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An empirical study of tumor markers in clinical routine: Applying different functional forms to continuous variable data








An empirical study of tumor markers in clinical routine: Applying different functional forms to continuous variable data


Georg Heinze


Institute of Clinical Biometrics Center for Medical Data Science Medical University of Vienna






03 February 2025


Tumor markers are substances in your body, that might provide information about certain types of cancer. Specifically, they help with diagnosing conditions, deciding on treatments, and predicting how diseases might progress. Their significance, along with other factors, is explored through clinical studies, influencing patient care decisions. Understanding these markers and the impact of statistical analyses on study results is paramount. Various statistical designs exist; randomized controlled trials (RCTs) aim to diminish bias because of uneven distribution of patient characteristics through random assignment of participants. However, most studies are not randomized but are based on retrospective databases or are prospective studies which group patients based on certain characteristics. These studies use statistical methods to minimize bias because of the lack of randomization. Within these methods, some variables are defined by distinct categories, like sex or blood type, others parameter have no defined categories but have a continuous distribution of possible values. Tumor markers represent one type of continuous parameter. In non-randomized studies, it is common to establish an arbitrary cut-off for continuous variables like tumor markers, categorizing them into 'low' or 'high' groups. This dichotomization, while convenient for clinical application, can oversimplify the data and potentially distort the interpretation of the study results, affecting our understanding of all the variables considered in the research. It is estimated that dichotomization results in a loss of approximately one third of the data and therefore hampers the ability to detect clinically relevant associations between variables and the outcome1. The aim of the current project is to showcase through data from RCTs how different statistical approaches to handling tumor markers can influence research outcomes. The selection of RCTs for this examination is deliberate; these trials often have comprehensive datasets with minimal missing information, making them ideal for methodological analysis. The goal of this project is to help researchers gain a better understanding of different methodological approaches and the subsequent effects on results, which will ultimately lead to better care for patients due to more rigorous data analysis.



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Statistical Analysis Plan