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標題: | 高階馬可夫鏈與移動-停留模型於難治型癲癇病人預後之應用 Application of High Order Markov Model and Mover-Stayer Model to the Prognosis of Patients with Intractable Epilepsy |
作者: | Fang-Yi Li 李芳儀 |
指導教授: | 陳秀熙 |
關鍵字: | 高階馬可夫模型,移動-停留馬可夫模型,難治型癲癇病患, High-order Markov model,Mover-stayer Markov model,Intractable epileptic patients, |
出版年 : | 2009 |
學位: | 碩士 |
摘要: | 闡明難治型癲癇病患接受手術治療的預後情形十分重要,然而,病患每段時間癲癇發作的追蹤資料伴隨著異質性和相關性,兩者都妨礙我們使用傳統的統計方法進行分析,因此,在有無考慮共變項的前提下,我們提出高階和移動-停留馬可夫模型以適用於擁有這些棘手特性的資料。
274名曾經接受手術治療的病患在手術後五年內接受追蹤,以癲癇的月發作次數分類成以下三種狀態:良好:月發作0次;輕微:月發作1-2次;嚴重:月發作3次以上。收集的共變項包含年齡、性別、初次發病年齡、術前用藥總數、出生前後損傷、顳葉內側硬化以及初次發病到手術間隔時間等。 將一階馬可夫模型、二階馬可夫模型以及移動-停留模型應用於模式化這些資料,並使用最大概似法進行參數估計,一階馬可夫模型的結果顯示進展和復原的速率相依於前一個狀態,給定前一個狀態為良好或輕微,進展到嚴重狀態的力量分別是1%和15%;二階馬可夫模型以及移動-停留模型移動者的狀態轉移行為也有相似的結果,值得注意的是,移動-停留模型的結果顯示停留者的比例為62%,而且根據AIC,移動-停留模型有最佳的配適程度。 共變項特定回歸模型方面,我們發現對於狀態惡化的總效應而言,術前用藥總數和出生前後損傷是兩個加速因子,相反的,顳葉內側硬化則是一個減速因子。 總結來說,高階馬可夫鏈和移動-停留模型可以有效地模式化病患狀態的進展和回復,此外,對於難治型癲癇病患手術治療預後因子的鑑別也十分有用。 Elucidating the prognosis of intractable epileptic patients treated with surgery is important. However, as follow-up data on the episodes of seizure of these patients are fraught with heterogeneity and correlated property, both preclude one from analyzing the data with conventional statistical method. The higher-order and mover-stayer Markov model with or without considering covariates were therefore proposed to accommodate these intractable properties. A total of 274 patients who had undergone surgery were followed over five years on the monthly episodes of seizure with classification of three states: Normal – 0 count of episode; Mild – 1 to 2 counts of episode; Severe – above 3 counts of episode. Baseline covariates were collected including age, gender, age of onset, medication, perinatal insult, MST, and duration of epilepsy. First-order Markov model, second-order Markov model, and mover-stayer model were applied to modeling this data by using maximum likelihood estimate (MLE) method. The result of first-order Markov model showed the rates of progression and regression depended on initial status. The forces of progression to severe state were, 1% and 15%, respectively, for normal and mild in nascent state. The similar findings were noted in the second-order Markov model and the transitions for mover in the mover-stayer model. Note thate the results of mover-stayer model found 62% stayer. In the light of AIC criteria, we found the mover-stayer model had the best fit. In the covariate-specific regression model, we found medication and perinatal insult were two accelerated factors for the net force of progression and MTS was a decelerated factor. In summary, higher order and mover-stayer model were very useful for modeling the progression and regression and identification of prognostic factors in intractable epileptic patients treated with surgery. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45620 |
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顯示於系所單位: | 流行病學與預防醫學研究所 |
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