Study Objectives In a randomized controlled non-inferiority trial, we compared face-to-face and telemedicine delivery (via the AASM SleepTM platform) of CBT for insomnia for improving insomnia/sleep and daytime functioning at post-treatment and 3-month follow-up. A secondary objective compared the modalities on treatment credibility, satisfaction, and therapeutic alliance. Methods Sixty-five adults with chronic insomnia (46 women, 47.2 ± 16.3 years of age) were randomized to 6 sessions of CBT for insomnia delivered individually via AASM SleepTM (n=33, CBT-TM) or face-to-face (n=32, CBT-F2F). Participants completed sleep diaries, the Insomnia Severity Index (ISI), and daytime functioning measures at pre-treatment, post-treatment, and 3-month follow-up. Treatment credibility, satisfaction, and therapeutic alliance were compared between treatment modalities. The ISI was the primary non-inferiority outcome. Results Based on a non-inferiority margin of 4 points on the ISI and, after adjusting for confounders, CBT-TM was non-inferior to CBT-F2F at post-treatment (β = 0.54, SE=1.10, 95% CI -1.64 to 2.72) and follow-up (β = 0.34, SE=1.10, 95% CI -1.83 to 2.53). Daytime functioning measures, except the physical composite scale of the SF-12, were significantly improved at post-treatment and follow-up, with no difference between treatment formats. CBT-TM sessions were, on average, nearly 10 minutes shorter, yet participant ratings of therapeutic alliance were similar to CBT-F2F. Conclusions Telemedicine delivery of CBT for insomnia is not inferior to face-to-face for insomnia severity and yields similar improvements on other sleep and daytime functioning outcomes. Further, telemedicine allows for more efficient treatment delivery while not compromising therapeutic alliance.

This cohort study uses Bayesian multistate models for a unified statistical approach to compare the association of surgery and radiotherapy with both metastatic clinical failure and survival in men with localized prostate cancer and develops an online calculator for individualized treatment-specific outcome prediction.

Purpose Prior studies suggest a need for greater clarity about provider roles in team-based cancer care. However, little is known about patient’s preferences for which providers handle their care needs after primary cancer treatment. Methods We surveyed women newly diagnosed with stages 0-II breast cancer who were treated in 2014–15 as reported to the Georgia and Los Angeles SEER registries (N=2,372, 68% response rate). Patient preferences for which provider handles the following care needs after treatment were ascertained: follow-up mammograms, screening for other cancers, general preventive care, and comorbidity management. The associations between patient demographic factors with preferences for provider roles (Oncology-directed care vs. primary care provider (PCP)-directed) were assessed using multivariable logistic regression. Results The majority of women preferred that their PCPs handle their general preventive care (79%) and comorbidity care (84%), but a notable minority of women preferred their oncologists direct this care (21% and 16%). Minority women (black and Asian vs. white) and women with a high school education or less (vs. college grad or more) had a greater odds of preferring oncology-directed care (vs. PCP-directed) for their general preventive care (black OR: 2.01, 95%CI: 1.43, 2.82; Asian OR: 1.74, 95%CI: 1.13, 2.69; ≤ high school OR: 1.51, 95%CI: 1.10, 2.08). Similar variation existed for comorbidity care. Conclusion In this sample, minority women and those with less education more often preferred oncologists direct aspects of their care after breast cancer treatment that are normally delivered by a PCP. Efforts to clarify provider roles in survivorship care to patients may be effective in improving team-based cancer care.

Summary Joint models for longitudinal and time‐to‐event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customisation and some available extensions, such as competing risks models. The software implementations are compared and contrasted through extensive simulation, highlighting their strengths and weaknesses. Data from an ongoing trial on adrenal cancer patients are used to study different nuances of software fitting on a practical example.