Bayesian inference and model choice in a hidden stochastic two-compartment model of hematopoietic stem cell fate decisions

Despite rapid advances in experimental cell biology, the in vivo behavior of hematopoietic stem cells (HSC) cannot be directly observed and measured. Previously we modeled feline hematopoiesis using a two-compartment hidden Markov process that had birth and emigration events in the first compartment. Here we perform Bayesian statistical inference on models which contain two additional events in the first compartment in order to determine if HSC fate decisions are linked to cell division or occur independently. Pareto Optimal Model Assessment approach is used to cross check the estimates from Bayesian inference. Our results show that HSC must divide symmetrically (i.e., produce two HSC daughter cells) in order to maintain hematopoiesis. We then demonstrate that the augmented model that adds asymmetric division events provides a better fit to the competitive transplantation data, and we thus provide evidence that HSC fate determination in vivo occurs both in association with cell division and at a separate point in time. Last we show that assuming each cat has a unique set of parameters leads to either a significant decrease or a nonsignificant increase in model fit, suggesting that the kinetic parameters for HSC are not unique attributes of individual animals, but shared within a species.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS269 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Efficacy of a bivalent killed whole-cell cholera vaccine over five years: a re-analysis of a cluster-randomized trial.

BACKGROUND: Oral cholera vaccine (OCV) is a feasible tool to prevent or mitigate cholera outbreaks. A better understanding of the vaccine's efficacy among different age groups and how rapidly its protection wanes could help guide vaccination policy. METHODS: To estimate the level and duration of OCV efficacy, we re-analyzed data from a previously published cluster-randomized, double-blind, placebo controlled trial with five years of follow-up. We used a Cox proportional hazards model and modeled the potentially time-dependent effect of age categories on both vaccine efficacy and risk of infection in the placebo group. In addition, we investigated the impact of an outbreak period on model estimation. RESULTS: Vaccine efficacy was 38% (95% CI: -2%,62%) for those vaccinated from ages 1 to under 5 years old, 85% (95% CI: 67%,93%) for those 5 to under 15 years, and 69% (95% CI: 49%,81%) for those vaccinated at ages 15 years and older. Among adult vaccinees, efficacy did not appear to wane during the trial, but there was insufficient data to assess the waning of efficacy among child vaccinees. CONCLUSIONS: Through this re-analysis we were able to detect a statistically significant difference in OCV efficacy when the vaccine was administered to children under 5 years old vs. children 5 years and older. The estimated efficacies are more similar to the previously published analysis based on the first two years of follow-up than the analysis based on all five years. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT00289224

HAI and NAI titer correlates of inactivated and live attenuated influenza vaccine efficacy

Abstract Background High hemagglutination inhibition (HAI) and neuraminidase inhibition (NAI) titers are generally associated with reduced influenza risk. While repeated influenza vaccination reduces seroresponse, vaccine effectiveness is not always reduced. Methods During the 2007-2008 influenza season, a randomized, placebo-controlled trial (FLUVACS) evaluated the efficacies of live-attenuated (LAIV) and inactivated influenza vaccines (IIV) among healthy adults aged 18-49 in Michigan; IIV vaccine efficacy (VE) and LAIV VE against influenza disease were estimated at 68% and 36%. Using the principal stratification/VE moderation framework, we analyzed data from this trial to assess how each VE varied by HAI or NAI responses to vaccination observed for vaccinated individuals and predicted counterfactually for placebo recipients. We also assessed how each VE varied with pre-vaccination/baseline variables including HAI titer, NAI titer, and vaccination history. Results IIV VE appeared to increase with Day 30 post-vaccination HAI titer, albeit not significantly (p=0.20 and estimated VE 14.4%, 70.5%, and 85.5% at titer below the assay lower quantification limit, 512, and 4096 (maximum)). Moreover, IIV VE increased significantly with Day 30 post-vaccination NAI titer (p=0.040), with estimated VE zero at titer 10 and 92.2% at highest titer 640. There was no evidence that fold-change in post-vaccination HAI or NAI titer associated with IIV VE (p=0.76, 0.38). For LAIV, there was no evidence that VE associated with post-vaccination or fold-rise HAI or NAI titers (p-values >0.40). For IIV, VE increased with increasing baseline NAI titer in those previously vaccinated, but VE decreased with increasing baseline NAI titer in those previously unvaccinated. In contrast, for LAIV, VE did not depend on previous vaccination or baseline HAI or NAI titer. Conclusions: Future efficacy trials should measure baseline and post-vaccination antibody titers in both vaccine and control/placebo recipients, enabling analyses to better elucidate correlates of vaccine- and natural-protection. Trial registration: ClinicalTrials.gov NCT00538512. October 1, 2007.https://deepblue.lib.umich.edu/bitstream/2027.42/149182/1/12879_2019_Article_4049.pd

Change Point Testing in Logistic Regression Models with Interaction Term

The threshold effect takes place in situations where the relationship between an outcome variable and a predictor variable changes as the predictor value crosses a certain threshold/change point. Threshold effects are often plausible in a complex biological system, especially in defining immune responses that are protective against infections such as HIV-1, which motivates the current work. We study two hypothesis testing problems in change point models. We first compare three different approaches to obtaining a p-value for the maximum of scores test in a logistic regression model with change point variable as a main effect. Next, we study the testing problem in a logistic regression model with the change point variable both as a main effect and as part of an interaction term. We propose a test based on the maximum of likelihood ratio statistics and show that the correct significance level can be obtained by transforming random samples from a multivariate normal distribution. In simulation studies, we show the optimality of the maximum of likelihood statistics test among change point model-based methods, and demonstrate the performance trade-off when compared to dichotomizing the predictor variable at median across a range of true thresholds. We illustrate the utility of the change point model-based testing methods with a real data example from a recent study of immune responses that are associated with the risk of mother to child transmission (MTCT) of HIV-1

nCal- Some Examples

1 A basic example The function ncal carries out nonlinear calibration, which entails fitting concentration-response curves to sets of samples of known concentrations, also known as standard samples, and using the fitted curves to obtain point estimates and confidence intervals for the analyte concentrations in the samples of interest, also known as the unknown samples. Take Luminex (Defawe et al., 2012) as an example. The assay is conducted on 96-well or 384-well plates. Each plate usually has a set of wells containing standard samples and a set of wells containing unknown samples. Within each well, multiple analytes can be assayed simultaneously. In the following, we will use plate and assay exchangeably. ncal has 2 required arguments, formula and data, and 24 optional arguments. data is a data frame object, where each row is a sample, either standard or unknown. The formula object specifies the names of the response and concentration columns. Besides these two columns, data is also expected to have two columns that help identify standard curves: analyte and assay id. If data contains both standard and unknown samples, two more columns are also required: well role and sample id. data may also have additional columns. For example, it is sometimes convenient t

Transformation Model Choice in Nonlinear Regression Analysis of Fluorescence-Based Serial Dilution Assays

<p>Many modern serial dilution assays are based on fluorescence intensity (FI) readouts. We study the optimal transformation model choice for fitting five-parameter logistic curves (5PL) to FI-based serial dilution assay data. We first develop a generalized least squares-pseudolikelihood type algorithm for fitting heteroscedastic logistic models. Next, we show that the 5PL and log 5PL functions can approximate each other well. We then compare four 5PL models with different choices of log transformation and variance modeling through a Monte Carlo study and real data. Our findings are that the optimal choice depends on the intended use of the fitted curves. Supplementary materials for this article are available online.</p