Logistic regression in cancer research: A narrative review of the concept, analysis, and interpretation

Abstract

Logistic regression is a fundamental statistical technique employed in predictive modeling. It transforms a linear combination of input variables into a probability value, allowing the available data to predict the likelihood of an event occurring. Interpretation involves understanding the coefficients of the model, odds ratios, and the impact of predictor variables on the outcome. Various performance metrics, such as the receiver operating characteristic curve, the area under the curve, and R-squared (measure of the percentage of total variation in the dependent variable that is accounted for by the independent variable), aid in assessing the model accuracy. We conducted an extensive search in the PubMed database for relevant articles published in English between January 2013 and August 2023 using the keywords, “logistic regression,” “binary logistic regression,” “logistic regression in cancer research,” “logistic regression analysis,” and “logistic regression result interpretation.” Of the 118 articles retrieved by the original search, we excluded 103 and included 15 in the review; we manually added six more articles considered classic examples of logistic regression and regression statistics. The review encompasses a wide spectrum of cancer research applications, from tumor classification and prognosis to risk assessment and response prediction. The article takes a step-by-step approach, guiding readers through the data preparation, model construction, and interpretation processes in the context of logistic regression.