Paul S. Albert

Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies

Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model (PMM) could be used to assess longitudinal biomarkers on disease early detection. In this article, we studied the discrimination and calibration performances of SREM and PMM on disease early detection through an application to ovarian cancer, where early detection using the risk of ovarian cancer algorithm (ROCA) has been evaluated. Comparisons of the above three approaches were performed via analyses of the ovarian cancer data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Discrimination was evaluated by the time‐dependent receiver operating characteristic curve and its area, while calibration was assessed using calibration plot and the ratio of observed to expected number of diseased subjects. The out‐of‐sample performances were calculated via using leave‐one‐out cross‐validation, aiming to minimize potential model overfitting. A careful analysis of using the biomarker cancer antigen 125 for ovarian cancer early detection showed significantly improved discrimination performance of PMM as compared with SREM and ROCA, nevertheless all approaches were generally well calibrated. Robustness of all approaches was further investigated in extensive simulation studies. The improved performance of PMM relative to ROCA is in part due to the fact that the biomarker measurements were taken at a yearly interval, which is not frequent enough to reliably estimate the changepoint or the slope after changepoint in cases under ROCA.

Hidden mover‐stayer model for disease progression accounting for misclassified and partially observed diagnostic tests: Application to the natural history of human papillomavirus and cervical precancer

Hidden Markov models (HMMs) have been proposed to model the natural history of diseases while accounting for misclassification in state identification. We introduce a discrete time HMM for human papillomavirus (HPV) and cervical precancer/cancer where the hidden and observed state spaces are defined by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover‐stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward‐backward algorithm, used for evaluating the E‐step within the E‐M algorithm for maximum‐likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward‐backward algorithm that considers all possible fully observed states that could have occurred across a participant's follow‐up visits. We apply our method to data from a large management trial for women with low‐grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias.

Serum concentrations of perfluoroalkyl and polyfluoroalkyl substances and risk of ovarian cancer

Abstract Background Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are persistent, widespread environmental contaminants, and some are endocrine disrupting. Studies of gynecologic cancers are limited; we evaluated ovarian cancer, a rare, often fatal malignancy. Methods This nested case-control study included 318 ovarian cancer cases and 472 individually matched female controls in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, which recruited participants aged 55-74 years from 10 US study centers (1993-2001). We looked at cases through 2016 and quantitated 8 PFAS in prediagnostic serum samples. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) for continuous (log2-transformed) and categorized PFAS concentrations by using conditional logistic regression models, implicitly adjusting for matching factors (age, center, year of random assignment, year of blood draw, race and ethnicity) and adjusting for smoking, body mass index, family history of cancer, menopausal hormone therapy and oral contraceptive use, parity, and number of freeze-thaw cycles. Results We found a positive association with ovarian cancer for a doubling in 2-(N-methyl-perfluorooctane sulfonamido) acetic acid (MeFOSAA) concentrations (OR for log2 = 1.24, 95% CI = 1.03 to 1.49) and 62% greater risk among those in the highest quartile (OR for quartile 4 vs quartile 1 = 1.62, 95% CI = 1.03 to 2.54; P for trend = .02). Perfluorooctane sulfonic acid (PFOS) was associated with increased risk (OR for log2 = 1.47, 95% CI = 1.05 to 2.06), with no quartile trend (P for trend = .79). Associations with perfluorononanoic acid (OR for log2 = 1.36, 95% CI = 0.95 to 1.95) and perfluorodecanoic acid (OR for log2 = 1.35, 95% CI = 0.94 to 1.95) were suggested, with nonmonotonic quartile trends (P for trend = .12 to .21). The MeFOSAA associations were strongest in women aged 55-59 years (OR for log 2 = 1.60, 95% CI = 1.13 to 2.27), more moderate in women aged 60-64 years (OR for log2 = 1.31, 95% CI = 0.90 to 1.90), and null among women 65 years of age and older (OR for log2 = 1.02, 95% CI = 0.73 to 1.43; P for heterogeneity = .22). Associations persisted in cases diagnosed 8 years or more after blood collection. Conclusions These findings offer novel evidence for PFAS as ovarian cancer risk factors, particularly PFOS and MeFOSAA, a PFOS precursor.