WorldCat Identities

Pennell, Michael L.

Overview
Works: 13 works in 13 publications in 1 language and 18 library holdings
Genres: Academic theses 
Roles: Contributor, Other, Author
Publication Timeline
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Most widely held works by Michael L Pennell
Bayesian threshold regression for current status data with informative censoring by Tao Xiao( )

1 edition published in 2015 in English and held by 2 WorldCat member libraries worldwide

Sometimes multivariate current status data also arise, e.g., tumors can develop in multiple organ sites in carcinogenicity studies. Examination time occurring at natural death could be affected by these different types of tumors which may intrinsically correlate with each other. We propose a multivariate Bayesian approach to accommodate multiple left censored events driven by different latent Wiener processes. We use a random effect shared by the drifts of the processes underlying the events of interest to model the correlation of the event times. The censoring process is modeled using a latent Wiener process whose time scale is affected by the occurrence of an event thus accounting for dependent censoring
Lower patient-reported function at 2 years is associated with elevated knee cartilage T1rho and T2 relaxation times at 5 years in young athletes after ACL reconstruction by Matthew P Ithurburn( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Change in longitudinal trends in sleep quality and duration following breast cancer diagnosis: results from the Women's Health Initiative by Chloe M Beverly( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Real-time cine and myocardial perfusion with treadmill exercise stress cardiovascular magnetic resonance in patients referred for stress SPECT by Subha V Raman( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

How Are Previous Physical Activity and Self-Efficacy Related to Future Physical Activity and Self-Efficacy?( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Self-efficacy (SE) has been found to be a robust predictor of success in achieving physical activity (PA) goals. While much of the current research has focused on SE as a trait, SE as a state has received less attention. Using day-to-day measurements obtained over 84 days, we examined the relationship between state SE and PA. Postmenopausal women (n = 71) participated in a 12-week PA intervention administered via cell phone and monitored their daily PA using a pedometer. At the end of each day, they reported their state SE and number of steps. Using a longitudinal model, state SE was found to be a robust predictor of PA even after accounting for trait SE and other covariates. The findings offer insights about the temporal relationship between SE and PA over the course of an intervention, which can be of interest to researchers and intervention designers
A simulation study of bivariate Wiener process models for an observable marker and latent health status by Sara A Conroy( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

Comparing the statistical power of analysis of covariance after multiple imputation and the mixed model in testing the treatment effect for pre-post studies with loss to follow-up by Wenna Xi( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Pre-post studies, where outcomes are measured both before and after an intervention, are common in biomedical research. When the outcomes at both pre- and post-test are completely observed, previous studies have shown that analysis of covariance (ANCOVA) is more powerful than the change score analysis in testing the treatment effect and therefore is usually recommended in analyzing pre-post studies. However, methods for analyzing pre-post studies with missing outcome values have not been compared. The goal of this study was to compare the power of two analysis methods in testing for a treatment effect when post-test values are missing: ANCOVA after multiple imputation (MI) and the mixed model. To do so, we analyzed data from a real study, the BePHIT study, and performed simulation studies. Four analysis methods were used to analyze the BePHIT and simulated data: ANCOVA after MI, ANCOVA using only complete cases (CC), the mixed model using all-available data, and the mixed model using complete cases. Simulation studies were conducted under various sample sizes, missingness rates, and missingness scenarios. In the analysis of the BePHIT data, ANCOVA after MI produced the smallest p-value for the test of a treatment effect. However, in the simulation studies, CC ANCOVA was generally the most powerful method. The simulation studies also showed that the power of ANCOVA after MI dropped the fastest when the percentage of missingness increased and, for most scenarios, was the least powerful method when 50% of the post-test outcomes were missing
Back problems among emergency medical services professionals : the LEADS health and wellness follow-up study by Jonathan R Studnek( Book )

1 edition published in 2010 in English and held by 1 WorldCat member library worldwide

Bayesian semiparametric methods for longitudinal, multivariate, and survival data by Michael L Pennell( )

1 edition published in 2006 in English and held by 1 WorldCat member library worldwide

Semi-parametric survival analysis via Dirichlet process mixtures of the first hitting time model by Jonathan A Race( )

1 edition published in 2019 in English and held by 1 WorldCat member library worldwide

Time-to-event data often violate the proportional hazards assumption inherent in the popular Cox regression model. Such violations are especially common in the sphere of biological and medical data where latent heterogeneity due to unmeasured covariates or time varying effects are common. A variety of parametric survival models have been proposed which make more appropriate assumptions on the hazard function, at least for certain applications. One such model is derived from the First Hitting Time (FHT) paradigm which assumes that a subject's event time is determined by a latent stochastic process reaching a threshold value. Several random effects specifications of the FHT model have also been proposed which allow for better modeling of data with unmeasured covariates. While often appropriate, these methods often display limited flexibility due to their inability to model a wide range of heterogeneities. To address this issue, we propose two Bayesian models which loosen assumptions on the mixing distribution inherent in the random effects FHT models currently in use. The first proposed model is ideally suited for standard regression analyses. The second model is designed for use in clinical trials where survival is the outcome of interest. We demonstrate via simulation study that the proposed models greatly improve both survival and parameter estimation in the presence of latent heterogeneity. We also apply the proposed methodologies to data from a toxicology/carcinogenicity study which exhibits nonproportional hazards and contrast the results with competing methods
A comparison of methods for addressing lag uncertainty in cumulative exposure-response analyses for time-to-event data by Yubo Tan( )

1 edition published in 2017 in English and held by 1 WorldCat member library worldwide

In survival analysis, there is often a latent period between a certain exposure and its actual effect on an event. The common approach to measuring the true time-to-event is to identify a set of models with reasonable latent periods that approximate the true model, and then selecting one model from them based on some criterion such as the AIC or BIC. However, selecting one model ignores the uncertainty in model selection and assumes that the true model can be captured or approximated by the identified models. Instead of making references based on a single model, Bayesian model averaging (BMA) incorporates the uncertainties in model selection by averaging the estimates over a set of reasonable models. We conducted a simulation study to evaluate the advantages and limitations of BMA compared to the method of selecting the best-fit model, and made suggestions on the application of BMA. Five hundred simulations were run with different sample sizes (1000 and 2000) and different true latent periods (0 days, 3 days, and 30 days) between the exposure and its effect on the outcome. We found that BMA had more accurate variance estimate as well as a better confidence interval (CI) coverage, but the point estimate of the coefficient from BMA is more biased than that from the method of selecting the best-fit model
A novel approach for modeling time to event data in maternal child health by Sara A Conroy( )

1 edition published in 2019 in English and held by 1 WorldCat member library worldwide

Results: Logistic regression provided evidence that the odds of birth before 37 weeks completed gestation were higher for smokers compared to non-smokers. Similarly, there was evidence from the Cox proportional hazards regression that the spontaneous live birth rate for was higher for smokers compared to non-smokers until about 37 weeks completed gestation. However, from 37 weeks until 44 weeks gestation there was no evidence of a difference in the birth rate. Threshold regression provided evidence that the latent fetal growth/development process has a greater rate of change for smokers compared to non-smokers, which corresponded to an estimated gestational age at delivery about two days sooner for smokers compared to non-smokers. Additionally, in a low-risk, prospective cohort of pregnant women, potential measurement error from gestational age estimation methods did not have a large impact on threshold regression estimates
 
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Alternative Names
Pennell, Michael Lindsey

Languages
English (13)