2 edition of Statistical discrimination with disease categories subject to misclassification found in the catalog.
Statistical discrimination with disease categories subject to misclassification
Rachel M. Hilliam
Thesis (Ph.D.) - De Montfort University, Leicester 2000.
|Statement||Rachel M. Hilliam.|
|Contributions||De Montfort University.|
Misclassification Costs and Value Labels. This dialog box is not available unless at least two values of the categorical dependent variable have defined value labels. See the topic To specify value labels for more information. To Specify Misclassification Costs. This feature requires the Decision Trees option. From the menus choose.
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Description. Statistical Methods of Discrimination and Classification: Advances in Theory and Applications is a collection of papers that tackles the multivariate problems of discriminating and classifying subjects into exclusive population.
The book presents 13 papers that cover that advancement in the statistical procedure Book Edition: 1. Statistical discrimination with disease categories subject to misclassification.
Author: Hilliam, Rachel M. ISNI: Awarding Body: De Montfort University Current Institution: De Montfort University Date of Award: Availability of Full Text. Disease and discrimination are processes linked to class in the early American colonies. Many early colonists fell victim to mass sickness as Old and New World systems collided and new social, political, economic, and ecological dynamics allowed disease to : Dale L.
Hutchinson. The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g.
diseased individuals are incorrectly classified/labeled as healthy by: 1. Presents different approaches to discrimination and classification problems from a statistical perspective. Provides computer projects concentrating on the most widely used and important algorithms, numerical examples, and theoretical questions reinforce to further develop the ideas introduced in the text.
Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and cat.
Then, while statistical discrimination might be due to SES, it would still be accurate to say that African Americans were subject to statistical discrimination in relation to whites.
This issue is analogous to the debate about the interpretation of racial/ethnic disparities in health by: Statistical discrimination is an economic theory that attempts to explain racial and gender inequality. The theory attempts to explain the existence and endurance of racial profiling and gender-based discrimination in the labor market even in the absence of overt prejudice on the part of the economic actors involved.
The pioneering of statistical discrimination theory Author: Mike Moffatt. Statistical Discrimination. Judging an individual on the average characteristics of his or her group. Occupational crowding.
Crowding some groups of workers into a limited number of jobs. Affirmative Action. Efforts to provide equal opportunities in terms of employment and education to underrepresented groups of people.
Prevalence estimation subject to misclassification: The mis-substitution bias and some remedies Article in Statistics in Medicine 33(25) November with Reads How we.
Misclassification in Logistic Regression with Discrete Covariates. We study the effect of misclassification of a binary covariate on the parameters of a logistic regression model. In particular we consider 2 × 2 × 2 tables.
We assume that a binary covariate is subject to misclassification that may depend on the observed outcome. categories into single categories is meaningless if the race does not accurately reflect an individuals’ heritage.
This article will illustrate the effects of racial misclassification on statistical measures commonly used to characterize disease burden in a population.
Racial misclassification can be described as the incorrect coding of an. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. "The topic addressed by this book is an important one.
This book shows that error-prone measurements may create serious biases and offers Bayesian approaches to attempt unbiased estimation, or 'adjustments'.
This is a useful book if you have data containing errors or if you have an interest in statistical theory of errors of by: Because assays are usually imperfect, misclassification of disease status is a major challenge in prevalence estimation.
To account for possible misclassification, it is usually assumed that the sensitivity and specificity of the assay are Cited by: 9.
Statistical discrimination (economics) Linear discriminant analysis (statistics) This disambiguation page lists articles associated with the title Statistical discrimination.
If an internal link led you here, you may wish to change the link to point directly to the. A 3 per cent non-differential misclassification rate would be sufficient to cause this risk level to lose statistical significance. Misreporting of smoking status, which is influenced by a number of social and cultural factors is, obviously, especially problematic for ex-smokers, since there is no known biomarker of lifetime tobacco use.
If the form of disease misclassification can be defined or constrained to one type, the impact on the results is at least predictable if not correctable.
When both false positive and false negative errors are present in a dichotomous outcome and those errors are nondifferential with respect to exposure, regardless of the design or measure of.
Non-differential misclassification • Inaccuracies with respect to disease classification are independent of exposure. • Or, inaccuracies with respect to exposure are independent of disease. • Will bias towards the null if the exposure has two categories.
• Non-differential misclassification makes the groups more similar. Two types of disease misclassification can arise in an exposure–disease association study: nondifferential and differential. Nondifferential misclassification occurs when neither sensitivity nor specificity for disease classification varies by exposure by: Misclassification rate observed in the non-linear scenario simulation studies.
Solid red line represents the median of each distribution, while dashed red. Introduction. A screening test (sometimes termed medical surveillance) is a medical test or procedure performed on members (subjects) of a defined 1 asymptomatic population or population subgroup to assess the likelihood of their members having a particular disease.
2 With few exceptions, screening tests do not diagnose the subjects Cited by: Download Citation | Case–Control Studies with Measurement Error or Misclassification | In epidemiological research case–control studies provide an important.
Theories of Statistical Discrimination and Affirmative Action: A Survey Hanming Fang, Andrea Moro. NBER Working Paper No. Issued in April NBER Program(s):Labor Studies, Public Economics This chapter surveys the theoretical literature on statistical discrimination and affirmative action.
12 Supervised Learning ⊕ In a supervised learning setting, we have a yardstick or plumbline to judge how well we are doing: the response itself. A frequent question in biological and biomedical applications is whether a property of interest (say, disease type, cell type, the prognosis of a patient) can be “predicted”, given one or more other properties, called the predictors.
unexposed subjects. Non-differential misclassification of the health outcome status occurs in a cohort study when a study subject who develops the health outcome is equally misclassified among exposed and unexposed cohorts. Effect of non-differential misclassification of exposure Non-differential misclassification biases the risk ratio, rate.
Correcting for misclassification for a monotone disease process with an application in dental research we evaluate the performance of binary Markov models in which the response variable is subject to an unconstrained misclassification process and follows a monotone or progressive behavior.
Journal of the American Statistical Association. selection of subject quality of info obtained misclassification confounding. major types of bias. selection bias information bias/misclassification (observer, subject, instrument) confounding.
selection bias definition. (pulmonary disease and smoking effect. Physician might be more prone to select the subjects who smoke, or might look at. Becker and Ms. Stern argue that, as misclassification cases continue to be filed and pursued by the WHD, as well as by private plaintiffs under the FLSA, statistical analysis of these data can be a valuable tool for companies in addressing allegations that.
Statistical discrimination is a theorized behavior in which racial or gender inequality results when economic agents (consumers, workers, employers, etc.) have imperfect information about individuals they interact with.
According to this theory, inequality may exist and persist between demographic groups even when economic agents are rational and non-prejudiced.
Non-Differential Misclassification (of disease) I’ve mentioned that a passive registry such as the FBDR may suffer from under-ascertainment of birth defects. Imagine that there is a global 10% under-ascertainment in infants of both foreign-born and U.S.-born women.
What would you observe. OR (observed) = always towards the null. Search the world's most comprehensive index of full-text books. My library. Disease and discrimination are processes linked to class in the early American colonies.
Many early colonists fell victim to mass sickness as Old and New World systems collided and new social, political, economic, and ecological dynamics allowed disease to by: 1.
Includes statistical discrimination and taste based. Statistical Discrimination. Use of statistics regarding a race to convey information. AKA racial profiling. Reasonable racist. Justification for racist views on grounds that race can be correlated with other things.
misclassification - Free download as Powerpoint Presentation .ppt), PDF File .pdf), Text File .txt) or view presentation slides online. Epidemiology. In the taste for discrimination model, prejudice is factored in to the decision and appears to be put ahead of profits. With statistical discrimination, people can discriminate against a group even if they have no prejudice against the group and the intent is to maximize profit.
Statistical discrimination has particular implications allowing for empirical work testing for the existence and quantitative importance of communication-based disparities in health care use. The preliminary evidence is persuasive enough that the hypothesis of statistical discrimination should be subject to empirical by: Principles of Epidemiology Lecture 5 than the comparison group Information Bias (cont.) Misclassification bias – errors are made in classifying either disease or exposure status Types of Misclassification Bias Differential misclassification – Errors in measurement are one way only Example: Measurement bias – instrumentation may be.
A flaw in measuring exposure, covariate, or outcome variables that results in different quality (accuracy) of information between comparison groups. The occurrence of information biases may not be independent of the occurrence of selection biases.
Bias in an estimate arising from measurement errors." 1 Misclassification. Sexual Activity and Contraceptive Use Among Teenagers Aged 15–19 in the United States, –Data Brief No. (5/6/); Electronic Cigarette Use Among U.S. Adults, Data Brief No. (4/30/); Marriage Rates in the United States, –NCHS Health E-Stat (4/29/); National Health and Nutrition Examination Survey, − Sample Design.
Misclassification (information bias) Misclassification refers to the classification of an individual, a value or an attribute into a category other than that to which it should be assigned . The misclassification of exposure or disease status can be considered as either differential or .Bias that can occur if patients who are exposed are more likely to be diagnosed with a disease.
This bias occurs when the people who play a role in or are responsible for, disease ascertainment base their diagnosis on whether or not the participant has the exposure of interest.
In, legal induced abortions were reported to CDC from 48 reporting areas. The abortion rate for was abortions per 1, women aged 15–44 years, and the abortion ratio was abortions per 1, live births.
Compared withthe total number and rate of reported abortions fell by 2%, and the abortion ratio decreased.