(3.238.96.184) 您好!臺灣時間:2021/05/08 04:31
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:黃裕峰
研究生(外文):Yu-Feng Huang
論文名稱:多階層Rasch模式於麻醉學筆試的應用
論文名稱(外文):Hierarchical Rasch Model for Written Examinations in Anesthesiology
指導教授:陳秀熙陳秀熙引用關係
口試委員:張光宜嚴明芳張淑惠潘信良
口試日期:2013-06-13
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:流行病學與預防醫學研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:51
中文關鍵詞:非線性迴歸模型麻醉學多階層Rasch模式貝氏模型
外文關鍵詞:Nonlinear regressionAnesthesiologyHierarchical Rasch model: Bayesian model
相關次數:
  • 被引用被引用:1
  • 點閱點閱:240
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
背景:生物醫學研究領域中,預測變項與反應變項之間經常是存在非線性的函數呈現關係。其中一個典型的例子是利用羅吉斯迴歸(logistic regression)處理以二元反應變項的資料,例如Rasch模式,根據概似函數理論而來的傳統Rasch模式需符合局部獨立(local independence),或稱為條件獨立(conditional independence)的假設,而且無法處理影響個人能力的共變量和階層的資料結構。因此如何對於Rasch模式發展新的統計方式,來避免局部獨立假設,並考慮因共變量造成的異質性,或是階層結構帶來的相關性影響,是值得關注研究的問題。

目的:我們提出非線性混合迴歸和貝氏多階層模式,來配適麻醉專科醫師甄審筆試中的實際數據,以表現此兩種創新Rasch模式的合適性。並且比較此兩種方式所得到的估計值與傳統最大概似法的差異。

研究材料與方法:首先在非線性混合迴歸的架構分析Rasch模式,將考生能力參數(θ)視為服從常態分配的隨機效應,來估計考生能力(θ)與試題難度(β),再進一步將此模式擴展為納入多階層的結構,包含來不同訓練醫院的考生和不同出題者的試題;同理,並且對此問題發展貝氏多階層Rasch模式。所應用的資料來自參加2007至2010這四年測驗的考生,分別有34至37人,每一次筆試各有100道麻醉學方面的題目,同時考慮個人與醫院或不同出題者的試題,以及年齡、性別等共變量所帶來的多階層的資料結構,我們使用SAS (Statistical Analysis System)統計軟體中的NLIN和NLMIXED運算功能來估計Rasch模式中的參數;貝氏多階層Rasch模式是利用WinBUGS軟體來處理。

結果:我們的結果顯示使用最大概似法與非線性混合迴歸所得到關於考生能力(θ)與試題難度(β)的估計值與標準誤差非常接近。代表這份資料可能符合局部獨立的假設。然而,使用貝氏多階層模式來配適資料時,會使標準誤差有擴大的情況。

結論:非線性混合迴歸模式與貝氏多階層Rasch模式提供了另一種較具有彈性的估計參數方式,特別是對於多階層的資料結構,這項特性可藉由應用Rasch模式分析麻醉學筆試資料來表現。


Background: The relationship between the predictors and the response in biomedical field is often characterized by a non-linear function. One of classical examples is the application of the logistic regression model to dealing with the data on threshold-based response outcomes, such as the Rasch model. The conventional Rasch model based on likelihood-based theory requires the assumption of local independence (conditional independence) and cannot deal with covariate affecting ability and hierarchical data structure. It is therefore interesting to relax the assumption of local independence and consider the heterogeneity due to covariates or correlated property from hierarchical structure by developing new statistical methods for the Rasch model.

Aims: We proposed the nonlinear mixed and Bayesian hierarchical regression model to fit the empirical data on the written test of board certification examination for anesthesiologists to demonstrate the feasibility of using the two innovative Rasch models. Estimates obtained from both methods were compared with the conventional maximum likelihood method.

Material and Methods: The Rasch model was first framed by a non-linear mixed regression underpinning to analyze the examinee ability (θ) and item difficulty (β) by treating the parameters of θ as a random effect following a normal distribution. This non-linear mixed regression was further extended to accommodate the data with hierarchical structures on examinees from training hospitals and items developed by raters. We also developed Bayesian hierarchical Rasch model for the same purpose. The data used for applications are the numbers of examinees distributed from 34 to 37 in 4 consecutive years from 2007 to 2010. There were 100 questions related to anesthesiology in each test. Hierarchical data structured on individuals and hospitals or items under raters and also covariates on age and gender were considered in our illustration. We used Statistical Analysis System (SAS) to estimate the parameters of the Rasch model by using PROC NLIN and NLMIXED. WinBUGS software was used for Bayesian hierarchical Rasch model.

Results: Our results show the two sets of estimates (θ and β) and standard error from maximum likelihood method were very close to those from non-linear mixed regression model. This suggests the data may obey the assumption of local independence. However, the standard errors were inflated when Bayesian hierarchical Rasch model was fitted to data.

Conclusion: The nonlinear mixed regression model and Bayesian hierarchical Rasch model provides alternative ways of estimating parameters with flexibility, particularly for hierarchical data. The feasibility is demonstrated with the application of the Rasch model to written examinations in anesthesiology.


誌謝 i
摘要 ii
Abstract iv
目錄 vi
表格目錄 viii
圖目錄 ix
I 緒論 1
I.1 研究背景 1
I.2 研究目的 2
II 文獻回顧 4
II.1 非線性迴歸模式 (Non-linear Regression Model) 4
II.1.1 非線性迴歸模式例子 5
II.1.2 非線性迴歸模式應用於羅吉斯迴歸的例子 6
II.2 非線性混合效應模式 (Nonlinear mixed effects model) 8
II.3 試題反應理論 (Item Response Theory) 9
II.3.1 Rasch模式 10
II.3.2 Rasch模式與非線性混合效應模式 13
II.3.3 多階層試題反應理論模式 14
II.4 Rasch模式在麻醉學領域應用 15
II.4.1 Rasch模式於麻醉學測驗應用 15
III 材料與方法 18
III.1 資料來源 18
III.1.1 資料變項 18
III.2 Rasch模式統計分析方法 19
III.2.1 聯合最大概似參數估計 (Joint maximum likelihood parameter estimation) 19
III.2.2 非線性混合模式 21
III.2.3 潛在變項Rasch迴歸模式 23
III.2.4 貝氏多階層Rasch模式分析 23
III.3 基本資料分析、事前處理及模式比較 25
IV 結果 27
IV.1 考生基本資料 27
IV.2 非線性混合迴歸模式與聯合最大概似估計法比較 27
IV.2.1 試題難度參數估計值與標準誤差 27
IV.2.2 考生能力參數估計值與標準誤差 28
IV.2.3 預測值與觀察值比較 28
IV.2.4 非線性混合迴歸模式運算時間 29
IV.3 多階層Rasch模式分析 29
IV.3.1 潛在變項迴歸 29
IV.3.2 醫院與考生階層的隨機截距模式 29
IV.3.3 醫院與考生階層的隨機截距模式合併出題者訊息 31
V 討論 33
V.1 估計法比較 33
V.2 多階層Rasch模式應用 35
V.3 研究限制 35
VI 結論 36
表格 37
圖 46
參考文獻 48


Agresti, A. (2000). Random‐Effects Modeling of Categorical Response Data. Sociological Methodology, 30(1), 27-80.
Amtmann, D., Cook, K. F., Jensen, M. P., Chen, W. H., Choi, S., Revicki, D., . . . Lai, J.-S. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150(1), 173-182.
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561-573. doi: 10.1007/bf02293814
Andrich, D. (1988). Rasch Models for Measurement. Newbury Park, CA: Sage Publications.
Aronson, S., Butler, A., Subhiyah, R., Buckingham Jr, R. E., Cahalan, M. K., Konstandt, S., . . . Thys, D. (2002). Development and analysis of a new certifying examination in perioperative transesophageal echocardiography. Anesthesia & Analgesia, 95(6), 1476-1482.
Bates, D. M., & Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York: Wiley.
Bond, T. G., & Fox, C. M. (2007). Applying the Rasch Model: Fundamental Measurement in the Human Sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Chang, K.-Y., Chan, K.-H., Chang, S.-H., Yang, M.-C., & Chen, T. H.-H. (2008). Decision analysis for epidural labor analgesia with Multiattribute Utility (MAU) Model. The Clinical journal of pain, 24(3), 265.
Chang, K.-Y., Tsou, M.-Y., Chan, K.-H., & Chen, H.-H. (2011). Application of the Rasch Model to Develop a Simplified Version of a Multiattribute Utility Measurement on Attitude Toward Labor Epidural Analgesia. Anesthesia & Analgesia, 113(6), 1444-1449. doi: 10.1213/ANE.0b013e318230b2a8
Chang, K. Y., Tsou, M. Y., Chan, K. H., Chang, S. H., Tai, J. J., & Chen, H. H. (2010). Item analysis for the written test of Taiwanese board certification examination in anaesthesiology using the Rasch model. British Journal of Anaesthesia, 104(6), 717-722.
Cox, C., & Ma, G. (1995). Asymptotic confidence bands for generalized nonlinear regression models. Biometrics, 142-150.
Darrell Bock, R. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37(1), 29-51. doi: 10.1007/bf02291411
Davidian, M., & Giltinan, D. M. (2003). Nonlinear models for repeated measurement data: an overview and update. Journal of Agricultural, Biological, and Environmental Statistics, 8(4), 387-419.
De Ayala, R. J. (2009). The theory and practice of item response theory. New York: Guilford Press.
Dobson, A. J., & Barnett, A. G. (2008). An introduction to generalized linear models. Boca Raton: CRC Press.
Fang, Z., & Bailey, R. L. (2001). Nonlinear mixed effects modeling for slash pine dominant height growth following intensive silvicultural treatments. Forest Science, 47(3), 287-300.
Garibaldi, R. A., Subhiyah, R., Moore, M. E., & Waxman, H. (2002). The in-training examination in internal medicine: an analysis of resident performance over time. Annals of Internal Medicine, 137(6), 505-510.
Kamata, A. (2001). Item Analysis by the Hierarchical Generalized Linear Model. Journal of Educational Measurement, 38(1), 79-93. doi: 10.2307/1435439
Lesaffre, E., & Spiessens, B. (2001). On the effect of the number of quadrature points in a logistic random effects model: an example. Journal of the Royal Statistical Society: Series C (Applied Statistics), 50(3), 325-335.
Maier, K. S. (2001). A Rasch hierarchical measurement model. Journal of Educational and Behavioral Statistics, 26(3), 307-330.
Masters, G. (1982). A rasch model for partial credit scoring. Psychometrika, 47(2), 149-174. doi: 10.1007/bf02296272
McMahon, J. M., Pouget, E. R., & Tortu, S. (2006). A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED. Computational statistics & data analysis, 50(12), 3663-3680.
McRoberts, R. E., Brooks, R. T., & Rogers, L. L. (1998). Using nonlinear mixed effects models to estimate size-age relationships for black bears. Canadian Journal of Zoology, 76(6), 1098-1106. doi: 10.1139/z98-049
Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society. Series A (General), 370-384.
O''Neill, T. R., Marks, C. M., & Reynolds, M. (2005). Re-evaluating the NCLEX-RN passing standard. Journal of Nursing Measurement, 13(2), 147-165.
Pauler, D. K., & Finkelstein, D. M. (2002). Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes. Statistics in Medicine, 21(24), 3897-3911. doi: 10.1002/sim.1392
Revicki, D. A., Chen, W. H., Harnam, N., Cook, K. F., Amtmann, D., Callahan, L. F., . . . Keefe, F. J. (2009). Development and psychometric analysis of the PROMIS pain behavior item bank. Pain, 146(1-2), 158-169.
Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185-205. doi: 10.1037/1082-989x.8.2.185
SAS Institute, I., & Publishing, S. (2011). SAS / STAT 9.3 User''s Guide (Book Excerpt): Sas Inst.
Sheiner, L., & Ludden, T. (1992). Population Pharmacokinetics/Dynamics*. Annual Review of Pharmacology and Toxicology, 32(1), 185-209.
Sheu, C.-F., Chen, C.-T., Su, Y.-H., & Wang, W.-C. (2005). Using SAS PROC NLMIXED to fit item response theory models. Behavior Research Methods, 37(2), 202-218. doi: 10.3758/bf03192688
Smits, D. M., Boeck, P., & Verhelst, N. (2003). Estimation of the MIRID: A program and a SAS-based approach. Behavior Research Methods, Instruments, & Computers, 35(4), 537-549. doi: 10.3758/bf03195533
Varni, J. W., Stucky, B. D., Thissen, D., Dewitt, E. M., Irwin, D. E., Lai, J. S., . . . Dewalt, D. A. (2010). PROMIS pediatric pain interference scale: An item response theory analysis of the pediatric pain item bank. Journal of Pain.
Verbeke, G., & Molenberghs, G. (2009). Linear mixed models for longitudinal data: Springer.
Yang, S. C., Tsou, M. Y., Chen, E. T., Chan, K. H., & Chang, K. Y. (2011). Statistical item analysis of the examination in anesthesiology for medical students using the Rasch model. Journal of the Chinese Medical Association, 74(3), 125-129.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔