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Background: Survivors of Hodgkin lymphoma (HL) in childhood have an increased risk of subsequent malignant neoplasms (SMNs). Herein, the authors extended the follow-up of a previously reported Late Effects Study Group cohort and identified patients at highest risk for SMNs to create evidence for risk-based screening recommendations.Methods: The standardized incidence ratio was calculated using rates from the Surveillance, Epidemiology, and End Results program as a reference. The risk of SMN was estimated using proportional subdistribution hazards regression. The cohort included 1136 patients who were diagnosed with HL before age 17 years between 1955 and 1986. The median length of follow-up was 26.6 years.Results: In 162 patients, a total of 196 solid SMNs (sSMNs) were identified. Compared with the general population, the cohort was found to be at a 14-fold increased risk of developing an sSMN (95% confidence interval, 12.0-fold to 16.3-fold). The cumulative incidence of any sSMN was 26.4% at 40 years after a diagnosis of HL. Risk factors for breast cancer among females were an HL diagnosis between ages 10 years and 16 years and receipt of chest radiotherapy. Males treated with chest radiotherapy at age <10 years were found to be at highest risk of developing lung cancer. Survivors of HL who were treated with abdominal/pelvic radiotherapy and high-dose alkylating agents were found to be at highest risk of developing colorectal cancer and females exposed to neck radiotherapy at age <10 years were at highest risk of thyroid cancer. By age 50 years, the cumulative incidence of breast, lung, colorectal, and thyroid cancer was 45.3%, 4.2%, 9.5%, and 17.3%, respectively, among those at highest risk.Conclusions: Survivors of childhood HL remain at an increased risk of developing sSMNs. In the current study, subgroups of survivors of HL at highest risk of specific sSMNs were identified, and evidence for screening provided. [ABSTRACT FROM AUTHOR]

Assessment of benefit–risk for treatments is usually complex and involves trade-offs between multiple, often conflicting, preferences. Although discrete choice experiments are increasingly used in health outcomes research to assess trade-offs in preferences, their usage in benefit–risk assessment so far is fairly limited. This is primarily because of the high cognitive burden to assess multiple attributes and the requirement for a large pool of respondents. The hierarchical Bayes benefit–risk model using choice based conjoint that we propose drastically reduces the cognitive burden as each respondent only needs to evaluate a small fraction of preference questions, each of which compares a single pair of attributes. This method also leverages the Bayesian framework to borrow strength for analysis with a limited number of respondents. This article illustrates both a simulated experiment and a pilot experiment incorporating experts' preferences in oncology. Ultimately, patients are the most important voice in the benefit–risk balance. Therefore, we propose an augmented model to obtain a more precise estimate of benefit–risk preferences based on patients' characteristics. [ABSTRACT FROM AUTHOR]