Practical guide to the typical analysis of prognostic factors and biomarkers without the use of P-values

We have seen an increasing number of studies evaluating biomarkers and prognostic factors. Biomedical researchers like to draw conclusions based on P-values. However, P-values are often not needed for this type of study. In this article, we show how most biomedical research problems in this area could be organized into three main analyses, each avoiding the use of P-values.

The three main analyses follow the framework of prediction modeling when the outcome of interest is binary or time-to-event. The analyses make use of figures such as boxplots, nonparametric smoothing line, and nomogram, and also incorporate prediction performance measures such as the Area under the receiver operating characteristic curve, and index of predictive accuracy.

Our proposed framework is easy to follow. It is also in line with most of the research in the field of biomarkers and prognostic factors evaluation, such as reclassification table, net reclassification index, Akaike information criterion/Bayesian information criterion, receiver operating characteristic curve, and decision curve analysis.

Overall, we provide a step-by-step guideline that biomedical researchers could easily follow to conduct statistical analysis without using P-values, especially with the goal of evaluating biomarkers and prognostic factors.