Cancer-related fatigue is one of the most persistent and disabling consequences of cancer, yet clinicians still have limited tools for identifying which patients are most likely to experience it. A new methodological study published in the Journal of Behavioral Medicine presents a data-driven strategy that could help researchers build more reliable prediction models while avoiding one of the most common problems in medical statistics: choosing an analysis method simply because it is familiar, fashionable or produces the best fit in a single data set. Rather than declaring one machine-learning technique universally superior, the researchers designed a simulation study that recreated realistic cancer-data conditions and tested which methods performed best under different circumstances.
The study, led by Nele Stadtbaeumer of Bielefeld University with Peter Borchmann of the German Hodgkin Study Group and Axel Mayer of Bielefeld University, focuses on supervised machine learning. In this framework, an algorithm learns relationships between known patient characteristics, such as demographic, clinical or psychosocial measures, and an outcome that researchers want to predict. For cancer research, the target might be fatigue, health-related quality of life or functional impairment. The central goal is not necessarily to explain why a symptom occurs, but to make accurate predictions for new patients whose outcomes are not yet known. That distinction is crucial: a variable can improve prediction without being a direct cause, while an important causal factor may contribute little to predictive accuracy if it is measured unreliably or overlaps with other information.
The researchers argue that applied scientists often face an uncomfortable choice when selecting a prediction method. They may rely on established theories, personal preferences, earlier simulation studies or whichever algorithm appears to fit their current data most closely. Each approach has weaknesses. Theory may not indicate which method will handle a particular pattern of correlations or interactions. A previous simulation may have used sample sizes or effect sizes unlike those in the new study. Choosing the best in-sample fit can reward overfitting, allowing a model to memorize quirks in the original data while performing poorly on patients outside the research sample. To address this problem, the team tailored a Monte Carlo simulation to an empirical cancer application, repeatedly generating artificial data with known properties and then checking which algorithms recovered useful predictive patterns.
The simulated data were constructed to resemble the complexity of cancer research, where predictors can be numerous, correlated and unevenly informative. The investigators varied sample size, the strength of relationships between predictors and outcomes, the degree of correlation among predictors, and the presence or absence of interaction structures. An interaction occurs when the effect of one variable depends on the level of another—for example, when the relationship between treatment burden and fatigue differs according to physical functioning or psychological distress. By controlling these features in the simulated data, the researchers could determine not only which method performed well overall, but also under which conditions its strengths or weaknesses became visible. This is a more targeted approach than treating machine-learning performance as a single universal ranking.
Eleven methods were compared. Seven were parametric approaches, including ordinary least squares regression, ridge regression, the lasso, an all-pairs lasso designed to consider interactions, and forward, backward and hybrid stepwise regression. Four were non-parametric methods capable of representing more flexible relationships: regression trees, random forests, bagging and boosting. Ordinary least squares estimates coefficients by minimizing prediction errors, but it can become unstable when predictors are strongly correlated. Ridge regression reduces that instability by shrinking coefficients toward zero, although it generally retains all variables. The lasso also applies a penalty, but can force some coefficients exactly to zero, effectively performing variable selection. These penalties are controlled by a tuning parameter, typically selected through cross-validation, so that the model balances complexity against predictive error.
The all-pairs lasso extends this idea by allowing the model to evaluate pairwise interactions between predictors. If there are many candidate variables, the number of possible pairs can expand rapidly, creating a high-dimensional problem. Regularization becomes essential because it discourages the model from retaining spurious relationships. In principle, this approach can detect situations in which combinations of patient characteristics are more informative than any single measure alone. Stepwise methods, by contrast, add or remove predictors sequentially according to a selection rule. They remain familiar and computationally accessible, but their selected variables can change substantially when the sample changes slightly, particularly when predictors are correlated. Tree-based methods split observations into increasingly homogeneous groups, while ensemble approaches such as random forests, bagging and boosting combine many trees to improve stability or predictive accuracy.
Across the different simulated conditions, forward stepwise regression, the lasso, the all-pairs lasso, bagging and boosting repeatedly outperformed the other approaches. The result does not mean that these methods are always the best choice for every cancer study. Instead, it shows that their performance was comparatively robust across the particular combination of sample sizes, effect strengths, correlations and interaction patterns considered relevant to the empirical application. This distinction is one of the study’s most important messages. A machine-learning algorithm is not judged in a vacuum; its success depends on the structure of the data, the amount of noise, the number of observations and the complexity of the relationships it must learn.
When the researchers applied the methods to empirical cancer data, the all-pairs lasso produced the strongest predictive performance among the approaches tested. Its advantage suggests that interactions between patient variables may contain useful information about cancer-related fatigue or related aspects of health-related quality of life. A patient’s fatigue burden, for example, may reflect a combination of physical limitations, emotional functioning, treatment history and other characteristics rather than a single dominant predictor. By selecting both main effects and potentially meaningful pairwise relationships while penalizing excessive complexity, the all-pairs lasso can search for these patterns without allowing every possible interaction to remain in the final model.
The empirical result should not be interpreted as a clinical diagnostic breakthrough or as evidence that the selected variables cause fatigue. Prediction and explanation answer different scientific questions. A model that forecasts a patient’s likely fatigue accurately may still reflect associations, measurement overlap or unmeasured background factors. It also requires careful external validation in new hospitals, cancer types and patient populations before it could be considered clinically useful. A model developed from Hodgkin lymphoma research may not transfer directly to people receiving treatment for breast, lung, colorectal or metastatic cancers, whose therapies, disease trajectories and symptom profiles can differ substantially. Calibration, fairness, missing-data handling and transparent reporting would also be essential before deployment.
The broader contribution of the study is methodological. It demonstrates how researchers can use an application-specific simulation to select predictive tools responsibly instead of relying on generic claims about machine learning. The authors provide R code, sample data and detailed results intended to make the analysis reproducible. Their framework offers a practical template for other investigators: first identify the likely structure of the real data, then simulate realistic alternatives, compare candidate methods using out-of-sample performance and finally test the most promising approaches on empirical observations. For cancer patients living with fatigue, the immediate benefit is not a new treatment but a clearer path toward identifying risk patterns. As predictive modeling becomes more common in behavioral medicine and oncology, that disciplined approach could help separate genuinely useful algorithms from models that merely appear impressive inside the data that created them.
Subject of Research: Supervised machine-learning methods for predicting cancer-related fatigue and health-related quality of life in cancer patients, with a focus on Hodgkin lymphoma data.
Article Title: Methodological illustration using machine learning methods to predict cancer-related fatigue in cancer patients
Article References: Stadtbaeumer, N., Borchmann, P. & Mayer, A. “Methodological illustration using machine learning methods to predict cancer-related fatigue in cancer patients.” Journal of Behavioral Medicine, 49, 337–352 (2026). https://doi.org/10.1007/s10865-026-00631-z
Image Credits: AI Generated
DOI: 10.1007/s10865-026-00631-z
Keywords: Predictor selection, machine learning, large data sets, Monte Carlo simulation, prediction, health-related quality of life, Hodgkin lymphoma, cancer-related fatigue, lasso regression, statistical learning
