Statistics in Oncology Series
- 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.
- 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.
- Statistical methods are essential in medical research. They are used for data analysis and drawing appropriate conclusions. Clarity and accuracy of statistical reporting in medical journals can enhance readers’ understanding of the research conducted and the results obtained. In this manuscript, we provide guidelines for statistical reporting in medical journals for authors to consider, with a focus on the Journal of Thoracic Oncology.
- The Journal of Thoracic Oncology (JTO) is excited to introduce a series of brief articles on various statistical topics in the journal this year, starting with the current issue. As the mission of the JTO is to be the “primary educational and informational publication on topics relevant to the prevention, detection, diagnosis, and treatment of thoracic malignancies,” we strive to publish manuscripts of the highest scientific rigor in these areas. Rigorous research requires that studies are thoughtfully conceived, designed, and conducted; with data appropriately analyzed and interpreted; and conclusions meaningfully framed in a context that expands current knowledge.
- The scientific synergy between statistics and medical research often leads to controversy when considering statistical versus clinical significance of findings from studies that are neither overwhelmingly practice-changing nor clearly negative. Studies can reach statistical significance but provide evidence that is not clinically meaningful, or results could not be statistically significant but very clinically relevant. To fully appreciate the debate about studies that fall in this gray area, one must understand how to interpret several features of statistical design and the interpretation of results.
- Clinical trials are a fundamental component of medical research and serve as the main route to obtain evidence of the safety and efficacy of treatment before its approval. A trial’s ability to provide the intended evidence hinges on appropriate design, background knowledge, trial rationale to sample size, and interim monitoring rules. In this article, we present some general design principles for investigators and their research teams to consider when planning to conduct a trial.
- Systematic reviews and meta-analytic approaches are widely used in the clinical arena to integrate outcome data from published studies in a patient population that address a set of related research hypotheses. The credibility of this line of research is dependent on how the studies are chosen, how the data are assembled, and how the results are reported. In this brief report, we provide an overview of the minimum set of reporting requirements for systematic reviews and meta-analyses based on the Preferred Reporting Items of Systematic reviews and Meta-Analyses guidelines.
- Designs for biomarker validation have been proposed and used in the phase III oncology clinical trial setting. Broadly, these designs follow either an enrichment (i.e., targeted) strategy or an all-comers (i.e., unselected) strategy. An enrichment design screens patients for the presence or absence of a marker or a panel of markers and then only includes patients who either have or do not have a certain marker characteristic or profile. In contrast, all patients meeting the eligibility criteria (regardless of a particular biomarker status) are entered into an all-comers design.
- The classic single-arm oncology phase II trial designs for evaluating an experimental regimen/agent are limited by multiple sources of bias arising from the inability to separate trial effects (such as patient selection, trial eligibility, imaging techniques and assessment schedule, and treatment locations) from treatment effect on clinical outcomes. Changes in patient population based on biologic subsetting, newer imaging technologies, the use of alternative end points, constrained resources, and the multitude of promising therapies for a given disease make randomized phase II designs, with a concurrent control arm where necessary, attractive.