Statistical Considerations for Bias and Protocol Deviation in Medical Device Pivotal Clinical Study

Re-ordered fuzzy conformance checking for uncertain clinical records

Modern hospitals implement clinical pathways to standardize patients' treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts' knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.

Compliance of Clinical Trial Protocols for Foods with Function Claims (FFC) in Japan: Consistency between Clinical Trial Registrations and Published Reports

Background: A new type of foods with a health claims notification system, the Foods with Function Claims (FFC), was introduced in Japan in April 2015. This cross-sectional study sought to clarify compliance of clinical trial protocols reported as the scientific basis of efficacy in the FFC system. Methods: All articles based on clinical trials published on the Consumer Affairs Agency website from 1 July 2018 to 30 June 2021 were reviewed. Items assessed included first author characteristics (for-profit or academia), journal name, year published, journal impact factor in 2020, article language, name of clinical trial registration (CTR), and seven compliance items (Title: T, Participant: P, Intervention: I, Comparison: C, Outcome: O, Study design: S, and Institutional Review Board, IRB). Among studies that conducted CTR, consistency with these seven compliance items was evaluated. Results: Out of 136 studies that met all inclusion criteria, 103 (76%) performed CTR, and CTR was either not performed or not specified for 33 (24%). Compliance between the protocol and the text was high (≥96%) for items P and S, but considerably lower for items T, I, C, O, and IRB (52%, 15%, 13%, 69%, and 27%, respectively). Furthermore, 43% of protocols did not include functional ingredients or food names in items T or I. The total score was 3.7 ± 1.1 pts (out of 7). Conclusions: Some CTs had no protocol registration, and even registered protocols were suboptimal in transparency. In addition to selective reporting, a new problem identified was that the content of the intervention (test food) was intentionally concealed.

Biostatistical and Logistical Considerations in the Development of Basket and Umbrella Clinical Trials

Oncology clinical trials are undergoing transformation to evaluate targeted therapies addressing a wider variety of biologically defined cancer subgroups. Multiarm basket and umbrella trials conducted under master protocols have become more prominent mechanisms for the clinical evaluation of promising new biologically driven anticancer therapies that are integral to precision oncology medicine. These new trial designs permit efficient clinical evaluation of multiple therapies in a variety of histologically and biologically defined cancers. These complex trials require extensive planning and attention to many factors, including choice of biomarker assay platform, mechanism for processing clinicopathologic and biomarker data to assign patients to substudies, and statistical design, monitoring, and analysis of substudies. Trial teams have expanded to include expertise in the interface between biology, clinical oncology, bioinformatics, and statistics. Strategies for the design, conduct, and analysis of these complex trials will continue to evolve to meet new challenges and opportunities in precision oncology medicine.