![]() This paper concentrates on prognostic factors and prognostic models. Such aspects require additional investigations (for example analysis of subgroups or investigation for an interaction between treatment and a factor) which will not be considered here. Despite of using terms like ‘prediction’ and ‘added predictive value’ we will not consider the role of ‘predictive factors’, a term popular in cancer research where it usually implies that a factor is relevant for treatment decision. Notation in this area of research is confusing. The use of a combined predictor would only be sensible if the genetic information adds substantial predictive value to such an ‘optimized’ clinical predictor. Knowing about difficulties in using a combined model in practice, it follows that one may try to ‘optimize’ the predictive value from a model based on clinical data. Obviously, adding predictive value from genetic information to a ‘good’ clinical model is much more difficult than adding value to a ‘less good’ clinical model. Boulesteix and Sauerbrei critically discuss various approaches for the construction of combined prediction rules and review procedures that assess and validate the added predictive value. Yet, assessing the added predictive value of genetic data to clinical data is far from trivial. In other terms, the added value of the genetic information would need to be ‘substantial’. Obviously, to be cost effective the predictive value of a combined prediction rule would need to be much larger than the predictive value of rules based on some generally available clinical measurements. However, applying combined prediction rules at a broader level would cause difficulties in many (smaller) centers and increase costs. įor some years it has been discussed to improve prediction rules through the integration of clinical and gene expression data. There are many potential pitfalls inherent in the complex process of successfully developing and validating a marker from omics data. Unfortunately, most of the results from the very large number of individual studies have not been validated and the number of clinically useful markers is pitifully small. A substantial part of such studies investigates issues for patients with cancer, breast cancer thereby being the disease considered most often. ![]() Since the beginning of the century, much of the research has been focused on issues related to personalized or stratified medicine with the assessment of genetic markers and analyses of high dimensional data as the challenge for researchers in many disciplines. Two of the key topics are the role of prognostic factors and prognostic models. ![]() In the PROGnosis RESearch Strategy (PROGRESS) series a framework to improve research of interrelated prognosis themes has been proposed. Understanding and improving the prognosis of patients with a disease or health condition is a priority in clinical research and practice.
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