Abstract IA02: A brief overview of building and validating absolute risk models

Statistical models that predict disease incidence, disease recurrence or mortality following disease onset have broad public health and clinical applications. Of great importance are models that predict absolute risk, namely the probability of a particular outcome, e.g. breast cancer, in the presence of competing causes of mortality. Although relative risks are useful for assessing the strength of risk factors, they are not nearly as useful as absolute risks for making clinical decisions or establishing policies for disease prevention. That is because such decisions or policies often weigh the favorable effects of an intervention on the disease of interest against the unfavorable effects that the intervention might have on other health outcomes. The common currency for such decisions is the (possibly weighted) absolute risk for each of the health outcomes in the presence and absence of intervention. First, I discuss various approaches to building absolute risk models from various data sources and illustrate them with absolute risk models for breast cancer and thyroid cancer.Before a risk prediction model can be recommended for clinical or public health applications, one needs to assess how good the predictions are. I will give an overview over various criteria for assessing the performance of a risk model. I assume that we have developed a risk model on training data and assess the performance of the model on independent test or validation data. This approach, termed external...
Source: Cancer Epidemiology Biomarkers and Prevention - Category: Cancer & Oncology Authors: Tags: Improving Cancer Risk Prediction for Prevention and Early Detection: Oral Presentations - Invited Abstracts Source Type: research