Risk Prediction for Nonsurgical Premature Menopause in Childhood Cancer Survivors

  • Author / Creator
    Clark, Rebecca A
  • Childhood cancer survivorship has increased drastically over the previous several decades, consequently increasing the frequency of chronic conditions in survivors. Female childhood cancer survivors are at an increased risk of developing nonsurgical premature menopause (NSPM) due to toxicities from their treatment. NSPM occurs when ovarian function is retained for at least 5 years following cancer diagnosis, but menopause develops naturally before age 40. Such a condition can negatively impact quality of life and reduce potential reproductive years. The literature details risk factors including an older age at cancer diagnosis, and treatment with high doses of alkylating agents and radiation. In order to aid physicians, patients and their families have informed discussions regarding fertility preservation, I aimed to develop prediction algorithms of the absolute risk an individual has of developing NSPM. The Childhood Cancer Survivor Study cohort was the primary data source for this project. Due to the presence of both stratified random sampling and participant loss to follow-up within the cohort, I initially investigated methods for combining sampling and censoring weights in the estimation of model accuracy measures to aid in model evaluation. I designed and implemented four simulation studies, varying the relationship between sampling design, censoring distribution and risk score distribution, and assessed weighting scenarios with distinct combinations of censoring and sampling weights. Depending on the study setting, different weighting scenarios gave reasonable estimates, and ignoring or inadequately accounting for weights resulted in biased accuracy estimates.Candidate risk prediction models were developed on a training set of 4,054 observations from the Childhood Cancer Survivor Study cohort using a time-specific logistic regression model with competing risks (TLR-CR), a Fine-Gray regression (FGR) model and a random survival forest model with competing risks (RSF-CR). Model performance and accuracy were measured using the time-specific area under the ROC curve (AUCt), the time-specific average positive predictive value (APt), and calibration curves on both the training set and an internal validation set of 1,454 observations.Model accuracy values and curves were presented for 15 years post cancer diagnosis as an illustration of overall model performance. All three models performed similarly on the training set. The estimated AUCt values decreased when internal validation was conducted; however APt values were still larger than the event rate. The APt / Event Rate ratio for the TLR-CR model increased from the training set performance. AUCt and APt values on the test set calculated over 10-20 years post cancer diagnosis displayed similar findings. The models were well calibrated for low risk patients, however only the TLR-CR model was consistently well calibrated for high risk patients on both datasets. Moving forward, model performance on individuals with clinically verified ovarian status will be assessed through validation on an external cohort. The future practical application of the risk estimates as a risk scoring system aims to have a positive impact on the quality of life of survivors well into their adulthood.

  • Subjects / Keywords
  • Graduation date
    Spring 2019
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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