Thursday, Aug 8: 10:30 AM - 12:20 PM
5189
Contributed Papers
Oregon Convention Center
Room: CC-E148
Main Sponsor
Biometrics Section
Presentations
In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time dependent covariates have however several limitations; they can for example not be applied when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models. In a joint model, both variables are modeled with a random-effects model, and the random effects are allowed to correlate. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the manifest correlations between the responses as observed. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response . Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology will be presented by means of a case study.
Keywords
Longitudinal data analysis
Joint model
Random effects model
Time dependent effects
Probit link
Research on progressive disorders typically involves understanding if the change in biomarker levels can monitor the worsening in medical conditions over time. Their relationship can be quantified by fitting a mixed model for longitudinal medical outcomes with the subject-specific rate of change in biomarker as a fixed effect. As the true long-term change in the biomarker levels is unobservable, biased estimates and invalid inferences may arise if it is replaced by the estimated random slope from a mixed effects model for biomarker measurements. We thus propose a joint modeling method where both longitudinal biomarker measurements and medical outcomes are modeled by linear mixed effects models. We show that the resulting estimators are consistent and asymptotically normal with a sandwich variance estimator. The method is evaluated via simulations and applied to analyze the association between the rate of change in the cerebrospinal fluid biomarker measurements and cognitive decline for participants with mild cognitive impairment in the Alzheimer's Disease Neuroimaging Initiative.
Keywords
Longitudinal data
Measurement Error
Biomarker
A growing body of research has shown that poor diet is a leading risk factor for death, especially in connection with chronic diseases such as cardiovascular disease. However, these studies are limited because they use simplistic measures of diet measured at a single timepoint. To address this issue, we develop a Bayesian dynamic latent factor model that measures how multivariate dietary patterns change over time. Our approach flexibly incorporates multivariate, longitudinal nutrition survey data such as food frequency questionaires with multiple outcome types (e.g. ordinal, continuous, etc.). A multiplicative gamma process prior is placed on the factor loadings to adaptively estimate low-dimensional dietary patterns. Importantly, our model also incorporates covariates such as demographics to assess how dietary patterns differ across subpopulations. We evaluate the Frequentist operating characteristics of the method in a simulation study. Our motivating application is the Black Women's Health Study, where we construct dynamic measures of diet that will be used in downstream analyses to better understand cardiovascular disease risk among black women in the United States.
Keywords
nutrition
Bayesian inference
latent factor model
health disparities
survey data
There is a growing interest in longitudinal omics data, but there are gaps in existing high-dimensional methodology. In particular, we are focused on modeling general continuous longitudinal outcomes with continuous longitudinal multi-omics predictors. Simple univariate longitudinal models do not leverage the correlation across predictors, thus losing power. Our method, PROLONG, leverages the first differences of the data to address the piecewise linear structure and the observed time dependence and applies penalties that induce sparsity while incorporating the dependence structure of the data. This presentation will review PROLONG and discuss recent extensions to multiple treatment arms, mixed effects, and general multi-omic data.
Keywords
Omics
Longitudinal
High-dimensional
Biomarkers
TB
Metabolomics
Abstracts
Huntington disease is a rare neurodegenerative disorder for which no effective therapies have yet been discovered. To help clinicians search for effective therapies that halt or slow the progression of the disease, analysts seek to model the progression of symptoms before and after diagnosis. However, many studies that track symptom progression end before all participants have been diagnosed. This presents a challenge: How do we model symptom progression given time of diagnosis, a right-censored covariate? Analysts frequently meet this challenge by imputing the time of diagnosis for undiagnosed participants. However, if the model used to impute the time to diagnosis is misspecified, it can introduce bias into the symptom progression model. This bias arises because the misspecified imputation model generates values that are prone to significant errors. To mitigate this estimation bias, we adopt a semiparametric technique to correct for imputation errors, enabling us to reliably estimate linear longitudinal models even in the presence of covariate censoring. Our novel approach is presented, and we assess its performance when applied to an observational study of Huntington disease.
Keywords
censored covariate
imputation
longitudinal data
measurement error
semiparametric theory
Accelerometry data collected by high-capacity sensors present a primary data type in smart mobile health. I holistically summarize an individual subject's activity profile using Occupation Time curves (OTCs). Being a functional predictor, OTCs describe the percentage of time spent at or above a continuum of activity count levels. The resulting functional curve is informative to capture time-course individual variability of physical activities both on the underlying functional variables of interest, as well as the specific health outcomes. I leverage the OTC curves to develop a longitudinal functional framework with repeated wearable data to understand the influence of serially measured functional accelerometer data on longitudinal health outcomes. I develop a new one-step method that can simultaneously conduct fusion via change-point detection and parameter estimation through a new L0 constraint formulation, invoking Quadratic Inference Functions (QIF), with an aim to detect physical activity intensity windows and assess their population-average effects on children health outcomes.
Keywords
L0 regularization
changepoint detection
accelerometer
functional data analysis