Statistics in Oncology Series
- In oncology, overall survival and progression-free survival are common time-to-event end points used to measure treatment efficacy. Analyses of this type of data rely on a complex statistical framework and the analysis results are only valid when the data meet certain assumptions. This article provides an overview of time-to-event data, the basic mechanics of common analysis methods, and issues often encountered when analyzing such data. Our goal is to provide clinicians and other lung cancer researchers with the knowledge to choose the appropriate time-to-event analysis methods and to interpret the outcomes of such analyses appropriately.
- Although statistical models serve as the foundation of data analysis in clinical studies, their interpretation requires sufficient understanding of the underlying statistical framework. Statistical modeling is inherently a difficult task because of the general lack of information of the nature of observable data. In this article, we aim to provide some guidance when using regression models to aid clinical researchers to better interpret results from their statistical models and to encourage investigators to collaborate with a statistician to ensure that their studies are designed and analyzed appropriately.
- Biomarkers have various applications including disease detection, diagnosis, prognosis, prediction of response to intervention, and disease monitoring. In this era of precision medicine, having validated biomarkers to inform clinical decision making is more important than ever. In this article, we discuss best the practices and potential issues in biomarker discovery and validation. We encourage team science partnerships to bring cutting-edge discovery from bench to bedside, leading to improved patient care and outcomes.
- Randomized clinical trials (RCTs) are conducted to evaluate the effect of an experimental treatment on outcomes of a target patient population. Eligibility criteria for large trials are often broad to ensure that the trial results can be generalized to a larger patient population. Subgroup analyses, either specified a priori or post hoc, are perfo rmed to evaluate the treatment effect specific to a subgroup of treated patients. Regardless of whether a subgroup analysis is specified a priori or post hoc, investigators must consider inflated false-positive rates, chance differences in observed treatment effects, low power for the comparisons of interest, and interpretation of the subgroup results.