J. P. (2013). Estimating within-study covariances in multivariate meta-analysis with multiple outcomes, Statistics in Medicine, 32(7), 1191–1205. – reference: Efron, B. (2024). Machine learning and the James–Stein estimator, Japanese Journal of Statistics and Data Science, 7, 257–266. – reference: Higgins, J. P., Thompson, S. G. and Spiegelhalter, D. J. (2009). A re-evaluation of random-effects meta-analysis, Journal of the Royal Statistical Society Series A: Statistics in Society, 172(1), 137–159. – reference: 江村剛志,大庭幸治 (2024).「生存時間変数に対する代替性評価–メタアナリシスアプローチ–」『計量生物学』45(1), 67–85. – reference: Röver, C. and Friede, T. (2023). Using the bayesmeta R package for Bayesian random-effects meta-regression, Computer Methods and Programs in Biomedicine, 229, 107303. – reference: Raudenbush, S. W. and Bryk, A. S. (1985). Empirical Bayes meta-analysis, Journal of Educational Statistics, 10(2), 75–98. – reference: Judge, G. G. and Bock, M. E. (1978). The Statistical Implications of Pre-test and Stein-rule Estimators in Econometrics, North Holland, New York. – reference: Yang, S. P. and Emura, T. (2017). A Bayesian approach with generalized ridge estimation for high-dimensional regression and testing, Communications in Statistics - Simulation and Computation, 46(8), 6083–6105. – reference: Sclove, S. L., Morris, C. and Radhakrishnan, R. (1972). 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A meta-analysis for simultaneously estimating individual means with shrinkage, isotonic regression and pretests, Axioms, 10, 267. – reference: Kaiser, T. and Menkhoff, L. (2020). Financial education in schools: A meta-analysis of experimental studies, Economics of Education Review, 78, 101930. – reference: Fleiss, J. L. (1993). The statistical basis of meta-analysis, Statistical Methods in Medical Research, 2(2), 121–145. – reference: Röver, C. and Friede, T. (2021). Bounds for the weight of external data in shrinkage estimation, Biometrical Journal, 63(5), 1131–1143. – reference: Pagliaro, L., D'Amico, G., Sörensen, T. I., Lebrec, D., Burroughs, A. K., Morabito, A., Tiné, F., Politi, F. and Traina, M. (1992). Prevention of first bleeding in cirrhosis. A meta-analysis of randomized trials of nonsurgical treatment, Annals of Internal Medicine, 117(1), 59–70. – reference: 梅津佑太 (2023).「周辺回帰モデルにおけるスパース正則化法」『日本統計学会誌』53(1), 91–110. – reference: Magnus, J. R. (2000). 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Snippet | メタ分析は公表された複数の研究の結果を統合する統計的手法の一つである.メタ分析では各研究の結果が共通平均を持つと仮定し,各研究の結果に基づき共通平均を推... |
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SubjectTerms | General Pretest推定量 Pretest推定量 メタ分析 正規分布 縮小推定量 |
Title | メタ分析のためのデータを用いた個々の研究の正規母平均のPretest推定量 |
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