Missing data present a perennial challenge in scientific research, potentially undermining the validity of conclusions if not addressed rigorously. The analysis of missing data encompasses a broad ...

This manuscript makes a valuable contribution to the concept of fragility of meta-analyses via the so-called 'ellipse of insignificance for meta-analyses' (EOIMETA). The strength of evidence is solid, ...

A significant number of patient records in large cancer registries are missing data, which can have an impact on real-world data studies. Investigators found a high prevalence of missing data within a ...

Data is almost always incomplete. Patients drop out of clinical trials and survey respondents skip questions; schools fail to report scores, and governments ignore elements of their economies. When ...

There are data about practically everything these days, and they can be used to try to answer any number of questions. Do clinical trials really show a drug works? Can surveys really signal who’s ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

A new online tool predicts how much news coverage a person would be "worth" if they went missing. The analysis highlights "missing white woman syndrome," where certain races and genders get more ...