Analysis of Interval Censored Data Using a Longitudinal Biomarker

In many medical studies, interest focuses on studying the effects of potential risk factors on some disease events, where the occurrence time of disease events may be defined in terms of the behavior of a biomarker. For example, in diabetic studies, diabetes is defined in terms of fasting plasma glucose being 126 mg/dl or higher. In practice, several issues complicate determining the exact time-to-disease occurrence. First, due to discrete study follow-up times, the exact time when a biomarker crosses a given threshold is unobservable, yielding so-called interval censored events. Second, most biomarker values are subject to measurement error due to imperfect technologies, so the observed biomarker values may not reflect the actual underlying biomarker levels. Third, using a common threshold for defining a disease event may not be appropriate due to patient heterogeneity. Finally, informative diagnosis and subsequent treatment outside of observational studies may alter observations after the diagnosis. It is well known that the complete case analysis excluding the externally diagnosed subjects can be biased when diagnosis does not occur completely at random. To resolve these four issues, we consider a semiparametric model for analyzing threshold-dependent time-to-event defined by extreme-value-distributed biomarkers. First, we propose a semiparametric marginal model based on a generalized extreme value distribution. By assuming the latent error-free biomarkers to be non-decreasing, the proposed model implies a class of proportional hazards models for the time-to-event defined for any given threshold value. Second, we extend the marginal likelihood to a pseudo-likelihood by multiplying the likelihoods over all observation times. Finally, to adjust for externally diagnosed cases, we consider a weighted pseudo-likelihood estimator by incorporating inverse probability weights into the pseudo-likelihood by assuming that external diagnosis depends on observed data rather than unobserved data. We estimate the three model parameters using the nonparametric EM, pseudo-EM and weighted-pseudo-EM algorithm, respectively. Herein, we theoretically investigate the models and estimation methods. We provide a series of simulations, to test each model and estimation method, comparing them against alternatives. Consistency, convergence rates, and asymptotic distributions of estimators are investigated using empirical process techniques. To show a practical implementation, we use each model to investigate data from the ARIC study and the diabetes ancillary study of the ARIC study.Doctor of Philosoph

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA

Effect of changes in monitor unit rate and energy on dose rate of total marrow irradiation based on Linac volumetric arc therapy

Background This study set out to evaluate the effect of dose rate on normal tissues (the lung, in particular) and the variation in the treatment efficiency as determined by the monitor unit (MU) and energy applied in Linac-based volumetric arc therapy (VMAT) total marrow irradiation (TMI). Methods Linac-based VMAT plans were generated for the TMI for six patients. The planning target volume (PTV) was divided into six sub-volumes, each of which had their own isocenter. To examine the effect of the dose rate and energy, a range of MU rates (40, 60, 80, 100, 300, and 600 MU/min) were selected for 6, 10, and 15 MV. All the plans were verified by portal dosimetry. Results The dosimetric parameters for the target and normal tissue were consistent in terms of the energy and MU rate. The beam-on time was changed from 59.6 to 6 min for 40 and 600 MU/min. When 40 MU/min was set for the lung, the dose rate delivered to the lung was less than 6 cGy/min (that is, 90%), while the beam-on time was approximately 10 min. The percentage volume of the lung receiving 20 cGy/min was 1.47, 3.94, and 6.22% at 6, 10, and 15 MV, respectively. However, for 600 MU/min, the total lung volume received over 6 cGy/min regardless of the energy, and over 20 cGy/min for 10 and 15 MV (i.e., 54.4% for 6 MV). Conclusions In TMI treatment, reducing the dose rate administered to the lung can decrease the incidence of pulmonary toxicity. To reduce the probability of normal tissue complications, the selection of the lowest MU rate is recommended for fields including the lung. To minimize the total treatment time, the maximum MU rate can be applied to other fields.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (no. 2017M2A2A7A02020641) and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (no. HA 16C0025)

Lung function decline over 25 years of follow-up among black and white adults in the ARIC study cohort

Interpretation of longitudinal information about lung function decline from middle to older age has been limited by loss to follow-up that may be correlated with baseline lung function or the rate of decline. We conducted these analyses to estimate age-related decline in lung function across groups of race, sex, and smoking status while accounting for dropout from the Atherosclerosis Risk in Communities Study

Novel Metabolic Markers for the Risk of Diabetes Development in American Indians

OBJECTIVETo identify novel metabolic markers for diabetes development in American Indians.RESEARCH DESIGN AND METHODSUsing an untargeted high-resolution liquid chromatography–mass spectrometry, we conducted metabolomics analysis of study participants who developed incident diabetes (n = 133) and those who did not (n = 298) from 2,117 normoglycemic American Indians followed for an average of 5.5 years in the Strong Heart Family Study. Relative abundances of metabolites were quantified in baseline fasting plasma of all 431 participants. Prospective association of each metabolite with risk of developing type 2 diabetes (T2D) was examined using logistic regression adjusting for established diabetes risk factors.RESULTSSeven metabolites (five known and two unknown) significantly predict the risk of T2D. Notably, one metabolite matching 2-hydroxybiphenyl was significantly associated with an increased risk of diabetes, whereas four metabolites matching PC (22:6/20:4), (3S)-7-hydroxy-2′,3′,4′,5′,8-pentamethoxyisoflavan, or tetrapeptides were significantly associated with decreased risk of diabetes. A multimarker score comprising all seven metabolites significantly improved risk prediction beyond established diabetes risk factors including BMI, fasting glucose, and insulin resistance.CONCLUSIONSThe findings suggest that these newly detected metabolites may represent novel prognostic markers of T2D in American Indians, a group suffering from a disproportionately high rate of T2D

Comparison of a Stationary Digital Breast Tomosynthesis System to Magnified 2D Mammography Using Breast Tissue Specimens

RATIONAL AND OBJECTIVES: The objective of this study was to compare the stationary digital breast tomosynthesis (s-DBT) system to a conventional mammography system in a study of breast specimens. Radiologist evaluation of image quality was assessed in a reader study. This study represents the first human tissue imaging with the novel carbon nanotube-based s-DBT device. MATERIALS AND METHODS: Thirty-nine patients, with known breast lesions (Breast Imaging Reporting and Data System 4 or 5) by conventional mammography and scheduled for needle localization biopsy, were recruited under an institutional review board-approved protocol. Specimen images were obtained using a two-dimensional (2D) mammography system with a ×1.8 magnification factor and an s-DBT system without a high magnification factor. A reader study was performed with four breast fellowship-trained radiologists over two separate sessions. Malignancy scores were recorded for both masses and microcalcifications (MCs). Reader preference between the two modalities for MCs, masses, and surgical margins was recorded. RESULTS: The s-DBT system was found to be comparable to magnified 2D mammography for malignancy diagnosis. Readers preferred magnified 2D mammography for MC visualization (P < .05). However, readers trended toward a preference for s-DBT with respect to masses and surgical margin assessment. CONCLUSIONS: Here, we report on the first human data acquired using a stationary digital breast tomosynthesis system. The novel s-DBT system was found to be comparable to magnified 2D mammography imaging for malignancy diagnosis. Given the trend of preference for s-DBT over 2D mammography for both mass visibility and margin assessment, s-DBT could be a viable alternative to magnified 2D mammography for imaging breast specimens

Mediators of Black-White inequities in cardiovascular mortality among survivors of 18 cancers in the USA.

This study aims to quantify Black-White inequities in cardiovascular disease (CVD) mortality among US survivors of 18 adult-onset cancers and the extent to which these inequities are explained by differences in socio-economic and clinical factors.Survivors of cancers diagnosed at ages 20-64 years during 2007-16 were identified from 17 Surveillance, Epidemiology and End Results registries. Associations between race and CVD mortality were examined using proportional hazards models. Mediation analyses were performed to quantify the contributions of potential mediators, including socio-economic [health insurance, neighbourhood socio-economic status (nSES), rurality] and clinical (stage, surgery, chemotherapy, radiotherapy) factors.Among 904 995 survivors, 10 701 CVD deaths occurred (median follow-up, 43 months). Black survivors were more likely than White survivors to die from CVD for all 18 cancers with hazard ratios ranging from 1.30 (95% CI = 1.15-1.47) for lung cancer to 4.04 for brain cancer (95%&nbsp;CI = 2.79-5.83). The total percentage mediations (indirect effects) ranged from 24.8% for brain (95%&nbsp;CI=-5.2-59.6%) to 99.8% for lung (95%&nbsp;CI = 61.0-167%) cancers. Neighbourhood SES was identified as the strongest mediator for 14 cancers with percentage mediations varying from 25.0% for kidney cancer (95%&nbsp;CI = 14.1-36.3%) to 63.5% for lung cancer (95%&nbsp;CI = 36.5-108.7%). Insurance ranked second for 12 cancers with percentage mediations ranging from 12.3% for leukaemia (95%&nbsp;CI = 0.7-46.7%) to 31.3% for thyroid cancer (95%&nbsp;CI = 10.4-82.7%).Insurance and nSES explained substantial proportions of the excess CVD mortality among Black survivors. Mitigating the effects of unequal access to care and differing opportunities for healthy living among neighbourhoods could substantially reduce racial inequities in CVD mortality among cancer survivors