医学
概化理论
偏爱
随机对照试验
随机化
临床试验
医学物理学
度量(数据仓库)
样本量测定
物理疗法
外科
统计
数据挖掘
数学
计算机科学
病理
作者
Jane Young,Michael J. Solomon,James D. Harrison,Glenn Salkeld,Phyllis Butow
出处
期刊:Surgery
[Elsevier BV]
日期:2008-05-01
卷期号:143 (5): 582-588
被引量:40
标识
DOI:10.1016/j.surg.2008.01.009
摘要
One of the major barriers to randomized trials in the field of surgery is the presence of strong preferences for one of the treatment options. Patients and surgeons who favor strongly a particular treatment approach are usually reluctant to participate in trials where operative intervention is determined on the basis of randomization. This then affects both the feasibility of the trial in terms of achieving the required sample size as well as the generalizability of the study's findings. Therefore, measurement of patient and surgeon preference is a crucial component of the feasibility assessment for surgery trials. In this article, we introduce the Prospective Measure of Preference, which is a novel method to measure preferences that has been designed to accommodate the complexity of surgical decision-making. We also present a simple method to measure individual and community equipoise among expert clinicians to assess the feasibility of future randomized trials in surgery. One of the major barriers to randomized trials in the field of surgery is the presence of strong preferences for one of the treatment options. Patients and surgeons who favor strongly a particular treatment approach are usually reluctant to participate in trials where operative intervention is determined on the basis of randomization. This then affects both the feasibility of the trial in terms of achieving the required sample size as well as the generalizability of the study's findings. Therefore, measurement of patient and surgeon preference is a crucial component of the feasibility assessment for surgery trials. In this article, we introduce the Prospective Measure of Preference, which is a novel method to measure preferences that has been designed to accommodate the complexity of surgical decision-making. We also present a simple method to measure individual and community equipoise among expert clinicians to assess the feasibility of future randomized trials in surgery.
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