Cook N R
Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Stat Med. 1996 Oct 15;15(19):2013-28. doi: 10.1002/(SICI)1097-0258(19961015)15:19<2013::AID-SIM351>3.0.CO;2-Y.
Predictive values are useful in estimating the probability distribution of a 'true' or underlying measurement, that is, without measurement error or within-person variability. They have been applied to blood pressure data to estimate the true probability that a person is hypertensive currently, or that he/she will become hypertensive based on previous data from childhood. The current work extends these results to situations where covariates are of interest. One can use multivariate regression models to model predictive values for future levels as functions of covariates as well as current measured levels. I compare predictive value estimates obtained from these models to those obtained from ordinary linear regression and from logistic regression with use of data on childhood blood pressure from East Boston, MA. Estimates obtained using the multivariate model are preferable either in terms of bias in the estimates themselves or in terms of their variability. This is particularly true with covariates included in the model. The difference between the multivariate and ordinary regression estimates depends on the conditional reliability of future levels given current blood pressure levels and covariates. I also discuss predictive value estimates for true current level given observed level as well as covariates. These also depend on the reliability of the current measure given values of covariates.
预测值在估计“真实”或潜在测量值的概率分布时很有用，也就是说，没有测量误差或个体内部变异性的情况下。它们已被应用于血压数据，以估计一个人当前患高血压的真实概率，或者根据其童年时期的既往数据判断其未来患高血压的概率。当前的工作将这些结果扩展到协变量很重要的情况。人们可以使用多元回归模型，将未来水平的预测值建模为协变量以及当前测量水平的函数。我使用马萨诸塞州东波士顿儿童血压数据，将这些模型得到的预测值估计与普通线性回归和逻辑回归得到的估计进行比较。使用多元模型得到的估计在估计本身的偏差方面或变异性方面都更可取。当模型中包含协变量时尤其如此。多元回归估计与普通回归估计之间的差异取决于给定当前血压水平和协变量时未来水平的条件可靠性。我还讨论了给定观察水平和协变量时真实当前水平的预测值估计。这些也取决于给定协变量值时当前测量的可靠性。
