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...

Full description

Saved in:
Bibliographic Details
Published in日本統計学会誌 Vol. 54; no. 2; pp. 73 - 108
Main Authors 武冨, 奈菜美, 今野, 良彦, 江村, 剛志, 渡辺(張), 元宗, 森, 美穂子
Format Journal Article
LanguageJapanese
Published 一般社団法人 日本統計学会 04.03.2025
Subjects
Online AccessGet full text
ISSN0389-5602
2189-1478
DOI10.11329/jjssj.54.73

Cover

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.
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
Author_xml – sequence: 1
  fullname: 武冨, 奈菜美
  organization: 広島大学病院 広島臨床研究開発支援センター
– sequence: 1
  fullname: 今野, 良彦
  organization: 大阪公立大学 大学院理学研究科
– sequence: 1
  fullname: 江村, 剛志
  organization: 久留米大学 バイオ統計センター
– sequence: 1
  fullname: 渡辺(張), 元宗
  organization: 東京経済大学ファイナンスリサーチセンター
– sequence: 1
  fullname: 森, 美穂子
  organization: 久留米大学 医学部 環境医学講座
BookMark eNo9kMtKw0AYhQepYK3d-RqpM5nMTGYjSPEGBV3oOkySGW2oVZJs3DWtWkG0Il0J3hCkLqwLQRAEH2ZIqm9hquLmPx_ng7P4p0GhuduUAMwiWEEIm3wuCKIoqBCrwvAEKJrI5gaymF0ARYhzJhSaU6AcRXUXQsIh4hgXgdCdO93-SI-PsutznQx1cqPbyRg6Xd15z5VuX4z6A50c5CptnejWYW5Ht_3R42sO2dP950Mvez5N317Sq27erIcyllGcnQ3S4eVXtzcDJpVoRLL8lyWwubS4UV0xamvLq9WFmhGYkFCDQZ8RTm1OXIW5gIoLRBE3bUaVBSlzGSFS-K7yPB8iqFyJlJSI-wzakiIXl8D8724QxWJLOnthfUeE-44I47rXkM7PexxiOeb4MPwvvG0ROoHA3yqVgkc
ContentType Journal Article
Copyright 2025 日本統計学会
Copyright_xml – notice: 2025 日本統計学会
DOI 10.11329/jjssj.54.73
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
EISSN 2189-1478
EndPage 108
ExternalDocumentID article_jjssj_54_2_54_73_article_char_ja
GroupedDBID 2WC
3K4
5GY
ABDBF
ACGFO
ACIWK
AEGXH
AIAGR
ALMA_UNASSIGNED_HOLDINGS
E3Z
EBS
EJD
GX1
JSF
JSH
KQ8
OK1
OVT
P2P
RJT
TN5
TR2
XSB
ID FETCH-LOGICAL-j2056-70d7596895bf39a0f9a16192876f4067b755eadbfccd010fbe1fee19d708e61b3
ISSN 0389-5602
IngestDate Wed Sep 03 06:30:47 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly true
Issue 2
Language Japanese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-j2056-70d7596895bf39a0f9a16192876f4067b755eadbfccd010fbe1fee19d708e61b3
OpenAccessLink https://www.jstage.jst.go.jp/article/jjssj/54/2/54_73/_article/-char/ja
PageCount 36
ParticipantIDs jstage_primary_article_jjssj_54_2_54_73_article_char_ja
PublicationCentury 2000
PublicationDate 2025/03/04
PublicationDateYYYYMMDD 2025-03-04
PublicationDate_xml – month: 03
  year: 2025
  text: 2025/03/04
  day: 04
PublicationDecade 2020
PublicationTitle 日本統計学会誌
PublicationTitleAlternate 日本統計学会和文誌
PublicationYear 2025
Publisher 一般社団法人 日本統計学会
Publisher_xml – name: 一般社団法人 日本統計学会
References Borenstein, M., Hedges, L. V., Higgins, J. P. and Rothstein, H. R. (2011). Introduction to Meta-Analysis, John Wiley & Sons: Hoboken, NJ, USA.
Judge, G. G. and Bock, M. E. (1978). The Statistical Implications of Pre-test and Stein-rule Estimators in Econometrics, North Holland, New York.
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.
Yamaguchi, Y. and Maruo, K. (2019). Bivariate beta-binomial model using Gaussian copula for bivariate meta-analysis of two binary outcomes with low incidence, Japanese Journal of Statistics and Data Science, 2(2), 347–373.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning, 2nd edition, Springer New York.
Lehmann, E. L. and Casella, G. (1998). Theory of Point Estimation, 2nd edition, Springer New York.
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.
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.
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.
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.
Efron, B. (2024). Machine learning and the James–Stein estimator, Japanese Journal of Statistics and Data Science, 7, 257–266.
Taketomi, N. and Emura, T. (2023a). Consistency of the estimator for the common mean in fixed-effect meta-analyses, Axioms, 12(5), 503.
Emura, T., Matsumoto, K., Uozumi, R. and Michimae, H. (2024). g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models, Symmetry, 16(2), 223.
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.
Fleiss, J. L. (1993). The statistical basis of meta-analysis, Statistical Methods in Medical Research, 2(2), 121–145.
産業衛生学雑誌 (2007).「許容濃度の暫定値(2007年度) の提案理由(ホルムアルデヒド)」『産業衛生学雑誌』49, 175–181.
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.
Röver, C. and Friede, T. (2020). Dynamically borrowing strength from another study through shrinkage estimation, Statistical Methods in Medical Research, 29(1), 293–308.
Schmid, C. (2001). Using Bayesian inference to perform meta-analysis, Evaluation and the Health Professions, 24(2), 165–189.
Magnus, J. R. (2002). Estimation of the mean of a univariate normal distribution with known variance, Econometrics Journal, 5, 225–236.
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.
Shao, J. (2003). Mathematical Statistics, Springer Science & Business Media, New York, NY, USA.
Magnus, J. R. (2000). The traditional pretest estimator, Theory of Probability and Its Applications, 44(2), 293–308.
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.
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.
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.
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.
Robbins, H. (1964). The empirical Bayes approach to statistical decision problems, The Annals of Mathematical Statistics, 35(1), 1–20.
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.
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.
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.
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.
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.
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). Conditions for posterior contraction in the sparse normal means problem, Electronic Journal of Statistics, 10, 976–1000.
Kaiser, T. and Menkhoff, L. (2020). Financial education in schools: A meta-analysis of experimental studies, Economics of Education Review, 78, 101930.
Casella, G. and Berger, R. L. (2002). Statistical Inference, 2nd edition, Duxbury Press.
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.
Sclove, S. L., Morris, C. and Radhakrishnan, R. (1972). Non-optimality of preliminary-test estimators for the mean of a multivariate normal distribution, The Annals of Mathematical Statistics, 43(5), 1481–1490.
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.
Jayadi, K., Abduh, A. and Basri, M. (2022). A meta-analysis of multicultural education paradigm in Indonesia, Heliyon, 8(1), e08828.
Lehmann, E. L. (2010). Elements of Large-Sample Theory, 2nd edition, Springer Science & Business Media, Berlin, Germany.
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.
Shih, J. H., Konno, Y., Chang, Y. T. and Emura, T. (2019). Estimation of a common mean vector in bivariate meta-analysis under the FGM copula, Statistics, 53(3), 673–695.
Raudenbush, S. W. and Bryk, A. S. (1985). Empirical Bayes meta-analysis, Journal of Educational Statistics, 10(2), 75–98.
Taketomi, N., Konno, Y., Chang, Y. T. and Emura, T. (2021). A meta-analysis for simultaneously estimating individual means with shrinkage, isotonic regression and pretests, Axioms, 10, 267.
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.
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.
森美穂子,星子美智子,原邦夫,石竹達也,嵯峨堅,山木宏一 (2012).「大規模改修による系統解剖学実習室内ホルムアルデヒド濃度および学生の自覚症状の変化」『日本衛生学雑誌』67(4), 501–507.
Röver, C. and Friede, T. (2021). Bounds for the weight of external data in shrinkage estimation, Biometrical Journal, 63(5), 1131–1143.
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). Non-optimality of preliminary-test estimators for the mean of a multivariate normal distribution, The Annals of Mathematical Statistics, 43(5), 1481–1490.
– reference: Borenstein, M., Hedges, L. V., Higgins, J. P. and Rothstein, H. R. (2011). Introduction to Meta-Analysis, John Wiley & Sons: Hoboken, NJ, USA.
– reference: Robbins, H. (1964). The empirical Bayes approach to statistical decision problems, The Annals of Mathematical Statistics, 35(1), 1–20.
– reference: Magnus, J. R. (2002). Estimation of the mean of a univariate normal distribution with known variance, Econometrics Journal, 5, 225–236.
– reference: Yamaguchi, Y. and Maruo, K. (2019). Bivariate beta-binomial model using Gaussian copula for bivariate meta-analysis of two binary outcomes with low incidence, Japanese Journal of Statistics and Data Science, 2(2), 347–373.
– reference: Taketomi, N., Konno, Y., Chang, Y. T. and Emura, T. (2021). 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). The traditional pretest estimator, Theory of Probability and Its Applications, 44(2), 293–308.
– reference: Shih, J. H., Konno, Y., Chang, Y. T. and Emura, T. (2019). Estimation of a common mean vector in bivariate meta-analysis under the FGM copula, Statistics, 53(3), 673–695.
– reference: 森美穂子,星子美智子,原邦夫,石竹達也,嵯峨堅,山木宏一 (2012).「大規模改修による系統解剖学実習室内ホルムアルデヒド濃度および学生の自覚症状の変化」『日本衛生学雑誌』67(4), 501–507.
– reference: Emura, T., Matsumoto, K., Uozumi, R. and Michimae, H. (2024). g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models, Symmetry, 16(2), 223.
– reference: 産業衛生学雑誌 (2007).「許容濃度の暫定値(2007年度) の提案理由(ホルムアルデヒド)」『産業衛生学雑誌』49, 175–181.
SSID ssib005901933
ssib023160829
ssib023160828
ssib000650024
ssib023157179
ssj0033564
ssib000936966
ssib002223900
ssib000936967
Score 2.4163594
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...
SourceID jstage
SourceType Publisher
StartPage 73
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
URI https://www.jstage.jst.go.jp/article/jjssj/54/2/54_73/_article/-char/ja
Volume 54
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
ispartofPNX 日本統計学会誌, 2025/03/04, Vol.54(2), pp.73-108
journalDatabaseRights – providerCode: PRVAFT
  databaseName: Open Access Digital Library
  customDbUrl:
  eissn: 2189-1478
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0033564
  issn: 0389-5602
  databaseCode: KQ8
  dateStart: 19700101
  isFulltext: true
  titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html
  providerName: Colorado Alliance of Research Libraries
– providerCode: PRVAFT
  databaseName: Open Access Digital Library
  customDbUrl:
  eissn: 2189-1478
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0033564
  issn: 0389-5602
  databaseCode: KQ8
  dateStart: 19950101
  isFulltext: true
  titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html
  providerName: Colorado Alliance of Research Libraries
– providerCode: PRVFQY
  databaseName: GFMER Free Medical Journals
  customDbUrl:
  eissn: 2189-1478
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0033564
  issn: 0389-5602
  databaseCode: GX1
  dateStart: 0
  isFulltext: true
  titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php
  providerName: Geneva Foundation for Medical Education and Research
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1Na9RANJR66UX8xG96cE6SmmQyX8dkN0tR_IIWegvJbnLYQ5WyvXjqbtUKohXpSfALQerBehAEQfDHhN3qv_C9yWQ3Kz3UwhJeZt578z6SzHuz82FZVzuc5_Ddd-xMJNT2RcrsVHrKVilPO6liScZw7fCt23xx2b-xwlZmZu_UZi2t99KF9sMD15UcxatQBn7FVbL_4dkxUygAGPwLV_AwXA_lYxJRIikJXA14JGyRiBEpieQk4kRFRDm6yiVBZADVMsjSna6CnzBA2Kgx1IDySCSI8kkgDZX0awwZTpiQoS4BgNU4CxI4SIiAIiGvVXESNElASSRJwIls6ZIWkU1kGAIyRUA1K8GQ6u4ajhT3EFVGWhqGvFRAIoV4cmqeorYCNMw00CBBA-UIgburW5VoLOTQRAkiHzVHVlAVEDkeQK5E5VpTjnTwZGpCbW7AB-lVyTwCwSaEwDIkcixdVBICvkJVUU_kW28oLE3KUXHlVg0Bvgo1PjQkpvClfgAkthwGkDBorAZq6akxOUO_GluJ-lCPx_Rct8lQr5ZZoh8jbZ7SZoGP_NEdoXYZR-8oppED-F07mqlr_RDEtDYExmWnmekyCAyV7frl6UtVR1ruBm4-GF6tVywPqzHxlav38Tig66ZolFa3C1HUAvMXKqKpzdDNqxZrrJj5sYcXQeOqAtcqxl1ImI55gnM80uTmven9Ip3a8QSOPs6S_3NfH9_wqKrtX4kLtdUkP4LciAl38jc33HOn9je0uVdVaEgpM_vZGYtWK3FA9es1xSEa7kJuWM0r1aHu0gnruMlR54NS2ZPWTDc5Zc1hWlru6n7aSorND8Xg1_Dpk9Hbl0V_r-i_KwZ9BDa3is2fUFUMXu3v7Bb9R1A13HhWbDyG2v33O_ufvwMw-vLx96ft0dfnwx_fhm-2oMS816MXu8O913-2ts9Yy61oqbFom8Na7K4HSZQtnI5gikvF0pyqxMlV4uLgDERbOSQNIhWMQa-V5u12x3GdPM3cPMtc1RGOzLib0rPW7Or91eycNQ-Rc9ZmbYd6NPchIUql32E8l20PmNPEP2-J0jzxg3JHnviwj8WFI1NetOYmb-Qla7a3tp5dhnSkl17Rj9hf2gf5hA
linkProvider Colorado Alliance of Research Libraries
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E3%83%A1%E3%82%BF%E5%88%86%E6%9E%90%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E3%83%87%E3%83%BC%E3%82%BF%E3%82%92%E7%94%A8%E3%81%84%E3%81%9F%E5%80%8B%E3%80%85%E3%81%AE%E7%A0%94%E7%A9%B6%E3%81%AE%E6%AD%A3%E8%A6%8F%E6%AF%8D%E5%B9%B3%E5%9D%87%E3%81%AEPretest%E6%8E%A8%E5%AE%9A%E9%87%8F&rft.jtitle=%E6%97%A5%E6%9C%AC%E7%B5%B1%E8%A8%88%E5%AD%A6%E4%BC%9A%E8%AA%8C&rft.au=%E6%AD%A6%E5%86%A8%2C+%E5%A5%88%E8%8F%9C%E7%BE%8E&rft.au=%E4%BB%8A%E9%87%8E%2C+%E8%89%AF%E5%BD%A6&rft.au=%E6%B1%9F%E6%9D%91%2C+%E5%89%9B%E5%BF%97&rft.au=%E6%B8%A1%E8%BE%BA%28%E5%BC%B5%29%2C+%E5%85%83%E5%AE%97&rft.date=2025-03-04&rft.pub=%E4%B8%80%E8%88%AC%E7%A4%BE%E5%9B%A3%E6%B3%95%E4%BA%BA+%E6%97%A5%E6%9C%AC%E7%B5%B1%E8%A8%88%E5%AD%A6%E4%BC%9A&rft.issn=0389-5602&rft.eissn=2189-1478&rft.volume=54&rft.issue=2&rft.spage=73&rft.epage=108&rft_id=info:doi/10.11329%2Fjjssj.54.73&rft.externalDocID=article_jjssj_54_2_54_73_article_char_ja
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0389-5602&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0389-5602&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0389-5602&client=summon