ABSTRACT
PURPOSE: The Spanish Society of Medical Oncology (SEOM) has carried out a study to analyse the conditions of access to oncology drugs in clinical practice in Spain. For the first time, the access of predictive biomarkers has also been analyzed. METHODS: A questionnaire was sent to 146 hospitals in Spain to collect information on the process of approval of 11 oncology drugs of an unquestionable clinical benefit and five predictive biomarkers of mandatory determination for specific treatments. RESULTS: Results highlight the still existing differences in the access of oncology drugs, as well as the newly identified differences in the access to predictive biomarkers between Autonomous Communities (AACC) in Spain, as well as between different hospitals within the same Autonomous Community. Conclusions The SEOM considers it necessary to reduce the differences identified, increase homogeneity, and improve conditions of access to oncology drugs and biomarkers, and makes proposals to address these issues.
Subject(s)
Antineoplastic Agents/supply & distribution , Biomarkers, Tumor/analysis , Drug Approval , Medical Oncology , Societies, Medical , Clinical Decision-Making , Humans , Neoplasms/drug therapy , Neoplasms/mortality , Spain , Surveys and Questionnaires , Time FactorsABSTRACT
Observational studies using registry data make it possible to compile quality information and can surpass clinical trials in some contexts. However, data heterogeneity, analytical complexity, and the diversity of aspects to be taken into account when interpreting results makes it easy for mistakes to be made and calls for mastery of statistical methodology. Some questionable research practices that include poor analytical data management are responsible for the low reproducibility of some results; yet, there is a paucity of information in the literature regarding specific statistical pitfalls of cancer studies. In addition to proposing how to avoid or solve them, this article seeks to expose ten common problematic situations in the analysis of cancer registries: convenience, dichotomization, stratification, regression to the mean, impact of sample size, competing risks, immortal time and survivor bias, management of missing values, and data dredging.