Search

In the ENSEMBLE randomized, placebo-controlled phase 3 trial (NCT04505722), estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were determined from 484 vaccine and 1,067 placebo recipients who acquired COVID-19. In this set of prespecified analyses, we show that in Latin America, VE was significantly lower against Lambda vs. Reference and against Lambda vs. non-Lambda [family-wise error rate (FWER) p < 0.05]. VE differed by residue match vs. mismatch to the vaccine-insert at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20); significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 antibody-epitope escape scores and 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccinee sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against the most distant viruses.

Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics & Computational Biology, 2017., Finite mixture models are increasingly used to model distributions in multivariate regression settings where outcomes and covariates exhibit heterogeneous relationships. When observations are collected repeatedly within subjects, for example longitudinally, these models must be adapted to account for potential dependencies. In this thesis, we consider a class of finite mixture regression models that is suitable for longitudinal data structures. The proposed model captures inter-subject variability and intra-subject dependencies with random effects and allows both mixing proportions and component densities to depend on covariates. We propose a new version of the stochastic approximation expectation maximization (SAEM) algorithm, which we call the Newton-Raphson SAEM (NR-SAEM) algorithm for maximum likelihood (ML) estimation. The implementation of our proposed algorithm requires sampling unobservable random effects from their posterior distributions using Markov Chain Monte Carlo (MCMC) methods, and reduces stochastic variability by estimating latent group indicators unconditional on random effects. The proposed method is demonstrated using both simulated data and real data of repeated hormone levels over a key period of the menstrual cycle. We compare the NR-SAEM algorithm to the standard expectation maximization (EM) and the mixture SAEM (MSAEM) algorithms. We find that the NR-SAEM algorithm performed comparably to the standard EM algorithm that converged to the ML estimators for finite mixture regression models with linear random effects, and it exhibited better convergence than the MSAEM algorithm in simulations. Application of our proposed model to the hormone level data suggests that women’s hormone trajectories over a key part of the menstrual cycle may follow more than one type of pattern, suggesting several subpopulations which differ with respect to the distributions of BMI and PFOS exposure.

Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics and Computational Biology, 2012., Copulas are bevariate distributions with uniform marginals. They provide a general method for binding several univariate marginal distributions together to form a multivariate distribution. Following Clayton (1978), several families of single-parameter copula models have been proposed for analyzing survival data. This dissertation explores the use of a flexible two-parameter copula family for bivariate survival data. The two parameters reflect respectively lower and upper tail dependence, allowing these two important aspects of a bivariate distribution to be modeled separately. The basic properties of this family are described and investigated. Under parametric assumptions on the univariate marginals, besides the commonly used one-stage M.L.E., a two-stage parametric estimator is developed for the dependence parameters. In addition we study a semi-parametric generalization of the two-stage approach in which the two marginal distributions are estimated nonparametrically. The asymptotic properties of all proposed estimators are derived and finite sample properties under different censorship scenarios are assessed by simulation. We find that the one-stage and two-stage parametric estimators perform similarly, with somewhat lower variances than the two-stage semi-parametric estimator. Robustness of the parametric estimator to the specification of the marginals is explored. We also design and implement a goodness-of-fit test for model checking, and a model selection test for choosing the best model among commonly used copula families. The practical application of the proposed two-parameter model is illustrated through a data example.