Assessing risk communication in breast cancer: are continuous measures of patient knowledge better than categorical?
Assessing risk communication in breast cancer: are continuous measures of patient knowledge better than categorical?
复制标题
评估乳腺癌的风险沟通：对患者知识的连续测量是否比分类更好？
DOI：
10.1016/j.pec.2008.11.012
复制
发表时间：
2009
影响因子：
3.5
通讯作者：
Hutton,DavidW
中科院分区：
文献类型：
--
作者：
Belkora,Jeffrey;Moore,DanH;Hutton,DavidW
CAPTIVETo比较患者知识的分类和连续措施的风险沟通乳腺癌的背景下，在统计学和临床意义以及efficiency.METHODSTwenty乳腺癌患者提供了10年的死亡风险估计之前和之后，他们的肿瘤学访问的性能。肿瘤学家审查了Adjuvant！的风险估计，一个经过充分验证且常用的预后模型。使用辅助剂！作为金标准，我们计算了患者在就诊前后的估计准确度。我们使用了三种新的连续测量患者准确性的方法，即绝对偏差、Brier和Kullback-Leibler评分，并将其与干预效果敏感性的分类测量方法进行了比较。我们还计算了使用分类和连续测量重复主要研究所需的样本量，作为比较效率的一种手段。在该样本中，Kullback-Leibler测量对干预效果最敏感（p=0.004），其次是Brier和绝对偏倚（p=0.011），最后是分类测量（0.125）。复制的主要研究所需的样本量为18 Kullback-Leibler措施，23绝对偏差和Brier，和37的分类measure. CONCLUSIONSSThe连续措施导致更有效的样本量和拒绝零假设没有干预效果。然而，连续测量的敏感性差异在统计学上并不显著，分类测量的性能取决于研究人员的准确性分类截止值。患者的准确性的连续措施可能是更敏感和有效的，而分类措施可能更临床relevant.PRACTICE IMPLICATIONS研究人员和其他有兴趣在评估患者知识的准确性应权衡临床相关性和统计学意义之间的权衡，同时设计或评估风险沟通研究。
OBJECTIVETo compare the performance of categorical and continuous measures of patient knowledge in the context of risk communication about breast cancer, in terms of statistical and clinical significance as well as efficiency.METHODSTwenty breast cancer patients provided estimates of 10-year mortality risk before and after their oncology visit. The oncologist reviewed risk estimates from Adjuvant!, a well-validated and commonly used prognostic model. Using the Adjuvant! estimates as a gold standard, we calculated how accurate the patient estimates were before and after the visit. We used three novel continuous measures of patient accuracy, the absolute bias, Brier, and Kullback–Leibler scores, and compared them to a categorical measure in terms of sensitivity to intervention effects. We also calculated the sample size required to replicate the primary study using the categorical and continuous measures, as a means of comparing efficiency.RESULTSIn this sample, the Kullback–Leibler measure was most sensitive to the intervention effects (p=0.004), followed by Brier and absolute bias (both p=0.011), and finally the categorical measure (0.125). The sample size required to replicate the primary study was 18 for the Kullback–Leibler measure, 23 for absolute bias and Brier, and 37 for the categorical measure.CONCLUSIONSThe continuous measures led to more efficient sample sizes and to rejection of the null hypothesis of no intervention effect. However, the difference in sensitivity of the continuous measures was not statistically significant, and the performance of the categorical measure depends on the researcher's categorical cutoff for accuracy. Continuous measures of patient accuracy may be more sensitive and efficient, while categorical measures may be more clinically relevant.PRACTICE IMPLICATIONSResearchers and others interested in assessing the accuracy of patient knowledge should weigh the trade-offs between clinical relevance and statistical significance while designing or evaluating risk communication studies.