On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies.

The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit-risk ratio. The statistical analysis of AEs is complicated by the fact that the follow-up times can vary between the patients included in a clinical trial. This paper takes as its focus the analysis of AE data in the presence of varying follow-up times within the benefit assessment of therapeutic interventions. Instead of approaching this issue directly and solely from an analysis point of view, we first discuss what should be estimated in the context of safety data, leading to the concept of estimands. Although the current discussion on estimands is mainly related to efficacy evaluation, the concept is applicable to safety endpoints as well. Within the framework of estimands, we present statistical methods for analysing AEs with the focus being on the time to the occurrence of the first AE of a specific type. We give recommendations which estimators should be used for the estimands described. Furthermore, we state practical implications of the analysis of AEs in clinical trials and give an overview of examples across different indications. We also provide a review of current practices of health technology assessment (HTA) agencies with respect to the evaluation of safety data. Finally, we describe problems with meta-analyses of AE data and sketch possible solutions.

Validation of the mobile serious game application Touch Surgery™ for cognitive training and assessment of laparoscopic cholecystectomy.

BACKGROUND: Touch Surgery™ (TS) is a serious gaming application for cognitive task simulation and rehearsal of key steps in surgical procedures. The aim was to establish face, content, and construct validity of TS for laparoscopic cholecystectomy (LC). Furthermore, learning curves with TS and a virtual reality (VR) trainer were compared in a randomized trial. METHODS: The performance of medical students and general surgeons was compared for all three modules of LC in TS to establish construct validity. Questionnaires assessed face and content validity. For analysis of learning curves, students were randomized to train on VR or TS first, and then switched to the other training modality. Performance data were recorded. RESULTS: 54 Surgeons and 51 medical students completed the validation study. Surgeons outperformed students with TS: patient preparation (students = 45.0 ± 19.1%; surgeons = 57.3 ± 15.2%; p < 0.001), access and laparoscopy (students = 70.2 ± 10.9%; surgeons = 75.9 ± 9.7%; p = 0.008) and LC (students = 69.8 ± 12.4%; surgeons = 77.7 ± 9.6%; p < 0.001). Both groups agreed that TS was a highly useful and realistic application. 46 students were randomized for learning curve analysis. It took them 2-4 attempts to reach a 100% score with TS. Training with TS first did not improve students' performance on the VR trainer; however, students who trained with VR first scored significantly higher in module 3 of TS. CONCLUSION: TS is an accepted serious gaming application for learning cognitive aspects of LC with established construct, face, and content validity. There appeared to be a synergy between TS and the VR trainer. Therefore, the two training modalities should accompany one another in a multimodal training approach to laparoscopy.