メンデルランダム化研究の統計手法
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| Published in | 計量生物学 Vol. 46; no. 1; pp. 1 - 19 |
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| Main Authors | , |
| Format | Journal Article |
| Language | Japanese |
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日本計量生物学会
30.05.2025
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| ISSN | 0918-4430 2185-6494 |
| DOI | 10.5691/jjb.46.1 |
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| Author | 折原, 隼一郎 比良野, 圭太 |
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| Author_xml | – sequence: 1 fullname: 折原, 隼一郎 organization: 東京医科大学 医療データサイエンス分野 – sequence: 1 fullname: 比良野, 圭太 organization: 京都大学大学院 医学研究科 人間健康科学系専攻 |
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| Copyright | 2025 日本計量生物学会 |
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| DOI | 10.5691/jjb.46.1 |
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| Discipline | Biology |
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| EndPage | 19 |
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| References | Burgess, S. and Thompson, S. G. (2011). Bias in causal estimates from Mendelian randomization studies with weak instruments. Statistics in Medicine, 30, 1312-1323. Terza, J. V., Basu, A. and Rathouz, P. J. (2008). Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. Journal of Health Economic, 27, 531-543. Arya, V., Banerjee, R., Lowies, B., Viljoen, C. and Lushington, K. (2023). The effect of psychological factors on financial behaviour among older Australians: Evidence from the early stages of COVID-19 pandemic. PLoS One, 18, e0286733. Baiocchi, M., Cheng, J. and Small, D. S. (2014). Instrumental variable methods for causal inference. Statistics in Medicine, 33, 2297-2340. Anderson, T. W., Kunitomo, N. and Matsushita, Y. (2010). On the asymptotic optimality of the LIML estimator with possibly many instruments. Journal of Econometrics, 157, 191-204. Martinussen, T., Vansteelandt, S., Tchetgen Tchetgen, E. J. and Zucker, D. M. (2017). Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. Biometrics, 73(4), 1140-1149. Orihara, S., Goto, A. and Taguri, M. (2024b). Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator. Biometrical Journal, 66(4), 2300113. Orihara, S., Sugasawa, S., Ohigashi, T., Nakagawa, T. and Taguri, M. (2024c). Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models. Available at: https://arxiv.org/abs/2312.02404. Wade, K. H., Carslake, D., Sattar, N., Davey Smith, G. and Timpson, N. J. (2018). BMI and mortality in UK Biobank: revised estimates using Mendelian randomization. Obesity, 26, 1796-1806. Wang, L., Tchetgen Tchetgen, E., Martinussen, T. and Vansteelandt, S. (2023). Instrumental variable estimation of the causal hazard ratio. Biometrics, 79, 539-550. Brookhart, M. A. and Schneeweiss, S. (2007). Preference-based instrumental variable methods for the estimation of treatment effects: assessing validity and interpreting results. The International Journal of Biostatistics, 3. Sun, B., Liu, Z. and Tchetgen Tchetgen, E. J. (2023). Semiparametric efficient G-estimation with invalid instrumental variables. Biometrika, 110, 953-971. Wooldridge, J. M. (2014). Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables. Journal of Econometrics, 182, 226-234. 稲垣宣生(2018). 数理統計学．裳華房 National Human Genome Research Institute. GWAS Catalog. https://www.ebi.ac.uk/gwas/home. （2024年5月2日閲覧） Schuster, N. A., Twisk, J. W., Ter Riet, G., Heymans, M. W. and Rijnhart, J. J. (2021). Noncollapsibility and its role in quantifying confounding bias in logistic regression. BMC Medical Research Methodology, 21, 1-9. Swanson, S. and Hernán, M. A. (2013). Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology, 24, 370-374. Kanai, M., Tanaka, T. and Okada, Y. (2016). Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set. Journal of Human Genetics, 61(10), 861-866. Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R. et al. (2022). Mendelian randomization. Nature Reviews Methods Primers, 2, 6. Guo, Z., Kang, H., Tony Cai, T. and Small, D. S. (2018). Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting. Journal of the Royal Statistical Society Series B: Statistical Methodology, 80, 793-815. Au Yeung, S. L., Jiang, C., Cheng, K. K., Liu, B., Zhang, W., Lam, T. H. et al. (2013). Is aldehyde dehydrogenase 2 a credible genetic instrument for alcohol use in Mendelian randomization analysis in Southern Chinese men?. International Journal of Epidemiology, 42(1), 318-328. Hernán, M. A. and Robins, J. M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. Bowden, J., Davey Smith, G., Haycock, P. C. and Burgess, S. (2016). Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 40, 304-314. Kang, H., Zhang, A., Cai, T. T. and Small, D. S. (2016). Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization. Journal of the American statistical Association, 111, 132-144. Tchetgen Tchetgen, E. J., Walter, S., Vansteelandt, S., Martinussen, T. and Glymour, M. (2015). Instrumental variable estimation in a survival context. Epidemiology, 26(3), 402-410. Jenkins, D. A., Wade, K. H., Carslake, D., Bowden, J., Sattar, N., Loos, R. J. et al. (2021). Estimating the causal effect of BMI on mortality risk in people with heart disease, diabetes and cancer using Mendelian randomization. International Journal of Cardiology, 330, 214-220. Kianian, B., Kim, J. I., Fine, J. P. and Peng, L. (2021). Causal proportional hazards estimation with a binary instrumental variable. Statistica Sinica, 31, 673. Burgess, S. and Thompson, S. G. (2013). Use of allele scores as instrumental variables for Mendelian randomization. International Journal of Epidemiology, 42, 1134-1144. Medical Research, 26, 2333-2355. Zhang, D., Yang, M., Zhou, D., Li, Z., Cai, L., Bao, Y. et al. (2018). The polymorphism rs671 at ALDH2 associated with serum uric acid levels in Chinese Han males: A genome-wide association study. Gene, 651, 62-69. Hartwig, F. P., Davey Smith, G. and Bowden, J. (2017). Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International Journal of Epidemiology, 46, 1985-1998. Locke, A. E., Kahali, B., Berndt, S. I., Justice, A. E., Pers, T. H., Day, F. R. et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518, 197-206. Rees, J. M., Wood, A. M., Dudbridge, F. and Burgess, S. (2019). Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates. PloS One, 14, e0222362. Li, P., Wang, H., Guo, L., Gou, X., Chen, G., Lin, D. et al. (2022). Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Medicine, 20, 443. Burgess, S. and Thompson, S. G. (2015). Mendelian randomization: methods for using genetic variants in causal estimation. CRC Press. Wan, F., Small, D. and Mitra, N. (2018). A general approach to evaluating the bias of 2-stage instrumental variable estimators. Statistics in Medicine, 37, 1997-2015. Grover, S., Del Greco M, F., Stein, C. M. and Ziegler, A. (2017). Mendelian randomization. Statistical Human Genetics: Methods and Protocols, 581-628. Bowden, J., Davey Smith, G. and Burgess, S. (2015). Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 44, 512-525. Cui, Y., Michael, H., Tanser, F. and Tchetgen Tchetgen, E. (2023). Instrumental variable estimation of the marginal structural Cox model for time-varying treatments. Biometrika, 110, 101-118. Levis, A. W., Kennedy, E. H. and Keele, L. (2024). Nonparametric identification and efficient estimation of causal effects with instrumental variables. Available at: https://arxiv.org/pdf/2402.09332. Angrist, J. D., Imbens, G. W. and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American statistical Association, 91, 444-455. 岡田随象(2020). ゼロから実践する 遺伝統計学セミナー 疾患とゲノムを結びつける．羊土社 Palmer, T. M., Sterne, J. A., Harbord, R. M., Lawlor, D. A., Sheehan, N. A., Meng, S. et al. (2011). Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. American Journal of Epidemiology, 173, 1392-1403. Hernán, M. A. and Robins, J. M. (2006). Instruments for causal inference: an epidemiologist's dream?. Epidemiology, 17, 360-372. Kelly, T. N., Takeuchi, F., Tabara, Y., Edwards, T. L., Kim, Y. J., Chen, P. et al. (2013). Genome-wide association study meta-analysis reveals transethnic replication of mean arterial and pulse pressure loci. Hypertension, 62(5), 853-859. Basu, A., Coe, N., Chapman, C. G. (2017). Comparing 2SLS vs 2SRI for binary outcomes and binary exposures. National Bureau of Economic Research. doi: 10.3386/w23840. Karthaus, E. G., Lijftogt, N., Vahl, A., Van Der Willik, E. M., Amodio, S. , Van Zwet, E. W. et al. (2020). Patients with a ruptured abdominal aortic aneurysm are better informed in hospitals with an “EVAR-preferred” strategy: an instrumental variable analysis of the Dutch surgical aneurysm audit. Annals of Vascular Surgery, 69, 332-344. Orihara, S., Goto, A. and Taguri, M. (2023). Instrumental variable estimation of causal effects with applying some model selection procedures under binary outcomes. Behaviormetrika, 50, 241-262. Orihara, S., Fukuma, S., Ikenoue, T. and Taguri, M. (2024a). Likelihood-based instrumental variable methods for Cox proportional hazard models. Japanese Journal of Statistics and Data Science, accepted. Burgess, S., Small, D. S. and Thompson, S. G. (2017). A review of instrumental variable estimators for Mendelian randomization. Statistical Methods in Medical Research, 26(5), 2333–2355. Martínez-Camblor, P., Mackenzie, T., Staiger, D. O., Goodney, P. P. and O’Malley, A. J. (2019). Adjusting for bias introduced by instrumental variable estimation in the Cox proportional hazards model. Biostatistics, 20, 80-96. Martinussen, T., Nørbo Sørensen, D. and Vansteelandt, S. (2019). Instrumental variables estimation under a structural Cox model. Biostatistics, 20(1), 65-79. Li, J., Bai, H., Qiao, H., Du, C., Yao, P., Zhang, Y. et al. (2023). Causal effects of COVID-19 on cancer risk: A Mendelian randomization study. Journal of Medical Virology, 95, e28722. Davies, N. M., Holmes, M. V. and Smith, G. D. (2018). Reading Mendelian randomisation studies: a guide, glossary and checklist for clinicians. Bmj, 362, k601. 高橋将宜(2022). 統計的因果推論の理論と実装：潜在的結果変数と欠測データ．共立出版 Orihara, S. and Goto, A. (2024). Com |
| References_xml | – reference: Jenkins, D. A., Wade, K. H., Carslake, D., Bowden, J., Sattar, N., Loos, R. J. et al. (2021). Estimating the causal effect of BMI on mortality risk in people with heart disease, diabetes and cancer using Mendelian randomization. International Journal of Cardiology, 330, 214-220. – reference: Davies, N. M., Holmes, M. V. and Smith, G. D. (2018). Reading Mendelian randomisation studies: a guide, glossary and checklist for clinicians. Bmj, 362, k601. – reference: Martinussen, T., Vansteelandt, S., Tchetgen Tchetgen, E. J. and Zucker, D. M. (2017). Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. Biometrics, 73(4), 1140-1149. – reference: Terza, J. V., Basu, A. and Rathouz, P. J. (2008). Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. Journal of Health Economic, 27, 531-543. – reference: 高橋将宜(2022). 統計的因果推論の理論と実装：潜在的結果変数と欠測データ．共立出版． – reference: Baiocchi, M., Cheng, J. and Small, D. S. (2014). Instrumental variable methods for causal inference. Statistics in Medicine, 33, 2297-2340. – reference: Tchetgen Tchetgen, E. J., Walter, S., Vansteelandt, S., Martinussen, T. and Glymour, M. (2015). Instrumental variable estimation in a survival context. Epidemiology, 26(3), 402-410. – reference: Au Yeung, S. L., Jiang, C., Cheng, K. K., Liu, B., Zhang, W., Lam, T. H. et al. (2013). Is aldehyde dehydrogenase 2 a credible genetic instrument for alcohol use in Mendelian randomization analysis in Southern Chinese men?. International Journal of Epidemiology, 42(1), 318-328. – reference: Kanai, M., Tanaka, T. and Okada, Y. (2016). Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set. Journal of Human Genetics, 61(10), 861-866. – reference: 稲垣宣生(2018). 数理統計学．裳華房． – reference: Wan, F., Small, D. and Mitra, N. (2018). A general approach to evaluating the bias of 2-stage instrumental variable estimators. Statistics in Medicine, 37, 1997-2015. – reference: Cui, Y., Michael, H., Tanser, F. and Tchetgen Tchetgen, E. (2023). Instrumental variable estimation of the marginal structural Cox model for time-varying treatments. Biometrika, 110, 101-118. – reference: Kelly, T. N., Takeuchi, F., Tabara, Y., Edwards, T. L., Kim, Y. J., Chen, P. et al. (2013). Genome-wide association study meta-analysis reveals transethnic replication of mean arterial and pulse pressure loci. Hypertension, 62(5), 853-859. – reference: Angrist, J. D., Imbens, G. W. and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American statistical Association, 91, 444-455. – reference: Burgess, S., Small, D. S. and Thompson, S. G. (2017). A review of instrumental variable estimators for Mendelian randomization. Statistical Methods in Medical Research, 26(5), 2333–2355. – reference: Sun, B., Liu, Z. and Tchetgen Tchetgen, E. J. (2023). Semiparametric efficient G-estimation with invalid instrumental variables. Biometrika, 110, 953-971. – reference: Wooldridge, J. M. (2014). Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables. Journal of Econometrics, 182, 226-234. – reference: Kianian, B., Kim, J. I., Fine, J. P. and Peng, L. (2021). Causal proportional hazards estimation with a binary instrumental variable. Statistica Sinica, 31, 673. – reference: Anderson, T. W., Kunitomo, N. and Matsushita, Y. (2010). On the asymptotic optimality of the LIML estimator with possibly many instruments. Journal of Econometrics, 157, 191-204. – reference: National Human Genome Research Institute. GWAS Catalog. https://www.ebi.ac.uk/gwas/home. （2024年5月2日閲覧） – reference: Li, J., Bai, H., Qiao, H., Du, C., Yao, P., Zhang, Y. et al. (2023). Causal effects of COVID-19 on cancer risk: A Mendelian randomization study. Journal of Medical Virology, 95, e28722. – reference: Martinussen, T., Nørbo Sørensen, D. and Vansteelandt, S. (2019). Instrumental variables estimation under a structural Cox model. Biostatistics, 20(1), 65-79. – reference: Palmer, T. M., Sterne, J. A., Harbord, R. M., Lawlor, D. A., Sheehan, N. A., Meng, S. et al. (2011). Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. American Journal of Epidemiology, 173, 1392-1403. – reference: Zhang, D., Yang, M., Zhou, D., Li, Z., Cai, L., Bao, Y. et al. (2018). The polymorphism rs671 at ALDH2 associated with serum uric acid levels in Chinese Han males: A genome-wide association study. Gene, 651, 62-69. – reference: Bowden, J., Davey Smith, G. and Burgess, S. (2015). Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 44, 512-525. – reference: Wade, K. H., Carslake, D., Sattar, N., Davey Smith, G. and Timpson, N. J. (2018). BMI and mortality in UK Biobank: revised estimates using Mendelian randomization. Obesity, 26, 1796-1806. – reference: Bowden, J., Davey Smith, G., Haycock, P. C. and Burgess, S. (2016). Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 40, 304-314. – reference: Hernán, M. A. and Robins, J. M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. – reference: Burgess, S. and Thompson, S. G. (2011). Bias in causal estimates from Mendelian randomization studies with weak instruments. Statistics in Medicine, 30, 1312-1323. – reference: Karthaus, E. G., Lijftogt, N., Vahl, A., Van Der Willik, E. M., Amodio, S. , Van Zwet, E. W. et al. (2020). Patients with a ruptured abdominal aortic aneurysm are better informed in hospitals with an “EVAR-preferred” strategy: an instrumental variable analysis of the Dutch surgical aneurysm audit. Annals of Vascular Surgery, 69, 332-344. – reference: Hartwig, F. P., Davey Smith, G. and Bowden, J. (2017). Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International Journal of Epidemiology, 46, 1985-1998. – reference: Arya, V., Banerjee, R., Lowies, B., Viljoen, C. and Lushington, K. (2023). The effect of psychological factors on financial behaviour among older Australians: Evidence from the early stages of COVID-19 pandemic. PLoS One, 18, e0286733. – reference: Li, P., Wang, H., Guo, L., Gou, X., Chen, G., Lin, D. et al. (2022). Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Medicine, 20, 443. – reference: Orihara, S., Goto, A. and Taguri, M. (2024b). Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator. Biometrical Journal, 66(4), 2300113. – reference: Kang, H., Zhang, A., Cai, T. T. and Small, D. S. (2016). Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization. Journal of the American statistical Association, 111, 132-144. – reference: Levis, A. W., Kennedy, E. H. and Keele, L. (2024). Nonparametric identification and efficient estimation of causal effects with instrumental variables. Available at: https://arxiv.org/pdf/2402.09332. – reference: Martínez-Camblor, P., Mackenzie, T., Staiger, D. O., Goodney, P. P. and O’Malley, A. J. (2019). Adjusting for bias introduced by instrumental variable estimation in the Cox proportional hazards model. Biostatistics, 20, 80-96. – reference: Orihara, S., Goto, A. and Taguri, M. (2023). Instrumental variable estimation of causal effects with applying some model selection procedures under binary outcomes. Behaviormetrika, 50, 241-262. – reference: Swanson, S. and Hernán, M. A. (2013). Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology, 24, 370-374. – reference: Basu, A., Coe, N., Chapman, C. G. (2017). Comparing 2SLS vs 2SRI for binary outcomes and binary exposures. National Bureau of Economic Research. doi: 10.3386/w23840. – reference: Guo, Z., Kang, H., Tony Cai, T. and Small, D. S. (2018). Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting. Journal of the Royal Statistical Society Series B: Statistical Methodology, 80, 793-815. – reference: Rees, J. M., Wood, A. M., Dudbridge, F. and Burgess, S. (2019). Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates. PloS One, 14, e0222362. – reference: Burgess, S. and Thompson, S. G. (2013). Use of allele scores as instrumental variables for Mendelian randomization. International Journal of Epidemiology, 42, 1134-1144. Medical Research, 26, 2333-2355. – reference: 岡田随象(2020). ゼロから実践する 遺伝統計学セミナー 疾患とゲノムを結びつける．羊土社． – reference: Burgess, S. and Thompson, S. G. (2015). Mendelian randomization: methods for using genetic variants in causal estimation. CRC Press. – reference: Orihara, S., Sugasawa, S., Ohigashi, T., Nakagawa, T. and Taguri, M. (2024c). Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models. Available at: https://arxiv.org/abs/2312.02404. – reference: Wang, L., Tchetgen Tchetgen, E., Martinussen, T. and Vansteelandt, S. (2023). Instrumental variable estimation of the causal hazard ratio. Biometrics, 79, 539-550. – reference: Hernán, M. A. and Robins, J. M. (2006). Instruments for causal inference: an epidemiologist's dream?. Epidemiology, 17, 360-372. – reference: Brookhart, M. A. and Schneeweiss, S. (2007). Preference-based instrumental variable methods for the estimation of treatment effects: assessing validity and interpreting results. The International Journal of Biostatistics, 3. – reference: Grover, S., Del Greco M, F., Stein, C. M. and Ziegler, A. (2017). Mendelian randomization. Statistical Human Genetics: Methods and Protocols, 581-628. – reference: Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R. et al. (2022). Mendelian randomization. Nature Reviews Methods Primers, 2, 6. – reference: Locke, A. E., Kahali, B., Berndt, S. I., Justice, A. E., Pers, T. H., Day, F. R. et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518, 197-206. – reference: Orihara, S. and Goto, A. (2024). Comparison of instrumental variable methods with continuous exposure and binary outcome: A simulation study. Journal of Epidemiology, accepted. – reference: Orihara, S., Fukuma, S., Ikenoue, T. and Taguri, M. (2024a). Likelihood-based instrumental variable methods for Cox proportional hazard models. Japanese Journal of Statistics and Data Science, accepted. – reference: Schuster, N. A., Twisk, J. W., Ter Riet, G., Heymans, M. W. and Rijnhart, J. J. (2021). Noncollapsibility and its role in quantifying confounding bias in logistic regression. BMC Medical Research Methodology, 21, 1-9. |
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| ispartofPNX | 計量生物学, 2025/05/30, Vol.46(1), pp.1-19 |
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