Development of a bayesian toxo-equivalence model between docetaxel and paclitaxel.

Members of the taxane class of chemotherapies, staples of cancer treatment since the 1990s, can induce chemotherapy-induced peripheral neuropathy (CIPN), a potentially irreversible outcome related to cumulative exposure. Switching between taxanes is often clinically necessary; however, different taxanes have different efficacies, toxicities, and dosing strategies, necessitating an evidence-based schema focused on toxicity. We performed a systematic review and meta-analysis of the literature on docetaxel and paclitaxel, extracting cumulative dose, rates of CIPN, and subject demographics, thereby establishing their dose-toxo-equivalence relationship through a Bayesian meta-analysis model, calculating doses of the two drugs that are expected to have comparable rates of CIPN, along with credible intervals. Our final model, based on 169 studies, produces credible interval widths that provide guidance within one treatment cycle. In practice, this model provides a framework under which oncologists can make treatment switching and dosing decisions, hopefully reducing patient risk of CIPN.

Seven decades of chemotherapy clinical trials: a pan-cancer social network analysis.

Clinical trials establish the standard of cancer care, yet the evolution and characteristics of the social dynamics between the people conducting this work remain understudied. We performed a social network analysis of authors publishing chemotherapy-based prospective trials from 1946 to 2018 to understand how social influences, including the role of gender, have influenced the growth and development of this network, which has expanded exponentially from fewer than 50 authors in 1946 to 29,197 in 2018. While 99.4% of authors were directly or indirectly connected by 2018, our results indicate a tendency to predominantly connect with others in the same or similar fields, as well as an increasing disparity in author impact and number of connections. Scale-free effects were evident, with small numbers of individuals having disproportionate impact. Women were under-represented and likelier to have lower impact, shorter productive periods (P < 0.001 for both comparisons), less centrality, and a greater proportion of co-authors in their same subspecialty. The past 30 years were characterized by a trend towards increased authorship by women, with new author parity anticipated in 2032. The network of cancer clinical trialists is best characterized as strategic or mixed-motive, with cooperative and competitive elements influencing its appearance. Network effects such as low centrality, which may limit access to high-profile individuals, likely contribute to the observed disparities.

Indication of Measures of Uncertainty for Statistical Significance in Abstracts of Published Oncology Trials: A Systematic Review and Meta-analysis.

IMPORTANCE: There is growing consensus that reliance on P values, particularly a cutoff level of .05 for statistical significance, is a factor in the challenges in scientific reproducibility. Despite this consensus, publications describing clinical trial results with P values near .05 anecdotally use declarative statements that do not express uncertainty. OBJECTIVES: To quantify uncertainty expression in abstracts describing the results of cancer randomized clinical trials (RCTs) with P values between .01 and .10 and examine whether trial features are associated with uncertainty expression. DATA SOURCES: A total of 5777 prospective trials indexed on HemOnc.org, as of September 15, 2019. STUDY SELECTION: Two-arm RCTs with a superiority end point with P values between .01 and .10. DATA EXTRACTION AND SYNTHESIS: Abstracts were evaluated based on an uncertainty expression algorithm. Ordinal logistic regression modeling with multiple imputation was performed to identify whether characteristics of study design, results, trial authors, and context P values were normalized by dividing by prespecified α value. MAIN OUTCOMES AND MEASURES: Uncertainty expression in abstracts as determined by the algorithm and its association with trial and publication characteristics. RESULTS: Of 5777 trials screened, 556 met analysis criteria. Of these, 222 trials (39.9%) did not express uncertainty, 161 trials (29.0%) expressed some uncertainty, and 173 trials (31.1%) expressed full uncertainty. In ordinal logistic regression with multiple imputation, trial features with statistically significant associations with uncertainty expression included later year of publication (odds ratio [OR], 1.70; 95% CI, 1.24-2.32; P < .001), normalized P value (OR, 1.36; 95% CI, 1.11-1.67; P = .003), noncooperative group studies (OR, 1.72; 95% CI, 1.12-2.63; P = .01), and reporting an end point other than overall survival (OR, 1.41; 95% CI, 1.01-1.96; P = .047). Funding source, number of authors, journal impact tier, author nationality, study of unapproved drugs, abstract word count, whether the marginal end point was a primary or coprimary end point, and effect size (in subgroup analysis) did not have statistically significant associations with uncertainty expression. CONCLUSIONS AND RELEVANCE: Published oncology articles with marginally significant results may often incompletely convey uncertainty. Although it appears that more uncertainty is expressed in recent abstracts, full uncertainty expression remains uncommon, and seemingly is less common when reporting overall survival, results with P values lower than α levels, and cooperative group studies.