Survival analysis following oncological treatments require specific analysis techniques to account for data considerations, such as failure to observe the time of event, patient withdrawal, loss to follow-up, and differential follow up. These techniques can include Kaplan-Meier and Cox proportional hazard analyses. However, studies do not always report overall survival (OS), disease-free survival (DFS), or cancer recurrence using hazard ratios, making the synthesis of such oncologic outcomes difficult. We propose a hierarchical utilization of methods to extract or estimate the hazard ratio to standardize time-to-event outcomes so that study inclusion into meta-analyses can be maximized. We also provide proof-of concept results from a statistical analysis that compares OS, DFS, and cancer recurrence for robotic surgery to open and non-robotic minimally invasive surgery. In our example, use of the proposed methodology would allow for the increase in data inclusion from 108 hazard ratios reported to 240 hazard ratios reported or estimated, resulting in an increase of 122%. While there are publications summarizing the motivation for these analyses, and comprehensive papers describing strategies to obtain estimates from published time-dependent analyses, we are not aware of a manuscript that describes a prospective framework for an analysis of this scale focusing on the inclusion of a maximum number of publications reporting on long-term oncologic outcomes incorporating various presentations of statistical data.
Background: We aim to evaluate how new robotic skills are acquired and retained by having participants train and retest using exercises on the robotic platform. We hypothesized that participants with a 3month break from the robotic platform will have less learning decay and increased retention compared with those with a 6-month break.Methods: This was a prospective randomized trial in which participants voluntarily enrolled and completed an initial training phase to reach proficiency in 9 robot simulator exercises. They were then instructed to refrain from practicing until they retested either 3 or 6 months later. This study was completed at an academic medical center within the general surgery department. Participants were medical students, and junior-level residents with minimal experience in robotic surgery were enrolled. A total of 27 enrolled, and 13 participants completed the study due to attrition.Results: Overall, intragroup analysis revealed that participants performed better in their retest phase compared with their initial training in terms of attempts to reach proficiency, time for completion, penalty score, and overall score. Specifically, during the first attempt in the retesting phase, the 3-month group did not deviate far from their final attempt in the training phase, whereas the 6-month group experienced significantly worse time to complete and overall score in interrupted suturing {[−4 (−18 to 20) seconds vs. 109 (55 to 118) seconds, P = 0.02] [−1.3 (−8 to 1.9) vs. −18.9 (−19.5 to (−15.0)], P = 0.04} and 3-arm relay {[3 (−4 to 23) seconds vs. 43 (30 to 50) seconds, P = 0.02] [0.4 (−4.6 to 3.1) vs. −24.8 (−30.6 to (−20.3)], P = 0.01] exercises. In addition, the 6month group had a significant increase in penalty score in retesting compared with the 3-month group, which performed similarly to their training phase [3.3 (2.7 to 3.3) vs. 0 (−0.8 to 1.7), P = 0.03].Conclusions: This study identified statistically significant differences in learning decay, skills retention, and proficiency between 3-month and 6-month retesting intervals on a robotic simulation platform.
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