Medicine and Health Sciences Commons^™

Range-Wide Sources Of Variation In Reproductive Rates Of Northern Spotted Owls, Jeremy T. Rockweit, Julianna M. Jenkins, James E. Hines, James D. Nichols, Katie M. Dugger, Alan B. Franklin, Peter C. Carlson, William L. Kendall, Damon B. Lesmeister, Christopher Mccafferty, Steven H. Ackers, L. Steven Andrews, Larissa L. Bailey, Jesse Burgher, Kenneth P. Burnham, Tara Chestnut, Mary M. Conner, Raymond J. Davis, Krista E. Dilione, Eric D. Forsman, Elizabeth M. Glenn, Scott A. Gremel, Keith A. Hamm, Dale R. Herter, J. Mark Higley, Rob B. Horn, David W. Lamphear, Trent L. Mcdonald, Janice A. Reid, Carl J. Schwarz, David C. Simon, Stan G. Sovern, James K. Swingle, J. David Wiens, Heather Wise, Charles B. Yackulic

USDA Wildlife Services: Staff Publications

We conducted a range-wide investigation of the dynamics of site-level reproductive rate of northern spotted owls using survey data from 11 study areas across the subspecies geographic range collected during 1993–2018. Our analytical approach accounted for imperfect detection of owl pairs and misclassification of successful reproduction (i.e., at least one young fledged) and contributed further insights into northern spotted owl population ecology and dynamics. Both nondetection and state misclassification were important, especially because factors affecting these sources of error also affected focal ecological parameters. Annual probabilities of site occupancy were greatest at sites with successful reproduction in the previous year …

Evaluating And Reducing The Effects Of Misclassification In A Sequential Multiple Assignment Randomized Trial (Smart), Jun He

Theses and Dissertations

SMART designs tailor individual treatment by re-randomizing patients to subsequent therapies based on their response to initial treatment. However, the classification of patients being responders/non-responders could be inaccurate and thus lead to inappropriate treatment assignment. In a two-step SMART design, by assuming equal randomization, and equal variances of misclassified patients and correctly classified patients, we evaluated misclassification effects on mean, variance, and type I error/ power of single sequential treatment outcome (SST), dynamic treatment outcome (DTRs), and overall outcome. The results showed that misclassification could introduce bias to estimates of treatment effect in all types of outcome. Though the magnitude …

Hidden Markov Chain Analysis: Impact Of Misclassification On Effect Of Covariates In Disease Progression And Regression, Haritha Polisetti

USF Tampa Graduate Theses and Dissertations

Most of the chronic diseases have a well-known natural staging system through which the disease progression is interpreted. It is well established that the transition rates from one stage of disease to other stage can be modeled by multi state Markov models. But, it is also well known that the screening systems used to diagnose disease states may subject to error some times. In this study, a simulation study is conducted to illustrate the importance of addressing for misclassification in multi-state Markov models by evaluating and comparing the estimates for the disease progression Markov model with misclassification opposed to disease …

Bayesian Credible Sets For A Binomial Proportion Based On One-Sample Binary Data Subject To One Type Of Misclassification, Dewi Rahardja, Yan D. Zhao, Hongmei Zhang

Faculty Publications

Interval estimation for the proportion parameter in one-sample misclassified binary data has caught much interest in the literature. Recently, an approximate Bayesian approach has been proposed. This approach is simpler to implement and performs better than existing frequentist approaches. However, because a normal approximation to the marginal posterior density was used in the Bayesian approach, some efficiency may be lost. We develop a closed-form fully Bayesian algorithm which draws a posterior sample of the proportion parameter from the exact marginal posterior distribution. We conducted simulations to show that our fully Bayesian algorithm is easier to implement and has better coverage …

Adjusting Wheal Size Measures To Correct Atopy Misclassification, Hongmei Zhang, Wilfried J. Karmaus, Jianjun Gan, Weichao Bao, Yan D. Zhao, Dewi Rahardja, John W. Holloway, Martha Scott, Syed Hasan Arshad

Faculty Publications

Purpose: Skin prick testing (SPT) is fundamental to the practice of clinical allergy identifying relevant allergens and predicting the clinical expression of disease. Wheal sizes on SPT are used to identify atopic cases, and the cut-off value for a positive test is commonly set at 3 mm. However, the measured wheal sizes do not solely reflect the magnitude of skin reaction to allergens, but also skin reactivity (reflected in the size of histamine reaction) and other random or non-random factors. We sought to estimate wheal sizes exclusively due to skin response to allergens and propose gender-specific cutoff points of atopy. …

Sensitivity Analysis – The Effects Of Glasgow Outcome Scale Misclassification On Traumatic Brain Injury Clinical Trials, Juan Lu

Theses and Dissertations

I. EFFECTS OF GLASGOW OUTCOME SCALE MISCLASSIFICATION ON TRAUMATIC BRAIN INJURY CLINICAL TRIALS The Glasgow Outcome Scale (GOS) is the primary endpoint for efficacy analysis of clinical trials in traumatic brain injury (TBI). Accurate and consistent assessment of outcome after TBI is essential to the evaluation of treatment results, particularly in the context of multicenter studies and trials. The inconsistent measurement or interobserver variation on GOS outcome, or for that matter, on any outcome scales, may adversely affect the sensitivity to detect treatment effects in clinical trial. The objective of this study is to examine effects of nondifferential misclassification of …

Gastric Cancer Among American Indians And Alaska Natives In The United States, 1999–2004, Charles Wiggins, David G. Perdue, Jeffrey A. Henderson, Michael G. Bruce, Anne P. Lanier, Janet J. Kelly, Brenda F. Seals, David K. Espey

Public Health Resources

BACKGROUND. Gastric cancer incidence rates for American Indians and Alaska Natives (AI/ANs) historically have exceeded those for non-Hispanic whites (NHWs). Previous reports may have underestimated the true burden of gastric cancer in AI/AN populations because of misclassification of AI/AN race in cancer registries.

METHODS. Population-based cancer registry data from 1999 through 2004 were used to describe gastric cancer incidence in AI/ANs and NHWs in the US. To address misclassification of race, registry data were linked with Indian Health Service administrative records, and analyses were restricted to residents of Contract Health Service Delivery Areas (CHSDA). Disease patterns were assessed …

Lung Cancer Incidence Among American Indians And Alaska Natives In The United States, 1999–2004, Anne Bliss, Nathaniel Cobb, Teshia Solomon, Kym Cravatt, Melissa A. Jim, Latisha Marshall, Janis Campbell

Public Health Resources

BACKGROUND. Lung cancer incidence rates among American Indians and Alaska Natives (AI/ANs) in the United States have not been described well, primarily because of race misclassification and, until the 1990s, incomplete coverage of their population by cancer registries. Smoking, the predominant cause of lung cancer, is particularly prevalent among this population.

METHODS. Data from the National Program of Cancer Registries and the Surveillance, Epidemiology, and End Results Program were combined to estimate age adjusted incidence rates of lung cancer during 1999 through 2004. Cases were linked to Indian Health Service (IHS) registration databases to identify AI/ANs whose race …

Methods For Improving Cancer Surveillance Data In American Indian And Alaska Native Populations, David K. Espey, Charles Wiggins, Melissa A. Jim, Barry A. Miller, Christopher J. Johnson, Tom M. Becker

Public Health Resources

BACKGROUND. The misclassification of race decreases the accuracy of cancer incidence data for American Indians and Alaska Natives (AI/ANs) in some central cancer registries. This article describes the data sources and methods that were used to address this misclassification and to produce the cancer statistics used by most of the articles in this supplement.

METHODS. Records from United States cancer registries were linked with Indian Health Service (IHS) records to identify AI/AN cases that were misclassified as non-AI/AN. Data were available from 47 registries that linked their data with IHS, met quality criteria, and agreed to participate. Analyses …

Prostate Cancer Incidence Among American Indian And Alaska Native Men, Us, 1999–2004, Jeffrey A. Henderson, David K. Espey, Melissa Jim, Robert R. German, Kate M. Shaw, Richard M. Hoffman

Public Health Resources

BACKGROUND. American Indian and Alaska Native (AI/AN) men experience lower incidence of prostate cancer than other race/ethnic populations in the US, but racial misclassification of AI/AN men threatens the validity of these estimates. To the authors’ knowledge, little is known concerning prostate-specific antigen (PSA) testing in AI/AN men.

METHODS. The authors linked cancer registry data with Indian Health Service enrollment records to improve race classification. Analyses comparing cancer incidence rates and stage at diagnosis for AI/AN and non-Hispanic white (NHW) men for 6 geographic regions focused on counties known to have less race misclassification. The authors also used …

Primary Liver Cancer Incidence Among American Indians And Alaska Natives, Us, 1999–2004, Melissa Jim, David G. Perdue, Lisa C. Richardson, David K. Espey, John T. Redd, Howard J. Martin, Sandy L. Kwong, Janet J. Kelly, Jeffrey A. Henderson, Faruque Ahmed

Public Health Resources

BACKGROUND. American Indians and Alaska Natives (AI/AN) experience higher morbidity and mortality from primary liver cancer than other United States (US) populations, but racial misclassification in medical records results in underestimates of disease burden.

METHODS. To reduce misclassification, National Program of Cancer Registries and Surveillance, Epidemiology, and End Results data were linked with Indian Health Service (IHS) enrollment records to compare primary liver cancer incidence and stage at diagnosis between AI/AN and non-Hispanic whites (NHW) living within the regionalized IHS Contract Health Service Delivery Area counties. Incidence rates are expressed per 100,000 persons and age-adjusted by 19 age …

Cancer Among American Indians And Alaska Natives In The United States, 1999–2004, Charles Wiggins, David K. Espey, Phyllis A. Wingo, Judith S. Kaur, Robin Taylor Wilson, Judith Swan, Barry A. Miller, Melissa Jim, Janet J. Kelly, Anne P. Lanier

Public Health Resources

BACKGROUND. Cancer incidence rates vary among American Indian and Alaska Native (AI/AN) populations and often differ from rates among non-Hispanic whites (NHWs). However, the misclassification of race for AI/AN cancer cases in central cancer registries may have led to underestimates of the AI/AN cancer burden in previous reports.

METHODS. Cases diagnosed during 1999 through 2004 were identified from population- based cancer registries in the United States. Age-adjusted rates were calculated for the 25 most common sites for AI/ANs and NHWs. To minimize the misclassification of race, cancer registry records were linked with patient registration files from the Indian …