Semiparametric estimation for joint modeling of colorectal cancer risk and functional biomarkers measured with errors

A joint logistic regression and covariate-adjusted continuous-time Markov chain model

The use of longitudinal measurements to predict a categorical outcome is an increasingly common goal in research studies. Joint models are commonly used to describe two or more models simultaneously by considering the correlated nature of their outcomes and the random error present in the longitudinal measurements. However, there is limited research on joint models with longitudinal predictors and categorical cross-sectional outcomes. Perhaps the most challenging task is how to model the longitudinal predictor process such that it represents the true biological mechanism that dictates the association with the categorical response. We propose a joint logistic regression and Markov chain model to describe a binary cross-sectional response, where the unobserved transition rates of a two-state continuous-time Markov chain are included as covariates. We use the method of maximum likelihood to estimate the parameters of our model. In a simulation study, coverage probabilities of about 95%, standard deviations close to standard errors, and low biases for the parameter values show that our estimation method is adequate. We apply the proposed joint model to a dataset of patients with traumatic brain injury to describe and predict a 6-month outcome based on physiological data collected post-injury and admission characteristics. Our analysis indicates that the information provided by physiological changes over time may help improve prediction of long-term functional status of these severely ill subjects. Copyright © 2017 John Wiley & Sons, Ltd.

Modeling clinical outcome using multiple correlated functional biomarkers: A Bayesian approach

In some biomedical studies, biomarkers are measured repeatedly along some spatial structure or over time and are subject to measurement error. In these studies, it is often of interest to evaluate associations between a clinical endpoint and these biomarkers (also known as functional biomarkers). There are potentially two levels of correlation in such data, namely, between repeated measurements of a biomarker from the same subject and between multiple biomarkers from the same subject; none of the existing methods accounts for correlation between multiple functional biomarkers. We propose a Bayesian approach to model a clinical outcome of interest (e.g. risk for colorectal cancer) in the presence of multiple functional biomarkers while accounting for potential correlation. Our simulations show that the proposed approach achieves good performance in finite samples under various settings. In the presence of substantial or moderate correlation, the proposed approach outperforms an existing approach that does not account for correlation. The proposed approach is applied to a study of biomarkers of risk for colorectal neoplasms and our results show that the risk for colorectal cancer is associated with two functional biomarkers, APC and TGF-α, in particular, with their values in the region between the proliferating and differentiating zones of colorectal crypts.

A Mediterranean dietary intervention in persons at high risk of colon cancer: recruitment and retention to an intensive study requiring biopsies

This study recruited persons at increased risk of colon cancer to an intensive dietary intervention study that required biopsies of the colon by flexible sigmoidoscopy at baseline and after six months of intervention. A total of 1314 individuals contacted the study, and only 16 individuals indicated that the sigmoidoscopy procedure was an obstacle to study participation. A total of 270 individuals completed a screening visit and signed a screening consent form. Inquiries about the study tended to be fewer in the winter and late summer. Failure to return food records was the most common reason for exclusion. Dietary recall at enrollment indicated that subjects were consuming significantly more vegetables, lower sodium and a lower glycemic load on the day before starting the study vs. during the eligibility phase which might have an impact on biomarker measures. This makes it important to capture dietary changes in the period between determination of eligibility and enrollment. Subjects (n=120) were randomized to follow a Healthy Eating or a Mediterranean Diet, each of which required substantial dietary record-keeping. The study completion rate was 78%, and subjects reported high satisfaction with study participation. Of the 93 individuals who completed the study, only one refused the flexible sigmoidoscopy at the final visit. These findings suggest that flexible sigmoidoscopy does not appear to be a barrier for recruitment of high-risk individuals to an intensive dietary intervention trial, but that completing food records can be.

Approximate nonparametric corrected-score method for joint modeling of survival and longitudinal data measured with error

We consider the problem of jointly modeling survival time and longitudinal data subject to measurement error. The survival times are modeled through the proportional hazards model and a random effects model is assumed for the longitudinal covariate process. Under this framework, we propose an approximate nonparametric corrected-score estimator for the parameter, which describes the association between the time-to-event and the longitudinal covariate. The term nonparametric refers to the fact that assumptions regarding the distribution of the random effects and that of the measurement error are unnecessary. The finite sample size performance of the approximate nonparametric corrected-score estimator is examined through simulation studies and its asymptotic properties are also developed. Furthermore, the proposed estimator and some existing estimators are applied to real data from an AIDS clinical trial.