On the uncertainty of individual prediction because of sampling predictors
- Resource Type
- Authors
- Changyu, Shen; Xiaochun, Li
- Source
- Statistics in medicine. 35(12)
- Subject
- Likelihood Functions
Biomedical Research
Models, Statistical
Research Design
Uncertainty
Humans
Models, Theoretical
Sampling Studies
Forecasting
Probability
Proportional Hazards Models
- Language
- ISSN
- 1097-0258
Prediction of an outcome for a given unit based on prediction models built on a training sample plays a major role in many research areas. The uncertainty of the prediction is predominantly characterized by the subject sampling variation in current practice, where prediction models built on hypothetically re-sampled units yield variable predictions for the same unit of interest. It is almost always true that the predictors used to build prediction models are simply a subset of the entirety of factors related to the outcome. Following the frequentist principle, we can account for the variation because of hypothetically re-sampled predictors used to build the prediction models. This is particularly important in medicine where the prediction has important and sometime life-death consequences on a patient's health status. In this article, we discuss some rationale along this line in the context of medicine. We propose a simple approach to estimate the standard error of the prediction that accounts for the variation because of sampling both subjects and predictors under logistic and Cox regression models. A simulation study is presented to support our argument and demonstrate the performance of our method. The concept and method are applied to a real data set. Copyright © 2015 John WileySons, Ltd.