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Objectives Cognitive behavioral therapy for insomnia (CBT-I) will be compared with hormone replacement therapy (HRT) and sleep hygiene (active control treatment). Background The late menopausal transition is a vulnerable hormonal phase for women, where sleep disorders and vasomotor symptoms affect one out of three women. HRT is probably the most commonly used method to treat menopausal symptoms, including sleep problems. However, CBT-I is considered the treatment of choice for insomnia in adults in general. Despite this fact, there are only a few studies that have examined the effect of CBT-I in peri- and postmenopause and only one observational study comparing CBT-I and HRT for perimenopausal insomnia. Therefore, this study aims to compare the efficacy of HRT and CBT-I on subjective and objective sleep quality. Methods Fifty-four late perimenopausal women will be randomly assigned to receive psychotherapist-led CBT-I, HRT (transdermal estradiol 1.5 mg/d and oral micronized progesterone 200 mg/day), or sleep hygiene (active control). Sleep quality will be continually assessed for 3 months, using validated questionnaires and an in-ear EEG device. Moreover, potential changes in the biofunctional status and age (27) and vasomotor symptoms will be assessed. Discussion The goal is to enable evidence-based treatment decisions for affected women to close the menopausal medical care gap and improve women’s quality of life. Trial registration {2a, 2b} The study has been registered at ClinicalTrials.gov (Identifier: NCT06497894) on 11 July 2024. URL: ( https://clinicaltrials.gov/ct2/show/NCT06497894 ). Protocol version and trial status {3} Protocol version 3.1 (30. April 2024). Recruitment has not started yet (15. August 2024). The recruitment is planned to begin on 01. November 2024, with an estimated completion date of 01. April 2026.

Risk calculators based on statistical and/or mechanistic models have flourished and are increasingly available for a variety of diseases. However, in the day-to-day practice, their usage may be hampered by missing input variables. Certain measurements needed to calculate disease risk may be difficult to acquire, e.g. because they necessitate blood draws, and may be systematically missing in the population of interest. We compare several deterministic and probabilistic imputation approaches to surrogate predictions from risk calculators while accounting for uncertainty due to systematically missing inputs. The considered approaches predict missing inputs from available ones. In the case of probabilistic imputation, this leads to probabilistic prediction of the risk. We compare the methods using scoring techniques for forecast evaluation, with a focus on the Brier and CRPS scores. We also discuss the classification of patients into risk groups defined by thresholding predicted probabilities. While the considered procedures are not meant to replace fully-informed risk calculations, employing them to get first indications of risk distribution in the absence of at least one input parameter may find useful applications in medical practice. To illustrate this, we use the SCORE2 risk calculator for cardiovascular disease and a data set including medical data from 359 women, obtained from the gynecology department at the Inselspital in Bern, Switzerland. Using this data set, we mimic the situation where some input parameters, blood lipids and blood pressure, are systematically missing and compute the SCORE2 risk by probabilistic imputation of the missing variables based on the remaining input variables. We compare this approach to established imputation techniques like MICE by means of scoring rules and visualize in turn how probabilistic imputation can be used in sample size considerations.