James Stamey

James Stamey
Baylor University | BU · Department of Statistical Sciences

PhD

About

106
Publications
5,464
Reads
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780
Citations
Additional affiliations
January 2004 - June 2018
Baylor University
Position
  • Professor
January 2002 - December 2005

Publications

Publications (106)
Article
Full-text available
The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people’s lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of...
Article
The idea that medical treatment costs and outcomes might be connected is not new. Likewise, as long as researchers have been designing clinical trials and public opinion polls, there has been interest in the sample size necessary to obtain a desired level of precision and certainty before collecting the data. However, researchers continue to adapt...
Article
The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In...
Preprint
Purpose: We review statistical methods for assessing the possible impact of bias due to unmeasured confounding in real world data analysis and provide detailed recommendations for choosing among the methods. Methods: By updating an earlier systematic review, we summarize modern statistical best practices for evaluating and correcting for potential...
Article
Purpose: We review statistical methods for assessing the possible impact of bias due to unmeasured confounding in real world data analysis and provide detailed recommendations for choosing among the methods. Methods: By updating an earlier systematic review, we summarize modern statistical best practices for evaluating and correcting for potenti...
Article
Bayesian sensitivity analysis of unmeasured confounding is proposed for observational data with misclassified outcome. The approach simultaneously corrects bias from error in the outcome and examines possible change in the exposure effect estimation assuming the presence of a binary unmeasured confounder. We assess the influence of unmeasured confo...
Article
Full-text available
Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic test...
Article
Purpose: Observational pharmacoepidemiological studies can provide valuable information on the effectiveness or safety of interventions in the real world, but one major challenge is the existence of unmeasured confounder(s). While many analytical methods have been developed for dealing with this challenge, they appear under-utilized, perhaps due t...
Article
Full-text available
Cost-effectiveness models are commonly utilized to determine the combined clinical and economic impact of one treatment compared to another. However, most methods for sample size determination of cost-effectiveness studies assume fully observed costs and effectiveness outcomes, which presents challenges for survival-based studies in which censoring...
Data
The second OpenBUGS program was used to produce INMB estimates for the normal-normal cost-effectiveness model. (TXT)
Data
The nn_simulation.r program was used to simulate data for the normal-normal cost effectiveness models. (R)
Data
The first OpenBUGS program was used to produce INMB estimates for the gamma-Weibull cost-effectiveness model. (TXT)
Data
The wg_simulation.r program was used to simulate data for the Weibull-gamma cost effectiveness models. (R)
Article
Full-text available
Schizophrenia is a debilitating serious mental illness characterized by a complex array of symptoms with varying severity and duration. Patients may seek treatment only intermittently, contributing to challenges diagnosing the disorder. A misdiagnosis may potentially bias and reduce study validity. Thus we developed a statistical model to assess th...
Article
Measurement error is a commonly addressed problem in psychometrics and the behavioral sciences, particularly where gold standard data either does not exist or are too expensive. The Bayesian approach can be utilized to adjust for the bias that results from measurement error in tests. Bayesian methods offer other practical advantages for the analysi...
Article
Bayesian propensity score regression analysis with misclassified binary responses is proposed to analyse clustered observational data. This approach utilizes multilevel models and corrects for misclassification in the responses. Using the deviance information criterion (DIC), the performance of the approach is compared with approaches without corre...
Article
Binary measurement systems that classify parts as either pass or fail are widely used. In industrial settings, many previously passed and failed parts are often available. We develop a Bayesian model to incorporate baseline information to determine whether a part originated from the stream of previously passed or failed parts as well as the overall...
Article
We examine the effect of female employment on the odds of physical spousal violence using a Bayesian misclassification model combined with propensity score regression estimation. While a classical propensity score model finds a significant violence-provoking effect of female employment, our model finds no evidence of a significant effect. This sugg...
Article
Purpose: The existence of unmeasured confounding can clearly undermine the validity of an observational study. Methods of conducting sensitivity analyses to evaluate the impact of unmeasured confounding are well established. However, application of such methods to survival data (“time-to-event” outcomes) have received little attention in the litera...
Article
For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied up...
Article
Full-text available
Purpose: Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care admin...
Article
Validation of pharmaceutical manufacturing processes is a regulatory requirement and plays a key role in the assurance of drug quality, safety, and efficacy. The FDA guidance on process validation recommends a life-cycle approach which involves process design, qualification, and verification. The European Medicines Agency makes similar recommendati...
Article
Adaptive clinical trial designs can often improve drug-study efficiency by utilizing data obtained during the course of the trial. We present a novel Bayesian two-stage adaptive design for Phase II clinical trials with Poisson-distributed outcomes that allows for person-observation-time adjustments for early termination due to either futility or ef...
Article
Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is consi...
Article
Full-text available
Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity...
Article
Background/Aims: Schizophrenia is a debilitating serious mental illness (SMI) characterized by a complex array of symptoms. Patients tend to seek treatment only intermittently, contributing to difficulty in diagnosing the disorder. A misdiagnosis may potentially bias and reduce the validity of a study based on recorded diagnoses. It also may impact...
Article
We derive Bayesian interval estimators for the differences in the true positive rates and false positive rates of two dichotomous diagnostic tests applied to the members of two distinct populations. The populations have varying disease prevalences with unverified negatives. We compare the performance of the Bayesian credible interval to the Wald in...
Article
Safety assessment is essential throughout medical product development. There has been increased awareness of the importance of safety trials recently, in part due to recent US Food and Drug Administration guidance related to thorough assessment of cardiovascular risk in the treatment of type 2 diabetes. Bayesian methods provide great promise for im...
Article
Unmeasured confounding is a common problem in observational studies. Failing to account for unmeasured confounding can result in biased point estimators and poor performance of hypothesis tests and interval estimators. We provide examples of the impacts of unmeasured confounding on cost-effectiveness analyses using observational data along with a B...
Article
In clinical trials, multiple outcomes are often collected in order to simultaneously assess effectiveness and safety. We develop a Bayesian procedure for determining the required sample size in a regression model where a continuous efficacy variable and a binary safety variable are observed. The sample size determination procedure is simulation bas...
Article
The quantitative assessment of the potential influence of unmeasured confounders in the analysis of observational data is rare, despite reliance on the "no unmeasured confounders" assumption. In a recent comparison of costs of care between two treatments for type 2 diabetes using a health care claims database, propensity score matching was implemen...
Article
Through this study the authors assessed the outcomes of a randomized effectiveness trial of Project U-Turn, a comprehensive sex education program for at-risk youth in Miami, Florida. Data collection occurred at pretest, three month, and six month follow-ups with a sample of teenagers randomly selected and assigned to treatment (n = 549) and control...
Article
Using meta-analysis in health care research is a common practice. Here we are interested in methods used for analysis of time-to-event data. Particularly, we are interested in their performance when there is a low event rate. We consider three methods based on the Cox proportional hazards model, including a Bayesian approach. A formal comparison of...
Article
Background: Recent research suggests that the Bayesian paradigm may be useful for modeling biases in epidemiological studies, such as those due to misclassification and missing data. We used Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to the potential effect of these two important sources of bias...
Article
Covariate misclassification is a common problem in epidemiology, genetics, and other biomedical areas. Because this form of misclassification is known to bias estimators, accounting for it at the design stage is of high importance. In this paper, we extend on previous work applied to response misclassification by developing a Bayesian approach to s...
Article
This study examined the influence of family structure on the outcomes of a sex education program in Miami, Florida. Using an experimental design, data collection occurred at pretest, 3-month, and 6-month follow-up with a sample of teenagers from high schools with a large majority of minority youth, assigned into treatment (n = 549) and control (n =...
Article
“Noninformative” priors are widely used in Bayesian inference. Diffuse priors are often placed on parameters that are components of some function of interest. That function may, of course, have a prior distribution that is highly informative, in contrast to the joint prior placed on its arguments, resulting in unintended influence on the posterior...
Article
This study examined the influence of religious participation on the outcomes of a comprehensive sex education program for minority youth in Miami, FL. Data collection occurred at pretest and at 3-month and 6-month follow-up. A sample of teenagers was randomly selected from high schools, with a large majority of minority youth, and was assigned into...
Article
To determine whether and to what degree exposure to isoflavone-containing soy products affects EF. Endothelial dysfunction has been identified as an independent coronary heart disease risk factor and a strong predictor of long-term cardiovascular morbidity and mortality. Data on the effects of exposure to isoflavone-containing soy products on EF ar...
Article
We investigate three interval estimators for binomial misclassification rates in a complementary Poisson model where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. We investigate the coverage and average width properties of these intervals via a simu...
Article
A model is proposed to estimate and compare cervical cancer screening test properties for third world populations when only subjects with a positive screen receive the gold standard test. Two fallible screening tests are compared, VIA and VILI. We extend the model of Berry et al. [1] to the multi-site case in order to pool information across sites...
Article
Comparing occurrence rates of events of interest in science, business, and medicine is an important topic. Because count data are often under-reported, we desire to account for this error in the response when constructing interval estimators. In this article, we derive a Bayesian interval for the difference of two Poisson rates when counts are pote...
Article
A repeated binary testing framework is implemented when quality inspections fallibly classify units as either conforming or not conforming to quality standards. We develop a Bayesian approach to the statistical analysis of repeated binary testing data. Our approach allows an investigator to characterize and incorporate prior information on the unkn...
Article
Variable selection for Poisson regression when the response variable is potentially underreported is considered. A logistic regression model is used to model the latent underreporting probabilities. An efficient MCMC sampling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affe...
Article
Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist sample-size determination methods, the Bayesian paradigm offers a wide variety of sample-size determinati...
Article
Bayesian sample size estimation for equivalence and non-inferiority tests for diagnostic methods is considered. The goal of the study is to test whether a new screening test of interest is equivalent to, or not inferior to the reference test, which may or may not be a gold standard. Sample sizes are chosen by the model performance criteria of avera...
Article
To quantify the impact of ignoring misclassification of a response variable and measurement error in a covariate on statistical power, and to develop software for sample size and power analysis that accounts for these flaws in epidemiologic data. A Monte Carlo simulation-based procedure is developed to illustrate the differences in design requireme...
Article
We derive a new Bayesian credible interval estimator for comparing two Poisson rates when counts are underreported and an additional validation data set is available. We provide a closed-form posterior density for the difference between the two rates that yields insightful information on which prior parameters influence the posterior the most. We a...
Article
We develop a Bayesian approach to sample size and power calculations for cross-sectional studies that are designed to evaluate and compare continuous medical tests. For studies that involve one test or two conditionally independent or dependent tests, we present methods that are applicable when the true disease status of sampled individuals will be...
Article
Positive predictive and negative predictive values (PPV and NPV) are often used to assess the accuracy of binary diagnostic tests. Unlike sensitivity and specificity, PPV and NPV are functions of the accuracy of the test and the overall prevalence of the disease in the population. In many studies of performance of estimators of PPV and NPV the popu...
Article
We generalize the classical group testing problem to incorporate costs associated with pooling and inspection, both of which are significant factors in actual applications. We formulate the expected cost model as a nonlinear integer programming problem, prove several propositions and a theorem concerning when pooling is more efficient than individu...
Article
The problem of assessing agreement between two devices occurs with great frequency in the medical literature. If it can be demonstrated that a new device agrees sufficiently with a device currently in use, then the new device can be approved for general use. This work discusses how a prediction interval can be used to estimate the whether a future...
Article
The purpose of this study is to illustrate the impact of ignoring missing data in follow-up studies and to provide a hierarchical Bayesian approach to simultaneously estimate rates and missing data probabilities. To account for missing data in follow up studies, a hierarchical Bayesian procedure is proposed and investigated via simulation. A simula...
Article
We develop a simulation-based procedure for determining the required sample size in binomial regression risk assessment studies when response data are subject to misclassification. A Bayesian average power criterion is used to determine a sample size that provides high probability, averaged over the distribution of potential future data sets, of co...
Article
Full-text available
We develop a Bayesian analysis for the study of fixed-dose combinations of two or more drugs. The approach described here does not require knowledge of the dose-response relationships of the components or large sample approximations. We provide a procedure to estimate sample size in this context. In addition, we explore the performance of the Bayes...
Article
We derive three interval estimators for complementary Poisson rates where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. Also, we derive an EM algorithm to determine profile maximum likelihood estimators. We investigate the coverage and average width...
Article
In this, article we consider a Bayesian approach to the problem of ranking the means of normal distributed populations, which is a common problem in the biological sciences. We use a decision-theoretic approach with a straightforward loss function to determine a set of candidate rankings. This loss function allows the researcher to balance the risk...
Article
Response misclassification of counted data biases and understates the uncertainty of parameter estimators in Poisson regression models. To correct these problems, researchers have devised classical procedures that rely on asymptotic distribution results and supplemental validation data in order to estimate unknown misclassification parameters. We d...
Article
We derive a profile-likelihood confidence interval and a score based confidence interval to estimate the population prevalences, test sensitivities, and test specificities of two conditionally independent diagnostic tests when no gold standard is available. We are motivated by a real-data example on the study of the properties for two fallible diag...
Article
We consider a Bayesian analysis for modeling a binary response that is subject to misclassification. Additionally, an explanatory variable is assumed to be unobservable, but measurements are available on its surrogate. A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in th...
Article
We examine student achievement in College Geometry, Pre-calculus, and Probability and Statistics based upon the color-coding of the final examination. Multiple class sections taught by the same teacher were utilized in order to keep the possible confounding variables to a minimum. This study supports and expands the results of recently published ar...
Article
Bivariate Poisson regression models allow for joint estimation of two Poisson counts as a function of possibly different covariates. We use vector calculus to derive the Wald, score, and likelihood ratio statistics for testing a single coefficient parameter vector. We compare the power of these three statistics using a limited Monte Carlo simulatio...
Article
We derive a new Bayesian estimation procedure for comparing Poisson-distributed mortality rates of a particular cohort and a standard population using the standardized mortality ratio (SMR) incorporating missing death certificates. Our paper assumes the realistic case of unequal unknown reporting probabilities with only vague knowledge in the form,...
Article
We consider a fully Bayesian approach to estimation of the parameters for generalized Poisson data in a multiple population context. The hierarchical model we consider here extends previous single population models. This hierarchical model has applications in industrial, biological and sociological disciplines. We also extend two recently developed...
Article
The limits of agreement approach of Bland and Altman is by far the most popular method for investigating statistical agreement between two measurement devices. This work presents the dangers of relying exclusively on the limits of agreement alone and argues that authors should always provide confidence intervals to assess the variability in the est...
Article
Full-text available
Three Bayesian approaches are considered for the selection of binomial proportion parameters when data is subject to misclassification. The cases where the misclassification is non-differential and differential were considered, thus extending previous work which considered only non-differential misclassification. In this article, various selection...
Article
We consider the roblem of variable selection for logistic regression when the dependent variable is measured imperfectly, under both differential and non-differential misclassification. An MCMC sampling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affect the probability of m...
Article
We develop a Bayesian simulation based approach for determining the sample size required for estimating a binomial probability and the difference between two binomial probabilities where we allow for dependence between two fallible diagnostic procedures. Examples include estimating the prevalence of disease in a single population based on results f...
Article
The optimal sample size comparing two Poisson rates when the counts are underreported is investigated. We consider two sampling scenarios. We first consider the case where only underreported data will be sampled and rely on informative prior distributions to obtain posterior identifiability. We also consider the case where an expensive infallible s...
Article
We develop a Bayesian simulation based approach for determining the sample size required for a case-control study with misclassified data. Our method assumes a validation subsample is available. Using an average posterior variance criterion we consider both fixed cost and fixed variance procedures.
Article
We consider studies in which an enrolled subject tests positive on a fallible test. After an intervention, disease status is re-diagnosed with the same fallible instrument. Potential misclassification in the diagnostic test causes regression to the mean that biases inferences about the true intervention effect. The existing likelihood approach suff...
Article
For the Gauss-Markov model with E(y)=Xβ and Var(y)=V, we establish a new explicit characterization of the general non-negative-definite covariance structure V such that the best linear unbiased estimator and least squares estimator of Xβ are identical. The proof of our representation is brief, requiring only basic properties of real matrices. Furth...
Article
We consider three interval estimators for linear functions of Poisson rates: a Wald interval, a t interval with Satterthwaite's degrees of freedom, and a Bayes interval using noninformative priors. The differences in these intervals are illustrated using data from the Crash Records Bureau of the Texas Department of Public Safety. We then investigat...
Article
We formulate Bayesian approaches to the problems of determining the required sample size for Bayesian interval estimators of a predetermined length for a single Poisson rate, for the difference between two Poisson rates, and for the ratio of two Poisson rates. We demonstrate the efficacy of our Bayesian-based sample-size determination method with t...
Article
Full-text available
In this study, the effect of specific gravity and rings per inch on the bending strength of 11 mill-run batches of finger-jointed southern pine lumber was examined. The bending test specimens were prepared according to the Glued Lumber Standard for Southern Pine as outlined by the SPIB. For each finger-jointed board, 8 wood properties were calculat...
Article
We analyse a combination of errant count data subject to under-reported counts and inerrant count data to estimate multiple Poisson rates and reporting probabilities of cervical cancer for four European countries. Our analysis uses a Bayesian hierarchical model. Using a simulation study, we demonstrate the efficacy of our new simultaneous inference...
Article
We develop a new Bayesian approach to interval estimation for both the risk difference and the risk ratio for a 2 x 2 table with a structural zero using Markov chain Monte Carlo (MCMC) methods. We also derive a normal approximation for the risk difference and a gamma approximation for the risk ratio. We then compare the coverage and interval width...
Article
In this paper we derive five first-order likelihood-based confidence intervals for a population proportion parameter based on binary data subject to false-positive misclassification and obtained using a double sampling plan. We derive confidence intervals based on certain combinations of likelihood, Fisher-information types, and likelihood-based st...
Article
We develop Bayesian closed-form predictive probability functions for a true unobservable future count of interest in a future observable sample for a Poisson model incorporating misclassification. We also derive Bayesian closed-form posterior probability functions for the number of false-positive and false-negative counts in a current observable sa...
Article
Bioequivalence studies focus on determining if drug formulations are therapeutically equivalent. This is important, for example, in the development of generic drugs. Once a brand-name drug goes off patent, other pharmaceutical companies can develop and market a generic drug provided they can demonstrate that the two drug formulations are bioequival...