scholarly journals Joint modeling of time-varying HIV exposure and infection for estimation of per-act efficacy in HIV prevention trials

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Elizabeth R. Brown ◽  
Clara P. Dominguez Islas ◽  
Jingyang Zhang
Keyword(s):  
Hiv Prevention ◽  
Hiv Infection ◽  
Empirical Bayes ◽  
Preventive Intervention ◽  
Joint Modeling ◽  
Bayes Estimation ◽  
Sexual Contacts ◽  
Prevention Trials ◽  
Hiv Exposure ◽  
Exposure Process

AbstractObjectives: Using the MTN-020/ASPIRE HIV prevention trial as a motivating example, our objective is to construct a joint model for the HIV exposure process through vaginal intercourse and the time to HIV infection in a population of sexually active women. By modeling participants’ HIV infection in terms of exposures, rather than time exposed, our aim is to obtain a valid estimate of the per-act efficacy of a preventive intervention.Methods: Within the context of HIV prevention trials, in which the frequency of sex acts is self-reported periodically by the participants, we model the exposure process of the trial participants with a non-homogeneous Poisson process. This approach allows for variability in the rate of sexual contacts between participants as well as variability in the rate of sexual contacts over time. The time to HIV infection for each participant is modeled as the time to the exposure that results in HIV infection, based on the modeled sexual contact rate. We propose an empirical Bayes approach for estimation. Results: We report the results of a simulation study where we evaluate the performance of our proposed approach and compare it to the traditional approach of estimating the overall reduction in HIV incidence using a Proportional Hazards Cox model. The proposed approach is also illustrated with data from the MTN-020/ASPIRE trial. Conclusions: The proposed joint modeling, along with the proposed empirical Bayes estimation approach, can provide valid estimation of the per-exposure efficacy of a preventive intervention.

Tomorrow’s HIV Prevention Trials of Vaccines and Antibodies

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Dean Follmann
Keyword(s):  
Hiv Prevention ◽  
Hiv Infection ◽  
Infection Rate ◽  
Infection Risk ◽  
Effective Interventions ◽  
Prevention Trials ◽  
Prevention Methods ◽  
Regression Adjustment ◽  
Baseline Covariates ◽  
Historical Controls

Abstract Effective HIV prevention has the potential to change the landscape of HIV prevention trials. Low infection rates will make superiority studies necessarily large while non-inferiority trials will need some evidence that a counterfactual placebo group had a meaningful HIV infection rate in order to provide evidence of effective interventions. This paper explores these challenges in the context of immune related interventions of mAbs and vaccines. We discuss the issue of effect modification in the presence of PrEP, where subjects on PrEP may have less of a benefit of a mAb or (vaccine) than subjects off PrEP. We also discuss different methods of placebo infection rate imputation. We estimate infection risk as a function of mAb level (or vaccine induced immune response) in the mAb (or vaccine) arm and then extrapolate this infection risk to zero mAbs as a proxy for the placebo infection rate. Important aspects are the use of triangulation or multiple methods to impute the placebo infection rate, concern about extrapolation if few mAbs are close to zero, and the use of currently available data with placebo groups to rigorously evaluate the accuracy of imputation methods. We also discuss use of historical controls and some generalizations of the idea of (DMurray, J. 2019. “Regulatory Perspectives for Streamlining HIV Prevention Trials.” Statistical Communications in Infectious Diseases.) to use rectal gonorrhea rates to impute HIV infection rate. Generalizations include regression adjustment to calibrate for potential differences in baseline covariates for ongoing vs historical studies and the use of the gonorrhea, HIV relationship in a contemporaneous observational study. Examples of recent and ongoing trials of malaria chemoprophylaxis and HPV vaccines, where extremely effect prevention methods are available, are provided.

scholarly journals Modeling In-Match Sports Dynamics Using the Evolving Probability Method

2021 ◽  
Vol 11 (10) ◽  
pp. 4429
Author(s):  
Ana Šarčević ◽  
Damir Pintar ◽  
Mihaela Vranić ◽  
Ante Gojsalić
Keyword(s):  
Monte Carlo Simulation ◽  
Statistical Models ◽  
Empirical Bayes ◽  
Hybrid Approach ◽  
Real Life ◽  
Bayes Estimation ◽  
Probability Method ◽  
Specific Nature ◽  
Sport Event ◽  
Insight Into

The prediction of sport event results has always drawn attention from a vast variety of different groups of people, such as club managers, coaches, betting companies, and the general population. The specific nature of each sport has an important role in the adaption of various predictive techniques founded on different mathematical and statistical models. In this paper, a common approach of modeling sports with a strongly defined structure and a rigid scoring system that relies on an assumption of independent and identical point distributions is challenged. It is demonstrated that such models can be improved by introducing dynamics into the match models in the form of sport momentums. Formal mathematical models for implementing these momentums based on conditional probability and empirical Bayes estimation are proposed, which are ultimately combined through a unifying hybrid approach based on the Monte Carlo simulation. Finally, the method is applied to real-life volleyball data demonstrating noticeable improvements over the previous approaches when it comes to predicting match outcomes. The method can be implemented into an expert system to obtain insight into the performance of players at different stages of the match or to study field scenarios that may arise under different circumstances.

scholarly journals An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Pharmaceutics ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 42
Author(s):  
Walter M. Yamada ◽  
Michael N. Neely ◽  
Jay Bartroff ◽  
David S. Bayard ◽  
James V. Burke ◽  
...  
Keyword(s):  
Drug Development ◽  
Population Pharmacokinetics ◽  
Empirical Bayes ◽  
Applied Mathematics ◽  
Grid Method ◽  
Bayes Estimation ◽  
Population Pharmacokinetic ◽  
Support Points ◽  
Primal Dual ◽  
Log Normal

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

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