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| Title: | KLIC作為傾向分數配對平衡診斷之可行性探討
Using Kullback-Leibler Information Criterion on balancing diagnostics for baseline covariates between treatment groups in propensity-score matched samples |
| Authors: | 李珮嘉
Li, Pei Chia |
| Contributors: | 江振東
Chiang, Jeng Tung
李珮嘉
Li, Pei Chia |
| Keywords: | 傾向分數
Kullback-Leibler information criterion
Propensity score
Kullback-Leibler information criterion |
| Date: | 2014 |
| Issue Date: | 2015-03-02 10:08:39 (UTC+8) |
| Abstract: | 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。
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. |
| Reference: | 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.
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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. |
| Description: | 碩士
國立政治大學
統計研究所
101354001
103 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0101354001 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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| 400101.pdf | | 1515Kb | Adobe PDF2 | 308 | View/Open |
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