為了處理在願付價格的研究中，極端受訪者對於估計結果所造成的誤差。本文沿用Tsai(2005)所建議採用的三要素混合模型，將受訪者區分為價格再高都願意支付、願意支付合理價格以及價格再低都不願意支付等三種類型。在評估願付價格時，以加速失敗模型(accelerated failure time model，簡稱AFT model)針對願意支付合理價格的受訪者進行估計，並且在考慮不同起價點可能會造成不同程度的起價點偏誤(starting point bias)或是定錨效果(anchoring effect)的情形下，提出一個起價點偏誤調整模型來做探討。我們並以CVDFACTS中的高血壓之願付價格資料進行實證分析。分析結果發現，教育程度越高的男性對於能降低高血壓病患罹患心臟血管相關疾病之新藥願意付較高的金額。此外我們也發現在此筆資料中，不同起價點確實會造成不同程度的偏誤，經由偏誤調整後會得到較高的願付金額。

A study of willingness-to-pay often suffers from the bias introduced by extreme respondents who are willing to or not willing to pay any price. To overcome the problem, a three-component model proposed by Tsai (2005) is adopted. Under such a circumstance, respondents are classified into three categories, i.e. respondents who are willing to pay any price, unwilling to pay any price, or willing to pay a reasonable price. The willingness-to-pay for those subjects who are willing to pay a reasonable price is again modeled by an accelerated failure time model (AFT model). In this study, we, however, propose an unified model that allows us to look into the issue related to starting point bias and anchoring effect, simultaneously.
Willingness-to-pay for cardiovascular disease treatment from a longitudinal follow-up survey- CVDFACTS, is investigated using the new model. Through the use of the model, we are able to detect the effects of starting point biases, and make a proper adjustment accordingly. Our analysis indicates that male respondents with higher education level have an inclination to pay higher price for the new treatment. Besides, we also discover that starting point bias does exist in this dataset.

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