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  • 學位論文

門檻模式於產婦減痛分娩多屬性效用模式資料之應用

Hurdle Model in Multi-Attribute Utility Data on Epidural Analgesia in Labor

指導教授 : 陳秀熙

摘要


背景: 臨床醫療行為的決策模式通常是一系列複雜影響因子交織而成,這些複雜因素對於最後決策行為之相對影響力若能夠清楚釐清,有助於整個決策因素之了解。過去曾有研究成功的利用多屬性效用(Multi-attribute utility, MAU)理論建立台灣產婦對減痛分娩決策過程的模式。然而,MAU模式的資料結構非常的特殊,由於其屬於兩階段性質之資料,因此我們必須設法使用特別的統計模型,如門檻模式(Hurdle Model)來處理此種資料。 目的: 本論文主要目的為 (1) 發展評估MAU資料之門檻模式 (Hurdle model);(2) 利用所發展之 MAU-based Hurdle Model之統計模式評估影響MAU在決定正反意見及其內權重之決定因素;(3)將所發展之統計模式應用於產婦對減痛分娩使用與否的MAU資料。 研究材料與方法: 本研究資料取自台北某醫學中心在2006年一月到四月間的產婦,排除計畫性剖腹產、緊急剖腹產、對減痛分娩有禁忌症(如凝血功能異常或有局部感染)及有精神方面異常或藥物濫用病史者,計151位完成MAU問卷所有問題的產婦。 我們先將產婦對MAU問卷每個問題的回應分成三個部分: (1) 對每個問題是贊成或反對,(2) 以1到10分來表示贊成的強烈程度,(3) 以1到10分來表示反對的強烈程度,再應用門檻模型的概念來發展適合分析MAU問卷資料的方法,並使用貝氏分析方法估計相關參數,並評估產婦基本特徵對MAU問卷各個問題之淨效用。最後,以選擇性門檻模式及完整門檻模式預測產婦的最終決定,並以接受者操作特徵(receiver operating characteristics, ROC)曲線來評估預測效度。取自同醫院不同期間的產婦資料則被用來作為外部驗證之用。 結果: 在151位產婦中(75位為使用減痛分娩組,76位為非使用減痛分娩組),減痛分娩組的產婦明顯有較高的教育程度(大學以上教育程度的比率: 63% vs. 44%, P = 0.001),較高的家庭月收入(超過6萬元台幣的比例: 63% vs. 46%, P = 0.04),及較高比例的親友贊成跟較低比例的親友反對 (P < 0.001)。 另外也有較高比例的初產婦 (76% vs. 46%, P < 0.001)。 在我們的改良式門檻模式中,第一部份的答題反應只有16個問題可以被基本特徵建立模式。對於第二或第三部分的答題反應,則可以發現基本特徵對其中15個題目有顯著的影響。另外,有11個題目可以發現有基本特徵的顯著淨效應存在第二部分與第三部分之間。 選擇性門檻模式在ROC曲線下的面積為0.73 (95% 信賴區間 = 0.65, 0.81),完整門檻模式為0.75 (95% 信賴區間 = 0.68, 0.83)。使用相同醫院的101位產婦資料進行兩種預測模式的外部驗證,其ROC曲線下面積都同樣是0.64 (95% 信賴區間 = 0.53, 0.75)。 結論: 本研究針對MAU問卷資料所發展出的多屬效用門檻模式 (MAU-based Hurdle model),可以成功地被應用在臨床的多屬性效用模式資料,該模式不僅可以將共變數對MAU問卷「贊成」面與「反對」面的效應同時呈現,且可以完整了解其對每個問題的影響,依此建構出有效的預測模式,甚至可以用門檻模式來評估MAU問卷的內容設計是否適當,足以測量出每個概念在正反兩面的態度差異。

並列摘要


Background Clinical decision making is always affected by a constellation of factors. The elucidation of the relationships of these factors to the final decision is of paramount importance. Multi-attribute utility (MAU) theory has been successfully used to model the decision process of parturients in Taiwan about EA, and a hierarchical questionnaire on the basis of MAU theory can predict pre-labor decision and final decision of parturients. However, the two-stage property of MAU data needs specific statistical methods, such as hurdle model for analysis. Objectives The current study aims to develop MAU-based hurdle model, to assess factors related to whether to agree and what extent of agreement/disagreement to each question answered by clients, and to apply the developed MAU-based hurdle model to the data from a previously developed 20-item multi-dimensional questionnaire on attitude toward labor EA underpinning MAU theory. Materials and methods We used data collected in a previous study in a medical center in Taipei. Study participants enrolled from January to April 2006, were of mixed parity, and were native speakers of Chinese with uncomplicated singleton pregnancy. All eligible parturients during the study period were invited to participate after delivery. The exclusion criteria were elective cesarean delivery, emergency cesarean delivery without sufficient time to consider whether to use epidural analgesia for labor, contraindications to epidural analgesia (e.g., coagulopathy, local infection), and history of psychiatric disorders or substance abuse. Of 167 parturients responding to the MAU-based questionnaire, 151 participants who completed all questions were enrolled in this study. The response to each question in this questionnaire can be divided into three parts: (1) agree or disagree to each question (binary outcome), (2) the intensity of agreement on a 1 to 10 scale, (3) the intensity of objection on a 1 to 10 scale. Then the standard hurdle model was modified to examine the interactive effect of basic characteristics of parturients on the three parts of each question using Bayesian Markov Chain Monte Carlo methods. Partial and complete hurdle models were used to predict individual response to this MAU-based questionnaire and the final decision of parturients. Receiver operating characteristics (ROC) curves were also used to assess predictive validity. We recruited another 101 parturients in the same medical center to do external validation. Result Of 151 participants (75 EA and 76 non-EA groups), parturients in the EA group had significantly higher education level (rate of university or above: 63% vs. 44%, P = 0.001), higher family income per month (rate of more than sixty thousand NT: 63% vs. 46%, P = 0.04), and higher proportion of support and lower proportion of discourage from family members or friends (P < 0.001). There were also more primiparae in the EA group compared with non-EA group (76% vs. 46%, P < 0.001). The first part of our modified hurdle model showed only 16 out of 20 questions could be modeled by basic characteristics. Basic characteristics had significant effects on second part or third part consisted of 15 questions. Significant net effect of basic characteristics between second and third part could be identified in 11 questions. The area under ROC curve equaled to 0.73 (95% confidence interval = 0.65, 0.81) for partial hurdle model, and 0.75 (95% confidence interval = 0.68, 0.83) for complete hurdle model. External validation leads to the area under ROC curve of both partial and complete hurdle models equal to 0.64 (95% confidence interval = 0.53, 0.75). Conclusion We applied a hurdle model to analyze a MAU-based questionnaire originally developed to measure attitude toward labor EA. This novel method is able to highlight differential effects of basic characteristics on parturients who accept and refuse EA. This analysis enables us to have a better understanding of how basic characteristics influence the response to each question and the final decision.

參考文獻


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