TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS
TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS
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发表时间：
1996
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通讯作者：
Kerry L. Lee;Daniel B. Mark
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作者：
Kerry L. Lee;Daniel B. Mark
综述：多变量回归模型是临床结果研究中常用的有力工具。这些模型可以使用分类变量和连续变量的混合，并可以处理部分观察(审查)的反应。然而，不严格地应用建模技术可能会导致模型与手头的数据集不太匹配，或者更有可能的是，不准确地预测新对象的结果。人们必须知道如何衡量模特的适合度，以避免模特不合身或过合身。对于生存时间数据，在存在审查的情况下，预测准确性的测量可能很困难。我们讨论了一个易于解释的预测判别指数，以及评估预测生存概率校准的方法。在使用新数据系列中的预测之前，这两种类型的预测准确性都应该使用自举或交叉验证进行无偏见的验证。我们讨论了拟合不佳和过拟合回归模型的一些危险，并提出了一种避免许多所讨论的问题的建模策略。所描述的方法适用于所有回归模型，但对于二进制、序数和事件发生时间结果尤其需要。用COX回归方法对前列腺癌患者进行生存分析。由于许多原因，准确估计患者的预后是很重要的。首先，预后评估可以用来告知患者她的疾病可能的结果。其次，医生可以使用对预后的估计作为安排额外检查和选择适当治疗的指南。第三，预测评估在技术评估中是有用的；可以比较在使用和不使用给定测试结果的情况下得出的预测估计，以衡量该测试提供的预测信息相对于先前信息提供的信息的增量。第四，研究人员可能想要在观察性研究中估计单个因素(例如，给予的治疗)对预后的影响，在观察性研究中，还测量了许多未受控制的混杂因素。在这里，必须控制非受控变量的同时影响(如果使用回归模型，则在数学上保持恒定)，以便可以更纯粹地估计感兴趣的因素的影响。分析变量(特别是连续变量)如何影响感兴趣的患者结果是必要的
SUMMARY Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression. Accurate estimation of patient prognosis is important for many reasons. First, prognostic estimates can be used to inform the patient about likely outcomes of her disease. Second, the physician can use estimates of prognosis as a guide for ordering additional tests and selecting appropriate therapies. Third, prognostic assessments are useful in the evaluation of technologies; prognostic estimates derived both with and without using the results of a given test can be compared to measure the incremental prognostic information provided by that test over what is provided by prior information.' Fourth, a researcher may want to estimate the effect of a single factor (for example, treatment given) on prognosis in an observational study in which many uncontrolled confounding factors are also measured. Here the simultaneous effects of the uncontrolled variables must be controlled (held constant mathematically if using a regression model) so that the effect of the factor of interest can be more purely estimated. An analysis of how variables (especially continuous ones) affect the patient outcomes of interest is necessary to