A joint model of persistent human papilloma virus infection and cervical cancer risk: implications for cervical cancer screening. (17th March 2015)

• Record Type: Journal Article
• Authors: Katki, Hormuzd A.; Cheung, Li C.; Fetterman, Barbara; Castle, Philip E.; Sundaram, Rajeshwari
• Is Part Of: Journal of the Royal Statistical Society. Volume 178:Number 4(2015:Oct.)
• Page Start: 903
• Page End: 923
• Publication Date: 2015-03-17
• Subjects: Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12101
• Languages: English
• ISSNs: 0964-1998

Abstract

<abstract abstract-type="main" id="rssa12101-abs-0001"> <title>Summary</title> <p>New cervical cancer screening guidelines in the USA and many European countries recommend that women are tested for human papilloma virus (HPV). To inform decisions about screening intervals, we calculate the increase in precancer or cancer risk per year of continued HPV infection. However, both time to onset of precancer or cancer and time to HPV clearance are interval censored, and onset of precancer or cancer strongly informatively censors HPV clearance. We analyse these bivariate informatively interval‐censored data by developing a novel joint model for time to clearance of HPV and time to precancer or cancer by using shared random effects, where the estimated mean duration of each woman's HPV infection is a covariate in the submodel for time to precancer or cancer. The model was fitted to data on 9553 HPV positive and negative women undergoing cervical cancer screening at Kaiser Permanente Northern California: data that were pivotal to the development of US screening guidelines. We compare the implications for screening intervals of this joint model with those from population‐average marginal models of precancer or cancer risk. In particular, after 2 years the marginal population‐average precancer or cancer risk was 5%, suggesting a 2‐year interval to control <italic>population‐average</italic> risk at 5%. In contrast, the joint model reveals that almost all women exceeding 5% individual risk in 2 years also exceeded 5% in 1 year, suggesting that a 1‐year interval is better to control <italic>individual</italic> risk at 5%. The example suggests that sophisticated risk models that can predict individual risk may have implications that are different from those of population‐average risk models that are currently used for informing medical guideline development.</p> </abstract>