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Objective: To compare the representation of women and racial minorities among otolaryngology residents and faculty to other surgical specialties. Methods: Information from 2016 regarding female and minority representation among medical school graduates, otolaryngology applicants, otolaryngology residents, otolaryngology faculty and residents, and faculty in other surgical specialties was obtained from the publicly available registries from the American Medical Association and the American Association of Medical Colleges. The data obtained was used to explore the differences between the various stages of training in otolaryngology and to compare the female and minority diversity of otolaryngology residents with residents in other surgical specialties. Results: Women and African Americans were underrepresented at the resident level compared with their level of representation as medical school graduates. Women were underrepresented in otolaryngology resident applicants (P < .001), but equally represented between otolaryngology residency applicants and residents (P = .582). African Americans were equally represented between medical school graduates and otolaryngology resident applicants (P = .871), but underrepresented in otolaryngology residents (P < .001). Asian Americans and Hispanics were underrepresented among otolaryngology faculty compared with their representation in otolaryngology residency programs (P < .001, P < .001, respectively). Otolaryngology has the lowest percentage of African-American residents and faculty compared to other surgical specialties. The representation of women in otolaryngology residencies is higher than most surgical specialties but worse than general surgery, integrated plastics, and medical school graduates. Conclusion: Otolaryngology lags behind other surgical specialties in representation of minorities and women. Continued efforts should be made to increase diversity in the field of otolaryngology, especially in regard to underrepresented minorities.

Variable selection or feature extraction is fundamental to identify important risk factors from a large number of covariates and has applications in many fields. In particular, its applications in failure time data analysis have been recognized and many methods have been proposed for right-censored data. However, developing relevant methods for variable selection becomes more challenging when one confronts interval censoring that often occurs in practice. In this article, motivated by an Alzheimer’s disease study, we develop a variable selection method for interval-censored data with a general class of semiparametric transformation models. Specifically, a novel penalized expectation–maximization algorithm is developed to maximize the complex penalized likelihood function, which is shown to perform well in the finite-sample situation through a simulation study. The proposed methodology is then applied to the interval-censored data arising from the Alzheimer’s disease study mentioned above.

Variable selection has been discussed under many contexts and especially, a large literature has been established for the analysis of right‐censored failure time data. In this article, we discuss an interval‐censored failure time situation where there exist two sets of covariates with one being low‐dimensional and having possible nonlinear effects and the other being high‐dimensional. For the problem, we present a penalized estimation procedure for simultaneous variable selection and estimation, and in the method, Bernstein polynomials are used to approximate the involved nonlinear functions. Furthermore, for implementation, a coordinate‐wise optimization algorithm, which can accommodate most commonly used penalty functions, is developed. A numerical study is performed for the evaluation of the proposed approach and suggests that it works well in practical situations. Finally the method is applied to an Alzheimer's disease study that motivated this investigation.

Highlights Almost half of cancer survivors in this survey had not communicated electronically with clinicians over the past 12 months. Using technology for health-related purposes was a stronger predictor of electronic communication than demographic characteristics and past communication experiences with clinicians. Implications of the findings for facilitating transitions to telehealth in cancer care since the pandemic are discussed.

A block copolymer containing a liquid crystalline polymer and a poly(ionic liquid) self-assembles and can be used as a solid electrolyte.

This paper discusses regression analysis of the failure time data arising from case‐cohort periodic follow‐up studies, and one feature of such data, which makes their analysis much more difficult, is that they are usually interval‐censored rather than right‐censored. Although some methods have been developed for general failure time data, there does not seem to exist an established procedure for the situation considered here. To address the problem, we present a semiparametric regularized procedure and develop a simple algorithm for the implementation of the proposed method. In addition, unlike some existing procedures for similar situations, the proposed procedure is shown to have the oracle property, and an extensive simulation is conducted and it suggests that the presented approach seems to work well for practical situations. The method is applied to an HIV vaccine trial that motivated this study.