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題名 傾向分數配對與確切配對之合併使用: 蒙地卡羅模擬研究與實證分析
無作者 賴致淵 貢獻者 江振東
賴致淵關鍵詞 類隨機試驗研究
傾向分數分析
傾向分數配對
確切配對
電腦模擬
Propensity scores analysis
Propensity scores matching
Exact matching
Monte Carlo simulation日期 2011 上傳時間 30-Oct-2012 10:58:19 (UTC+8) 摘要 在觀察性研究或非隨機試驗研究中，欲探討因果效應時，研究者需要重新對觀察性研究進行設計，設計目的在於重新建立一個隨機指派受試者的機制，使其得以近似一個隨機試驗研究，這樣的研究一般稱為「類隨機試驗研究」(quasi-randomized-experiments)。傾向分數分析即為一種設計觀察性研究的方法，在不牽涉到反應變數結果之下進行設計。本文於一個病例對照研究(case-control study)中使用傾向分數進行配對接著再進一步估計處理效果，傾向分數配對是可降低觀察性研究中的選擇性偏誤的方法，透過配對可減少實驗組與對照組間的系統性差異，使研究群體在所觀察到的控制變數分配達到相似，進而得到處理效果(treatment effect)的不偏估計，為近年廣受流行病學、經濟學以及社會學領域使用的方法之一。傾向分數本身為一個條件機率，定義為研究受試者在其所觀察到的控制變數之下，接受某處理或被指派至某特定群體的機率，估計傾向分數最常見的方法為羅吉斯迴歸。此外，自1970年代起，配對方法(matching method)開始被使用來選取合適的實驗組與對照組並進行兩群體的比較，其中「確切配對」屬於最常使用的配對方法，過去文獻中經常可見各種配對方法的結合使用，因此，本文電腦模擬研究部份，欲比較四種情境之下「傾向分數配對」與「確切配對」結合使用的效果，分別以偏誤降低比例、信賴區間覆蓋率、均方誤衡量兩種配對方法結合使用的適合情境。結果顯示若對「與處理指派中度相關的變數」且「與反應變數高度相關的變數」，其效果最為明顯。根據結果，我們總結認為「確切配對與傾向分數配對合併使用」確實會有較好的表現，但表現的好壞也取決於確切配對的變數。實證研究部份，探討家庭結構對青少年偏差行為之影響，欲了解來自非完整家庭之青少年是否較來自完整家庭之青少年更有容易出現偏差行為。
In observational or nonrandomized studies, treatments are not randomly assigned so that baseline differences between treated and control groups are typically observed. Without properly executed, the differences would bias the treatment effect estimates. There has been a long history of using matching to eliminate confounder bias, and inferences are made based on the matched observations. The theoretical basis for matching has been developed since 1970, and among those matching methods commonly in use, the exact matching is probably the most popular one. On the other hand, introduced by Rosenbuam and Rubin in 1983, propensity scores, the conditional probability of being exposed or treated given the observed covariates, has been a welcome alternative used to adjust for baseline differences between study groups of late. Instead of matching a treated with an untreated subject by their covariates, subjects in both treated and control groups are matched by their propensity scores. In this study, we explore the benefits of using propensity score matching together with the exact matching for adjusting for baseline differences through Monte Carlo simulations. An empirical study is also be provided for illustration.參考文獻 中文部份侯崇文(2001)-家庭結構、家庭關係與青少年偏差行為《應用心理研究 , 第11期 , 25-43頁》英文部份Agresti, A. , Min, Y.(2004).Effects and non-effects of paired identical observations incomparing proportions with binary matched-pairs data. Statistics in Medicine, 23, 65–75.Austin, P. C.(2007). The performance of different propensity score methods forestimating marginal odds ratios. Statistics in Medicine, 26, 3078–3094.Austin, P. C., Mamdani, M. M. (2006). A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use. Statistics in Medicine, 25, 2084–2106.Cochran, W.G., Rubin, D.B.(1973). Controlling bias in observational studies: A review.The Indian Journal of Statistics, Series A, 35, 417–446.D’Agostino, R. B., Jr. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281.Gu, X. S., Rosenbaum, P. R. (1993).Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2, 405–420.Hansen, B. B. (2004). Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association, 99, 609–618.Imai, K., King, G., Stuart, E.A.(2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A, 171:481-502.Neyman, J.(1923). On the application of probability theory to agricultural experiments. Statistical Science, 5(4), 465-472.Normand, S. L. T., Landrum, M. B., Guadagnoli, E., Ayanian, J. Z., Ryan, T. J., Cleary, P. D., McNeil, B.J.(2001). Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: a matched analysis using propensity scores. Journal of Clinical Epidemiology, 54, 387-398.Rosenbaum, P. R.(1987). Model-based direct adjustment. Journal of the American Statistical Association, 82, 387–394.Rosenbaum, P. R.(1989). Optimal matching for observational StudiesJournal of the American Statistical Association, 1024-1032.Rosenbaum, P. R.(2010). Design of Observational Studies. Springer series in Statistics.Rosenbaum, P. R., Rubin, D. B.(1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.Rosenbaum, P. R., Rubin, D. B. (1985a). The bias due to incomplete matching. Biometrics, 41, 103–116.Rosenbaum, P. R., Rubin, D. B. (1985b). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.Rosenbaum, P. R., Ross, R. N., Silber, J. H.(2007). Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. Journal of the American Statistical Association, 102, 75–83.Rubin, D. B. (1973). Matching to remove bias in observational studies.Biometrics, 29:159–184.Rubin, D. B.(1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.Rubin, D. B.(2001). Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Services & Outcomes Research Methodology 2, 169–188.Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward . Statistics Science, 25(1), 1–21. Yoon, F. B., Huskamp, H. A. Busch, A. B., Normand, SLT. (2011). Using multiple control groups and matching to address unobserved biases in comparative effectiveness research an observational study of the effectiveness of mental health parity. Statistics in Biosciences, 3, 63–78.Zhao, Z.(2004). Using matching to estimate treatment effects: Data requirements, matching metrics, and montecarlo evidence. Review of Economics and Statistics, 86(1), 91–107. 描述 碩士
國立政治大學
統計研究所
99354005
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099354005 資料類型 thesis dc.contributor.advisor 江振東 zh_TW dc.contributor.author (Authors) 賴致淵 zh_TW dc.creator (作者) 賴致淵 zh_TW dc.date (日期) 2011 en_US dc.date.accessioned 30-Oct-2012 10:58:19 (UTC+8) - dc.date.available 30-Oct-2012 10:58:19 (UTC+8) - dc.date.issued (上傳時間) 30-Oct-2012 10:58:19 (UTC+8) - dc.identifier (Other Identifiers) G0099354005 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54408 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計研究所 zh_TW dc.description (描述) 99354005 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 在觀察性研究或非隨機試驗研究中，欲探討因果效應時，研究者需要重新對觀察性研究進行設計，設計目的在於重新建立一個隨機指派受試者的機制，使其得以近似一個隨機試驗研究，這樣的研究一般稱為「類隨機試驗研究」(quasi-randomized-experiments)。傾向分數分析即為一種設計觀察性研究的方法，在不牽涉到反應變數結果之下進行設計。本文於一個病例對照研究(case-control study)中使用傾向分數進行配對接著再進一步估計處理效果，傾向分數配對是可降低觀察性研究中的選擇性偏誤的方法，透過配對可減少實驗組與對照組間的系統性差異，使研究群體在所觀察到的控制變數分配達到相似，進而得到處理效果(treatment effect)的不偏估計，為近年廣受流行病學、經濟學以及社會學領域使用的方法之一。傾向分數本身為一個條件機率，定義為研究受試者在其所觀察到的控制變數之下，接受某處理或被指派至某特定群體的機率，估計傾向分數最常見的方法為羅吉斯迴歸。此外，自1970年代起，配對方法(matching method)開始被使用來選取合適的實驗組與對照組並進行兩群體的比較，其中「確切配對」屬於最常使用的配對方法，過去文獻中經常可見各種配對方法的結合使用，因此，本文電腦模擬研究部份，欲比較四種情境之下「傾向分數配對」與「確切配對」結合使用的效果，分別以偏誤降低比例、信賴區間覆蓋率、均方誤衡量兩種配對方法結合使用的適合情境。結果顯示若對「與處理指派中度相關的變數」且「與反應變數高度相關的變數」，其效果最為明顯。根據結果，我們總結認為「確切配對與傾向分數配對合併使用」確實會有較好的表現，但表現的好壞也取決於確切配對的變數。實證研究部份，探討家庭結構對青少年偏差行為之影響，欲了解來自非完整家庭之青少年是否較來自完整家庭之青少年更有容易出現偏差行為。 zh_TW dc.description.abstract (摘要) In observational or nonrandomized studies, treatments are not randomly assigned so that baseline differences between treated and control groups are typically observed. Without properly executed, the differences would bias the treatment effect estimates. There has been a long history of using matching to eliminate confounder bias, and inferences are made based on the matched observations. The theoretical basis for matching has been developed since 1970, and among those matching methods commonly in use, the exact matching is probably the most popular one. On the other hand, introduced by Rosenbuam and Rubin in 1983, propensity scores, the conditional probability of being exposed or treated given the observed covariates, has been a welcome alternative used to adjust for baseline differences between study groups of late. Instead of matching a treated with an untreated subject by their covariates, subjects in both treated and control groups are matched by their propensity scores. In this study, we explore the benefits of using propensity score matching together with the exact matching for adjusting for baseline differences through Monte Carlo simulations. An empirical study is also be provided for illustration. en_US dc.description.tableofcontents 第一章、緒論……………………………………………………1第一節、研究背景與動機………………………………………1第二節、研究目的………………………………………………2第三節、本文架構………………………………………………3第二章、文獻探討………………………………………………4第一節、處理效果………………………………………………4第二節、傾向分數之基本概念…………………………………7第三節、傾向分數之使用方式…………………………………11第四節、配對方法之發展………………………………………13第五節、配對後之平衡診斷……………………………………18第三章、研究方法………………………………………………22第一節、研究設計………………………………………………22第二節、邊際處理效果…………………………………………23第三節、資料生成………………………………………………25第四節、情境設定………………………………………………28第五節、模擬結果之評估方式…………………………………29第四章、模擬分析結果…………………………………………31第一節、對數勝算比之估計……………………………………31第二節、偏誤與偏誤降低比例…………………………………32第三節、均方誤…………………………………………………37第四節、95%信賴區間覆蓋率……………………………………41第五節、小結……………………………………………………42第五章、實證研究………………………………………………44第一節、實證主題探討…………………………………………44第二節、資料來源與研究母體…………………………………45第三節、變數定義………………………………………………45第四節、實證結果分析…………………………………………47第六章、結論與建議……………………………………………54第一節、結論……………………………………………………54第二節、建議……………………………………………………55參考文獻 …………………………………………………………56附錄一、對數勝算比分配………………………………………59附錄二、其餘11種Exact-PS配對實證分析結果………………61附錄三、內政部臺閩地區少年身心狀況調查表………………61附錄四、程式碼…………………………………………………68 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099354005 en_US dc.subject (關鍵詞) 類隨機試驗研究 zh_TW dc.subject (關鍵詞) 傾向分數分析 zh_TW dc.subject (關鍵詞) 傾向分數配對 zh_TW dc.subject (關鍵詞) 確切配對 zh_TW dc.subject (關鍵詞) 電腦模擬 zh_TW dc.subject (關鍵詞) Propensity scores analysis en_US dc.subject (關鍵詞) Propensity scores matching en_US dc.subject (關鍵詞) Exact matching en_US dc.subject (關鍵詞) Monte Carlo simulation en_US dc.title (題名) 傾向分數配對與確切配對之合併使用: 蒙地卡羅模擬研究與實證分析 zh_TW dc.title (題名) 無 zh_TW dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 中文部份侯崇文(2001)-家庭結構、家庭關係與青少年偏差行為《應用心理研究 , 第11期 , 25-43頁》英文部份Agresti, A. , Min, Y.(2004).Effects and non-effects of paired identical observations incomparing proportions with binary matched-pairs data. Statistics in Medicine, 23, 65–75.Austin, P. C.(2007). The performance of different propensity score methods forestimating marginal odds ratios. Statistics in Medicine, 26, 3078–3094.Austin, P. C., Mamdani, M. M. (2006). A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use. Statistics in Medicine, 25, 2084–2106.Cochran, W.G., Rubin, D.B.(1973). Controlling bias in observational studies: A review.The Indian Journal of Statistics, Series A, 35, 417–446.D’Agostino, R. B., Jr. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281.Gu, X. S., Rosenbaum, P. R. (1993).Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2, 405–420.Hansen, B. B. (2004). Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association, 99, 609–618.Imai, K., King, G., Stuart, E.A.(2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A, 171:481-502.Neyman, J.(1923). On the application of probability theory to agricultural experiments. Statistical Science, 5(4), 465-472.Normand, S. L. T., Landrum, M. B., Guadagnoli, E., Ayanian, J. Z., Ryan, T. J., Cleary, P. D., McNeil, B.J.(2001). Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: a matched analysis using propensity scores. Journal of Clinical Epidemiology, 54, 387-398.Rosenbaum, P. R.(1987). Model-based direct adjustment. Journal of the American Statistical Association, 82, 387–394.Rosenbaum, P. R.(1989). Optimal matching for observational StudiesJournal of the American Statistical Association, 1024-1032.Rosenbaum, P. R.(2010). Design of Observational Studies. Springer series in Statistics.Rosenbaum, P. R., Rubin, D. B.(1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.Rosenbaum, P. R., Rubin, D. B. (1985a). The bias due to incomplete matching. Biometrics, 41, 103–116.Rosenbaum, P. R., Rubin, D. B. (1985b). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.Rosenbaum, P. R., Ross, R. N., Silber, J. H.(2007). Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. Journal of the American Statistical Association, 102, 75–83.Rubin, D. B. (1973). Matching to remove bias in observational studies.Biometrics, 29:159–184.Rubin, D. B.(1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.Rubin, D. B.(2001). Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Services & Outcomes Research Methodology 2, 169–188.Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward . Statistics Science, 25(1), 1–21. Yoon, F. B., Huskamp, H. A. Busch, A. B., Normand, SLT. (2011). Using multiple control groups and matching to address unobserved biases in comparative effectiveness research an observational study of the effectiveness of mental health parity. Statistics in Biosciences, 3, 63–78.Zhao, Z.(2004). Using matching to estimate treatment effects: Data requirements, matching metrics, and montecarlo evidence. Review of Economics and Statistics, 86(1), 91–107. zh_TW
