| dc.contributor.advisor | 江振東 | zh_TW |
| dc.contributor.advisor | Chiang, Jeng Tung | en_US |
| dc.contributor.author (Authors) | 李珮嘉 | zh_TW |
| dc.contributor.author (Authors) | Li, Pei Chia | en_US |
| dc.creator (作者) | 李珮嘉 | zh_TW |
| dc.creator (作者) | Li, Pei Chia | en_US |
| dc.date (日期) | 2014 | en_US |
| dc.date.accessioned | 2-Mar-2015 10:08:39 (UTC+8) | - |
| dc.date.available | 2-Mar-2015 10:08:39 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Mar-2015 10:08:39 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0101354001 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/73538 | - |
| 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) | 101354001 | zh_TW |
| dc.description (描述) | 103 | zh_TW |
| dc.description.abstract (摘要) | 觀察性研究資料中，透過傾向分數的使用，可以使基準變數在實驗與對照兩組間達到某種程度的平衡，並可視同為一隨機試驗，進而進行有效的統計推論。文獻中有關平衡與否的診斷，大多聚焦於平均數與變異數的比較。本文中我們提出使用KLIC(Kullback-Leibler Information Criterion)及KS(Kolmogorov and Simonov)兩種比較分配函數差異的統計量，作為另一種平衡診斷工具的構想，並針對其可行性進行探討與評比。此外，數據顯示KLIC及KS與透過傾向分數配對的成功比例呈現負相關。由於配對成功比例過低將導致後續統計推論結果的侷限性，因此本文也就KLIC及KS作為是否進行配對的一個先行指標之可行性作探討。模擬結果顯示，二者的答案均是肯定的。 | zh_TW |
| dc.description.abstract (摘要) | In observational studies, propensity scores are frequently used as tools to balance the distribution of baseline covariates between treated and untreated groups to some extent so that the data could be treated as if they were from a randomized controlled trial (RCT) and causal inferences could thus be made. In the past, balance or not was usually diagnosed in terms of the means and/or the variances. In this study, we proposed using either Kullback-Leibler Information Criterion (KLIC) or Kolmogorov and Simonov (KS) statistic as a diagnostic measure, and evaluated its feasibility. In addition, since low propensity score matching rate decreases the power of the statistical inference and a pilot study showed that the matching rate was negatively correlated with KLIC and KS; thus, we also discussed the possibilities of using KLIC and KS to be pre-indices before implementing propensity score matching. Both considerations appear to be positive through our simulation study. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1第二章 文獻探討 3第一節 KLIC 3壹、Shannon Entropy 3貳、Kullback-Leibler Information Criterion 4第二節 傾向分數 5第三章 研究方法與設計 7第一節 研究目的 7第二節 研究設計 8壹、臨界值的求取 8貳、檢定力比較 10參、樣本數比例對照 13第四章 研究結果與討論 16第一節 臨界值的選定 16第二節 型一錯誤機率及檢定力 18第三節 配對前後對照表 24第五章 實證分析 28第一節 主題分析 28第二節 資料來源與變數定義 28壹、資料來源 28貳、變數說明 29第三節 分析結果 29壹、分析步驟 29貳、分析結果 30第六章 結論與建議 32第一節 結論 32第二節 未來研究方向建議 33參考文獻 34附錄 35 | zh_TW |
| dc.format.extent | 1552320 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0101354001 | en_US |
| dc.subject (關鍵詞) | 傾向分數 | zh_TW |
| dc.subject (關鍵詞) | Kullback-Leibler information criterion | zh_TW |
| dc.subject (關鍵詞) | Propensity score | en_US |
| dc.subject (關鍵詞) | Kullback-Leibler information criterion | en_US |
| dc.title (題名) | KLIC作為傾向分數配對平衡診斷之可行性探討 | zh_TW |
| dc.title (題名) | Using Kullback-Leibler Information Criterion on balancing diagnostics for baseline covariates between treatment groups in propensity-score matched samples | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | 1.Rosenbaum, P.R. and D.B. Rubin, The central role of the propensity score in observational studies for causal effects. Biometrika, 1983. 70(1): p. 41-55.2.Austin, P.C., An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 2011. 46(3): p. 399-424.3.Frölich, M., Finite-sample properties of propensity-score matching and weighting estimators. Review of Economics and Statistics, 2004. 86(1): p. 77-90.4.Busso, M., J. DiNardo, and J. McCrary, New evidence on the finite sample properties of propensity score reweighting and matching estimators. Review of Economics and Statistics, 2011(0).5.Cover, T.M. and J.A. Thomas, Entropy, relative entropy and mutual information. Elements of Information Theory, 1991: p. 12-49.6.Ullah, A., Entropy, divergence and distance measures with econometric applications. Journal of Statistical Planning and Inference, 1996. 49(1): p. 137-162.7.Kullback, S. and R.A. Leibler, On information and sufficiency. The Annals of Mathematical Statistics, 1951: p. 79-86.8.Austin, P.C., Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity‐score matched samples. Statistics in medicine, 2009. 28(25): p. 3083-3107.9.Azzalini, A., The skew-normal and related families. Vol. 3. 2013: Cambridge University Press.10.Austin, P.C., The performance of different propensity score methods for estimating marginal odds ratios. Statistics in medicine, 2007. 26(16): p. 3078-3094.11.Dowd, K., Measuring market risk. 2007: John Wiley & Sons.12.Frenkel-Toledo, S., et al., Journal of NeuroEngineering and Rehabilitation. Journal of neuroengineering and rehabilitation, 2005. 2(23): p. 0003-2.13.Goldberger, A.L., et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000. 101(23): p. E215-20. | zh_TW |
