Sequential Analysis in a Bayesian Model of Diastolic Blood Pressure Measurement
Sequential Analysis in a Bayesian Model of Diastolic Blood Pressure Measurement
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DOI：
10.1177/0272989x8800800307
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发表时间：
1988-08
影响因子：
3.6
通讯作者：
C. Schechter
中科院分区：
文献类型：
--
作者：
C. Schechter
本文介绍了一种基于贝叶斯舒张压模型诊断或排除高血压的序贯方法。对Wald的似然比方法进行了改进，以考虑先验概率分布的影响，并约束策略以获得指定的正负预测值。由此得到的用于诊断和排除舒张期高血压的上限和下限公式可以使用手持计算器和标准正态分布面积表进行评估。该策略是针对血压分布与参加高血压检测和随访计划的队列人群相似的人群说明的，以90毫米汞柱为定义高血压的截止点，所需的阳性预测值和阴性预测值均为95%。利用蒙特卡罗方法对该策略的性能进行了仿真。诊断所需读数的中位数是3，80%的受试者在11次或更少的读数中被诊断出来。与该策略95%的预测值形成对比的是，需要相同平均测量值的固定测量次数策略的正预测值仅为83%，负预测值为96%。当模型的参数被正确地测量或估计时，该方法对于诊断已知人群中的高血压是实用、有效和准确的。关键词：贝叶斯定理；序贯分析；高血压诊断。(1988年，梅德韦尔8：191-196)
A sequential method for diagnosing or excluding hypertension based on the Bayesian model of diastolic blood pressure presented in a companion article is presented. The likelihood ratio method of Wald is modified to include the effects of a prior probability distribution and to constrain the strategy to achieve specified positive and negative predictive values. The resulting formulas for upper and lower limits to diagnose and exclude diastolic hypertension can be evaluated using a hand calculator and a table of areas of the standard normal distribution. The strategy is illustrated for a population having a blood pressure distribution similar to that of the cohort screened for participation in the Hypertension Detection and Follow-up Program, with 90 mm Hg as the cutoff defining hypertension and required positive and negative predictive values of 95%. The performance of the strategy was simulated using Monte Carlo methods. The median number of readings required for diagnosis is three, and 80% of subjects are diagnosed in 11 or fewer readings. In contrast to the strategy's 95% predictive values, a fixed-number-of-measurements strategy requiring the same mean num ber of measurements has a positive predictive value of only 83% and a negative predictive value of 96%. When the parameters of the model have been properly measured or estimated, this method is practical, efficient, and accurate for diagnosing hypertension in a known population. Key words: Bayes' theorem; sequential analysis; hypertension diagnosis. (Med Decis Making 8:191-196, 1988)