The document discusses the concepts of validity and reliability in survey design. It defines validity as how well a survey measures what it aims to measure, noting that validity can only be determined by comparison to a gold standard. The document then discusses three types of validity: content validity, criterion-related validity, and construct validity. It also discusses the concepts of sensitivity and specificity in relation to screening tests, defining sensitivity as the proportion of true positives identified and specificity as the proportion of true negatives identified. Several diagrams are presented to illustrate these concepts visually.
This document discusses objective tests, including what they are, their categories and types. Objective tests are those where the scoring rules do not allow for subjective judgments. They have selected and constructed response formats. Some common types are true/false, multiple choice, matching, fill-in-the-blank, and labeling. Objective tests are easier to score objectively but can only measure factual knowledge directly. They require careful construction to be effective.
A good test should be valid and reliable. Validity refers to how well a test measures what it intends to measure. There are three main types of validity: content validity, criterion-related validity, and construct validity. Reliability refers to the consistency of test scores. Sources of measurement error can affect reliability. Reliability is estimated through methods like test-retest, parallel forms, and internal consistency. Item analysis evaluates item difficulty and discrimination to identify questions that need improvement.
An objective test is a test that has predetermined right and wrong answers that can be marked objectively. It includes questions that require selecting an answer from choices, identifying objects or positions, or supplying brief text responses. Objective tests are popular because they are easy to prepare and take, quick to mark, and provide quantifiable results. Common types of objective test questions include true-false items, matching items, multiple choice items, and completion items.
The document discusses key qualities of measurement devices: validity, reliability, practicality, and backwash effect. It defines each quality and provides examples. Validity refers to what a test measures, and includes content, construct, criterion-related, concurrent, and predictive validity. Reliability is how consistent measurements are, including equivalency, stability, internal, and inter-rater reliability. Practicality means a test is easy to construct, administer, score and interpret. Backwash effect is a test's influence on teaching and learning.
The document outlines 9 principles of high quality assessment:
1. Clarity of learning targets - assessments should clearly define what knowledge, skills, and abilities are being measured.
2. Appropriateness of assessment methods - the right methods like written tests, projects, and observations should be used to match the learning targets.
3. Validity, reliability, fairness, positive consequences, practicality/efficiency, and ethics - assessments should have these key properties to be effective and accurate measures of learning.
Screening tests aim to identify unrecognized disease in asymptomatic individuals. An effective screening program requires a suitable disease, test, and screening process. A suitable disease is serious, progressive, treatable at an early stage, and has a detectable pre-clinical phase. An effective screening test is inexpensive, easy to administer, valid, reliable, and has acceptable sensitivity and specificity. Screening programs must consider disease prevalence, test validity, reliability, and yield to determine if screening provides benefit.
This document discusses key concepts for evaluating diagnostic tests, including sensitivity, specificity, predictive values, and likelihood ratios. Sensitivity refers to a test's ability to correctly identify individuals with the disease, while specificity refers to a test's ability to correctly identify individuals without the disease. The accuracy of a diagnostic test is determined by comparing it to a gold standard test using a 2x2 table to calculate measures like sensitivity, specificity, and predictive values. The optimal test cutoff can be selected by considering the sensitivity and specificity at different cutoff levels or by examining the overall area under the receiver operating characteristic curve.
Application of a test or a procedure to large number of population who have no symptoms of a particular disease for the purpose of determining their likelihood of having the disease.
This document discusses objective tests, including what they are, their categories and types. Objective tests are those where the scoring rules do not allow for subjective judgments. They have selected and constructed response formats. Some common types are true/false, multiple choice, matching, fill-in-the-blank, and labeling. Objective tests are easier to score objectively but can only measure factual knowledge directly. They require careful construction to be effective.
A good test should be valid and reliable. Validity refers to how well a test measures what it intends to measure. There are three main types of validity: content validity, criterion-related validity, and construct validity. Reliability refers to the consistency of test scores. Sources of measurement error can affect reliability. Reliability is estimated through methods like test-retest, parallel forms, and internal consistency. Item analysis evaluates item difficulty and discrimination to identify questions that need improvement.
An objective test is a test that has predetermined right and wrong answers that can be marked objectively. It includes questions that require selecting an answer from choices, identifying objects or positions, or supplying brief text responses. Objective tests are popular because they are easy to prepare and take, quick to mark, and provide quantifiable results. Common types of objective test questions include true-false items, matching items, multiple choice items, and completion items.
The document discusses key qualities of measurement devices: validity, reliability, practicality, and backwash effect. It defines each quality and provides examples. Validity refers to what a test measures, and includes content, construct, criterion-related, concurrent, and predictive validity. Reliability is how consistent measurements are, including equivalency, stability, internal, and inter-rater reliability. Practicality means a test is easy to construct, administer, score and interpret. Backwash effect is a test's influence on teaching and learning.
The document outlines 9 principles of high quality assessment:
1. Clarity of learning targets - assessments should clearly define what knowledge, skills, and abilities are being measured.
2. Appropriateness of assessment methods - the right methods like written tests, projects, and observations should be used to match the learning targets.
3. Validity, reliability, fairness, positive consequences, practicality/efficiency, and ethics - assessments should have these key properties to be effective and accurate measures of learning.
Screening tests aim to identify unrecognized disease in asymptomatic individuals. An effective screening program requires a suitable disease, test, and screening process. A suitable disease is serious, progressive, treatable at an early stage, and has a detectable pre-clinical phase. An effective screening test is inexpensive, easy to administer, valid, reliable, and has acceptable sensitivity and specificity. Screening programs must consider disease prevalence, test validity, reliability, and yield to determine if screening provides benefit.
This document discusses key concepts for evaluating diagnostic tests, including sensitivity, specificity, predictive values, and likelihood ratios. Sensitivity refers to a test's ability to correctly identify individuals with the disease, while specificity refers to a test's ability to correctly identify individuals without the disease. The accuracy of a diagnostic test is determined by comparing it to a gold standard test using a 2x2 table to calculate measures like sensitivity, specificity, and predictive values. The optimal test cutoff can be selected by considering the sensitivity and specificity at different cutoff levels or by examining the overall area under the receiver operating characteristic curve.
Application of a test or a procedure to large number of population who have no symptoms of a particular disease for the purpose of determining their likelihood of having the disease.
1. The document summarizes key concepts in diagnostic test accuracy including sensitivity, specificity, predictive values, prevalence, and likelihood ratios.
2. It discusses ROC curves and how they are used to compare diagnostic tests by assessing the area under the curve.
3. Issues around bias in studies of diagnostic accuracy are covered such as spectrum, verification, and incorporation bias.
Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy
This document discusses the validity and reliability of diagnostic and screening tests. It defines validity as a test's ability to accurately distinguish those with a disease from those without. Validity has two components: sensitivity and specificity. Reliability refers to a test's ability to produce consistent results regardless of who performs it. A test must be both valid and reliable to be considered good. Factors like cutoff points, disease prevalence, and multiple tests can impact validity and predictive values. Reliability is affected by intra- and inter-observer variations and can be measured using percent agreement and kappa statistics. Both validity and reliability are important for a test to provide useful information.
This document discusses key concepts regarding diagnostic and screening tests. It covers validity measures like sensitivity, specificity, predictive values, and receiver operating characteristic curves. It also addresses reliability through percent agreement and kappa statistics. The document contrasts sequential versus simultaneous use of multiple tests and examines how prevalence impacts predictive values. Finally, it outlines important factors for evaluating screening tests such as disease characteristics, test properties, and societal considerations.
This document discusses concepts related to diagnostic testing in animal disease. It defines what a diagnostic test is and discusses some key issues like the presence of false positives and negatives. It describes different categories of tests, including screening tests for healthy animals and confirmatory tests for diseased animals. Key metrics for evaluating tests are explained, such as sensitivity, specificity, predictive values, and accuracy. Factors that can impact test results like cut-off points and prevalence are also covered. The document provides examples of specific tests and discusses the trade-offs of optimizing tests for sensitivity versus specificity.
This document discusses the evaluation of diagnostic tests. It defines key terms used to evaluate tests such as sensitivity, specificity, predictive values, and likelihood ratios. It provides examples of evaluating a fine needle aspiration test for breast cancer using these measures. The document also discusses how prevalence of a disease can impact predictive values and compares two-stage versus simultaneous testing approaches.
Epidemiological method to determine utility of a diagnostic testBhoj Raj Singh
The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value (NPV). Many clinicians are frequently unclear about the practical application of these terms (1). The traditional method for teaching these concepts is based on the 2 × 2 table (Table 1). A 2 × 2 table shows results after both a diagnostic test and a definitive test (gold standard) have been performed on a pre-determined population consisting of people with the disease and those without the disease. The definitions of sensitivity, specificity, positive predictive value and NPV as expressed by letters are provided in Table 1. While 2 × 2 tables allow the calculations of sensitivity, specificity and predictive values, many clinicians find it too abstract and it is difficult to apply what it tries to teach into clinical practice as patients do not present as ‘having disease’ and ‘not having disease’. The use of the 2 × 2 table to teach these concepts also frequently creates the erroneous impression that the positive and NPVs calculated from such tables could be generalized to other populations without regard being paid to different disease prevalence. New ways of teaching these concepts have therefore been suggested.
The document discusses medical testing and how to interpret test results. It explains that all medical tests have limitations and can produce false positives or false negatives. It emphasizes that the sensitivity and specificity of a test must be determined based on appropriate study populations that represent the full spectrum of disease. Most importantly, predictive values are needed to properly interpret individual test results, as these take into account the likelihood of disease before the test.
This document provides an overview of diagnostic testing and assessing diagnostic accuracy. It defines key concepts like sensitivity, specificity, predictive values, and likelihood ratios. Sensitivity measures the ability of a test to detect true positives, or people with the disease. Specificity measures the ability to detect true negatives, or people without the disease. Positive and negative predictive values depend on disease prevalence and estimate the probability of actual disease given a test result. Likelihood ratios quantify how much a test result changes the odds of disease. The document uses examples to demonstrate calculating and interpreting these performance measures.
VALIDITY AND RELIABLITY OF A SCREENING TEST seminar 2.pptxShaliniPattanayak
A presentation shedding some insight into the tricky concepts of validity and reliability of any screening test, used in day-to-day lives, using easy and understandable language.
Validity refers to how accurately a screening test measures a disease. Key measures of validity include sensitivity, specificity, and predictive value. Sensitivity measures the percentage of true positives, specificity measures the percentage of true negatives, and predictive value refers to the probability that the test result correctly identifies whether someone has the disease or not. The prevalence of a disease in a population also affects the predictive power of screening tests. Combining multiple screening tests can increase overall sensitivity and specificity for more accurate disease detection.
Epidemiological Approaches for Evaluation of diagnostic tests.pptxBhoj Raj Singh
Diagnosis of a disease or a problem is the first step towards solution/ treatment. Clinical Diagnosis or Provisional Diagnosis is the first step in diagnosis and is done after a physical examination of the patient by a clinician. Clinical diagnosis may or may not be true and to reach Final diagnosis Laboratory Investigations using gross and microscopic pathological observations and determining the disease indicators are required. The diagnostic tests may be Non-dichotomous Diagnostic Tests (when continuous values are given by the test in a range starting from sub-normal to above-normal range) and Dichotomous Diagnostic Tests (when results are given either plus or minus, disease or no-disease). To make non- Dichotomous diagnostic test a Dichotomous one you need to establish the cut-off values based on reference values or Gold Standard test readings or with the use of Receiver operator characteristic (ROC) curves, Precision-Recall Curves, Likelihood Ratios, etc., and finally establishing statistical agreement (using Kappa values, Level of Agreement, χ2 Statistics) between the true diagnosis and laboratory diagnosis. Thereafter, the Accuracy, Precision, Bias, Sensitivity, Specificity, Positive Predictive value, and Negative Predictive value, of a diagnostic test are established for use in clinical practice. Diagnostic tests are also used to determine Prevalence (True prevalence, apparent prevalence) and Incidence of the disease to estimate the disease burden so that control measures can be implemented. There are several Phases in the development and use of a diagnostic assay starting from conceptualization of the diagnostic test, development and evaluation to determine flaws in diagnostic test use and Interpretation influencers. This presentation mainly deals with the epidemiological evaluation procedures for diagnostic tests.
This document discusses diagnostic testing and key terms related to test accuracy. It defines sensitivity as the ability of a test to correctly identify those with a condition, and specificity as the ability to correctly identify those without a condition. Sensitivity answers what percentage of sick people a test identifies, while specificity answers what percentage of well people a test identifies as negative. Predictive values depend on disease prevalence in the population and indicate the likelihood a positive or negative test result is correct. High sensitivity means fewer false negatives, while high specificity means fewer false positives.
Sensitivity, specificity, positive and negative predictiveMusthafa Peedikayil
This document defines and provides formulas to calculate sensitivity, specificity, positive predictive value, and negative predictive value for medical tests. Sensitivity measures the percentage of true positives, or how well a test detects those with a disease. Specificity measures the percentage of true negatives, or how well a test identifies those without disease. Positive predictive value refers to the probability a patient has the disease given a positive test result. Negative predictive value refers to the probability a patient does not have the disease given a negative test result. Formulas are provided using a 2x2 contingency table to calculate each value.
The document discusses evaluating diagnostic tests and summarizes key points in 3 sentences:
Diagnostic tests are evaluated based on their sensitivity, specificity, predictive values, and likelihood ratios to determine how well they identify disease when compared to a gold standard test. The performance of diagnostic tests depends on the prior probability or prevalence of the disease in the population being tested. Receiver operating characteristic (ROC) curves can be used to visualize and compare the performance of diagnostic tests by plotting the true positive rate against the false positive rate at various threshold settings.
The document discusses key concepts for evaluating diagnostic tests and techniques, including sensitivity, specificity, predictive values, and likelihood ratios. It emphasizes that diagnostic tests need to be evaluated based on their relevance, validity, and ability to help clinicians care for patients. New diagnostic tests should be properly evaluated through clinical studies using gold standard references and accounting for prevalence, blinding, and independent application of the reference standard before being adopted into routine care.
Screening involves applying a medical test to asymptomatic individuals to identify those at high risk of a disease. It aims to reduce disease burden through early detection and treatment before symptoms appear. For a disease to be suitable for screening, it must be life-threatening, treatable at an early stage, and have a high prevalence of pre-clinical cases. An ideal screening test is low-cost, easy to administer, valid, reliable, and reproducible. Screening programs must also be feasible and effective to justify their implementation.
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Oranges are a popular fruit that provide many nutritional benefits. They are an excellent source of vitamin C, supplying over 100% of the daily recommended intake in one orange. Oranges also contain antioxidants like flavonoids that can help prevent cancer, heart disease, and damage from free radicals. Regular consumption of oranges can help lower blood pressure and cholesterol while boosting the immune system and protecting against various infections and kidney stones.
1. The document summarizes key concepts in diagnostic test accuracy including sensitivity, specificity, predictive values, prevalence, and likelihood ratios.
2. It discusses ROC curves and how they are used to compare diagnostic tests by assessing the area under the curve.
3. Issues around bias in studies of diagnostic accuracy are covered such as spectrum, verification, and incorporation bias.
Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy
This document discusses the validity and reliability of diagnostic and screening tests. It defines validity as a test's ability to accurately distinguish those with a disease from those without. Validity has two components: sensitivity and specificity. Reliability refers to a test's ability to produce consistent results regardless of who performs it. A test must be both valid and reliable to be considered good. Factors like cutoff points, disease prevalence, and multiple tests can impact validity and predictive values. Reliability is affected by intra- and inter-observer variations and can be measured using percent agreement and kappa statistics. Both validity and reliability are important for a test to provide useful information.
This document discusses key concepts regarding diagnostic and screening tests. It covers validity measures like sensitivity, specificity, predictive values, and receiver operating characteristic curves. It also addresses reliability through percent agreement and kappa statistics. The document contrasts sequential versus simultaneous use of multiple tests and examines how prevalence impacts predictive values. Finally, it outlines important factors for evaluating screening tests such as disease characteristics, test properties, and societal considerations.
This document discusses concepts related to diagnostic testing in animal disease. It defines what a diagnostic test is and discusses some key issues like the presence of false positives and negatives. It describes different categories of tests, including screening tests for healthy animals and confirmatory tests for diseased animals. Key metrics for evaluating tests are explained, such as sensitivity, specificity, predictive values, and accuracy. Factors that can impact test results like cut-off points and prevalence are also covered. The document provides examples of specific tests and discusses the trade-offs of optimizing tests for sensitivity versus specificity.
This document discusses the evaluation of diagnostic tests. It defines key terms used to evaluate tests such as sensitivity, specificity, predictive values, and likelihood ratios. It provides examples of evaluating a fine needle aspiration test for breast cancer using these measures. The document also discusses how prevalence of a disease can impact predictive values and compares two-stage versus simultaneous testing approaches.
Epidemiological method to determine utility of a diagnostic testBhoj Raj Singh
The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value (NPV). Many clinicians are frequently unclear about the practical application of these terms (1). The traditional method for teaching these concepts is based on the 2 × 2 table (Table 1). A 2 × 2 table shows results after both a diagnostic test and a definitive test (gold standard) have been performed on a pre-determined population consisting of people with the disease and those without the disease. The definitions of sensitivity, specificity, positive predictive value and NPV as expressed by letters are provided in Table 1. While 2 × 2 tables allow the calculations of sensitivity, specificity and predictive values, many clinicians find it too abstract and it is difficult to apply what it tries to teach into clinical practice as patients do not present as ‘having disease’ and ‘not having disease’. The use of the 2 × 2 table to teach these concepts also frequently creates the erroneous impression that the positive and NPVs calculated from such tables could be generalized to other populations without regard being paid to different disease prevalence. New ways of teaching these concepts have therefore been suggested.
The document discusses medical testing and how to interpret test results. It explains that all medical tests have limitations and can produce false positives or false negatives. It emphasizes that the sensitivity and specificity of a test must be determined based on appropriate study populations that represent the full spectrum of disease. Most importantly, predictive values are needed to properly interpret individual test results, as these take into account the likelihood of disease before the test.
This document provides an overview of diagnostic testing and assessing diagnostic accuracy. It defines key concepts like sensitivity, specificity, predictive values, and likelihood ratios. Sensitivity measures the ability of a test to detect true positives, or people with the disease. Specificity measures the ability to detect true negatives, or people without the disease. Positive and negative predictive values depend on disease prevalence and estimate the probability of actual disease given a test result. Likelihood ratios quantify how much a test result changes the odds of disease. The document uses examples to demonstrate calculating and interpreting these performance measures.
VALIDITY AND RELIABLITY OF A SCREENING TEST seminar 2.pptxShaliniPattanayak
A presentation shedding some insight into the tricky concepts of validity and reliability of any screening test, used in day-to-day lives, using easy and understandable language.
Validity refers to how accurately a screening test measures a disease. Key measures of validity include sensitivity, specificity, and predictive value. Sensitivity measures the percentage of true positives, specificity measures the percentage of true negatives, and predictive value refers to the probability that the test result correctly identifies whether someone has the disease or not. The prevalence of a disease in a population also affects the predictive power of screening tests. Combining multiple screening tests can increase overall sensitivity and specificity for more accurate disease detection.
Epidemiological Approaches for Evaluation of diagnostic tests.pptxBhoj Raj Singh
Diagnosis of a disease or a problem is the first step towards solution/ treatment. Clinical Diagnosis or Provisional Diagnosis is the first step in diagnosis and is done after a physical examination of the patient by a clinician. Clinical diagnosis may or may not be true and to reach Final diagnosis Laboratory Investigations using gross and microscopic pathological observations and determining the disease indicators are required. The diagnostic tests may be Non-dichotomous Diagnostic Tests (when continuous values are given by the test in a range starting from sub-normal to above-normal range) and Dichotomous Diagnostic Tests (when results are given either plus or minus, disease or no-disease). To make non- Dichotomous diagnostic test a Dichotomous one you need to establish the cut-off values based on reference values or Gold Standard test readings or with the use of Receiver operator characteristic (ROC) curves, Precision-Recall Curves, Likelihood Ratios, etc., and finally establishing statistical agreement (using Kappa values, Level of Agreement, χ2 Statistics) between the true diagnosis and laboratory diagnosis. Thereafter, the Accuracy, Precision, Bias, Sensitivity, Specificity, Positive Predictive value, and Negative Predictive value, of a diagnostic test are established for use in clinical practice. Diagnostic tests are also used to determine Prevalence (True prevalence, apparent prevalence) and Incidence of the disease to estimate the disease burden so that control measures can be implemented. There are several Phases in the development and use of a diagnostic assay starting from conceptualization of the diagnostic test, development and evaluation to determine flaws in diagnostic test use and Interpretation influencers. This presentation mainly deals with the epidemiological evaluation procedures for diagnostic tests.
This document discusses diagnostic testing and key terms related to test accuracy. It defines sensitivity as the ability of a test to correctly identify those with a condition, and specificity as the ability to correctly identify those without a condition. Sensitivity answers what percentage of sick people a test identifies, while specificity answers what percentage of well people a test identifies as negative. Predictive values depend on disease prevalence in the population and indicate the likelihood a positive or negative test result is correct. High sensitivity means fewer false negatives, while high specificity means fewer false positives.
Sensitivity, specificity, positive and negative predictiveMusthafa Peedikayil
This document defines and provides formulas to calculate sensitivity, specificity, positive predictive value, and negative predictive value for medical tests. Sensitivity measures the percentage of true positives, or how well a test detects those with a disease. Specificity measures the percentage of true negatives, or how well a test identifies those without disease. Positive predictive value refers to the probability a patient has the disease given a positive test result. Negative predictive value refers to the probability a patient does not have the disease given a negative test result. Formulas are provided using a 2x2 contingency table to calculate each value.
The document discusses evaluating diagnostic tests and summarizes key points in 3 sentences:
Diagnostic tests are evaluated based on their sensitivity, specificity, predictive values, and likelihood ratios to determine how well they identify disease when compared to a gold standard test. The performance of diagnostic tests depends on the prior probability or prevalence of the disease in the population being tested. Receiver operating characteristic (ROC) curves can be used to visualize and compare the performance of diagnostic tests by plotting the true positive rate against the false positive rate at various threshold settings.
The document discusses key concepts for evaluating diagnostic tests and techniques, including sensitivity, specificity, predictive values, and likelihood ratios. It emphasizes that diagnostic tests need to be evaluated based on their relevance, validity, and ability to help clinicians care for patients. New diagnostic tests should be properly evaluated through clinical studies using gold standard references and accounting for prevalence, blinding, and independent application of the reference standard before being adopted into routine care.
Screening involves applying a medical test to asymptomatic individuals to identify those at high risk of a disease. It aims to reduce disease burden through early detection and treatment before symptoms appear. For a disease to be suitable for screening, it must be life-threatening, treatable at an early stage, and have a high prevalence of pre-clinical cases. An ideal screening test is low-cost, easy to administer, valid, reliable, and reproducible. Screening programs must also be feasible and effective to justify their implementation.
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Here are the calculations for the predictive values of a positive HIV test with 95% sensitivity and 98% specificity in populations with different prevalence rates:
1) Prevalence of HIV in blood donors = 2%
Total tested = 1000
With HIV = 1000 * 0.02 = 20
Without HIV = 1000 - 20 = 980
Sensitivity = 95%
Specificity = 98%
True Positives (a) = Sensitivity * With HIV = 0.95 * 20 = 19
False Positives (b) = (1 - Specificity) * Without HIV = 0.02 * 980 = 20
False Negatives (c) = (1 - Sensitivity) * With HIV = 0
Oranges are a popular fruit that provide many nutritional benefits. They are an excellent source of vitamin C, supplying over 100% of the daily recommended intake in one orange. Oranges also contain antioxidants like flavonoids that can help prevent cancer, heart disease, and damage from free radicals. Regular consumption of oranges can help lower blood pressure and cholesterol while boosting the immune system and protecting against various infections and kidney stones.
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- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
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TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Kat...rightmanforbloodline
TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Katzung, Verified Chapters 1 - 66, Complete Newest Version.
TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Katzung, Verified Chapters 1 - 66, Complete Newest Version.
TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Katzung, Verified Chapters 1 - 66, Complete Newest Version.
TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Katzung, Verified Chapters 1 - 66, Complete Newest Version.
3. Definition
How well a survey measures what it sets out to
measure.
Validity can be determined only if there is a
reference procedure of “gold standard”.
Food–frequency questionnaires food diaries
Birth weight hospital record.
5. Screening test
Validity – get the correct result
Sensitive – correctly classify cases
Specificity – correctly classify non-cases
[screening and diagnosis are not identical]
8. 2 cases / month
OO
OO O
O
O
O O OO
O
9. Pre-detectable preclinical clinical old
OO
OO O
O O
O
O O
O O OO
O
10. Pre-detectable pre-clinical clinical old
O O O OO
OO O O O O
O O O O
O O O O
O O O O O
O O OOO
O O O O O
O O O O
11. What is the prevalence of “the condition”?
O O O OO
OO O O O O
O O O O
O O O O
O O O O O
O O OOO
O O O O O
O O O O
12. Sensitivity of a screening test
Probability (proportion) of
correct classification of detectable, pre-
clinical cases
13. Pre-detectable pre-clinical clinical old
(8) (10) (6) (14)
O O O OO
OO O O O O
O O O O
O O O O
O O O O O
O O OOO
O O O O O
O O O O
14. Correctly classified
Sensitivity: –––––––––––––––––––––––––––
Total detectable pre-clinical (10)
O O O OO
OO O O O O
O O O O
O O O O
O O O O O
O O OOO
O O O O O
O O O O
15. Specificity of a screening test
Probability (proportion) of
correct classification of noncases
Noncases identified / all noncases
16. Pre-detectable pre-clinical clinical old
(8) (10) (6) (14)
O O O OO
OO O O O O
O O O O
O O O O
O O O O O
O O OOO
O O O O O
O O O O
17. Correctly classified
Specificity: –––––––––––––––––––––––––––––
Total non-cases (& pre-detect) (162 or 170)
O O O OO
OO O O O O
O O O O
O O O O
O O O O O
O O OOO
O O O O O
O O O O
18. True Disease Status
Cases Non-cases
True False
Positive positive positive a+b
Screening
ab
Test c d True
Results False c+d
Negative negative
negative
a+c b+d
True positives a
Sensitivity = =
All cases a+c
True negatives d
Specificity = =
All non-cases b+d
19. True Disease Status
Cases Non-cases
Positive 140 1,000 1,140
Screening
ab
Test c d
Results 19,000 19,060
Negative 60
200 20,000
True positives 140
Sensitivity = = = 70%
All cases 200
Specificity = True negatives = 19,000 = 95%
All non-cases 20,000
20. Interpreting test results: predictive value
Probability (proportion) of those tested who
are correctly classified
Cases identified / all positive tests
Noncases identified / all negative tests
21. True Disease Status
Cases Non-cases
True False
Positive positive positive a+b
Screening
ab
Test c d True
Results False c+d
Negative negative
negative
a+c b+d
True positives a
PPV = =
All positives a+b
True negatives d
NPV = =
All negatives c+d
22. True Disease Status
Cases Non-cases
Positive 140 1,000 1,140
Screening
ab
Test c d
Results 19,000 19,060
Negative 60
200 20,000
True positives 140
PPV = = = 12.3%
All positives 1,140
19,000
NPV = True negatives = = 99.7%
All negatives 19,060
24. Receiver operating characteristic (ROC) curve
Not aIl tests give a simple yes/no result. Some
yield results that are numerical values along a
continuous scale of measurement. in these
situations, high sensitivity is obtained at the
cost of low specificity and vice versa
26. Reliability
Repeatability – get same result
Each time
From each instrument
From each rater
If don’t know correct result, then can
examine reliability only.
27. Definition
The degree of stability exhibited when a
measurement is repeated under identical
conditions
Lack of reliability may arise from divergences
between observers or instruments of
measurement or instability of the attribute
being measured
(from Last. Dictionary of Epidemiology.
30. EXAMPLE OF PERCENT
AGREEMENT
Two physicians are each given a
set of 100 X-rays to look at independently
and asked to judge whether pneumonia is
present or absent. When both sets of
diagnoses are tallied, it is found that 95%
of the diagnoses are the same.
31. IS PERCENT AGREEMENT GOOD
ENOUGH?
Do these two physicians exhibit high
diagnostic reliability?
Can there be 95% agreement between
two observers without really having
good reliability?
32. Compare the two tables below:
Table 2 Table 1
MD#1 MD#1
Yes No Yes No
Yes 1 3 Yes 43 3
MD#2 MD#2
No 2 94 No 2 52
In both instances, the physicians agree
95% of the time. Are the two physicians
equally reliable in the two tables?
33. USE OF THE KAPPA
STATISTIC TO ASSESS
RELIABILITY
Kappa is a widely used test of
inter or intra-observer agreement
(or reliability) which corrects for
chance agreement.
34. KAPPA VARIES FROM + 1 to - 1
+ 1 means that the two observers are perfectly
reliable. They classify everyone exactly the
same way.
0 means there is no relationship at all
between the two observer’s
classifications, above the agreement that
would be expected by chance.
- 1 means the two observers classify exactly the
opposite of each other. If one observer says
yes, the other always says no.
35. GUIDE TO USE OF KAPPAS IN
EPIDEMIOLOGY AND MEDICINE
Kappa > .80 is considered excellent
Kappa .60 - .80 is considered good
Kappa .40 - .60 is considered fair
Kappa < .40 is considered poor
36. WAY TO CALCULATE KAPPA
1. Calculate observed agreement (cells in
which the observers agree/total cells). In
both table 1 and table 2 it is 95%
2. Calculate expected agreement (chance
agreement) based on the marginal totals
38. OBSERVED MD #1 How do we calculate •
the N expected by
chance in each cell?
Yes No
We assume that •
MD#2 Yes 1 3 4 each cell should
No 2 94 96 reflect the marginal
distributions, i.e. the
3 97 100
proportion of yes
and no answers
EXPECTED MD #1 should be the same
within the four-fold
table as in the
Yes No marginal totals.
MD#2 Yes 4
No 96
3 97 100
39. To do this, we find the proportion of answers in either
the column (3% and 97%, yes and no respectively for
MD #1) or row (4% and 96% yes and no respectively
for MD #2) marginal totals, and apply one of the two
proportions to the other marginal total. For example,
96% of the row totals are in the “No” category.
Therefore, by chance 96% of MD #1’s “No’s” should
also be in the “No” column. 96% of 97 is 93.12.
MD#1
EXPECTED
Yes No
MD#2 Yes 4
No 93.12 96
3 97 100
40. By subtraction, all other cells fill in automatically,
and each yes/no distribution reflects the marginal
distribution. Any cell could have been used to make
the calculation, because once one cell is specified in
a 2x2 table with fixed marginal distributions, all
other cells are also specified.
EXPECTED MD #1
Yes No
MD#2 Yes 0.12 3.88 4
No 2.88 93.12 96
3 97 100
41. Now you can see that just by the operation
of chance, 93.24 of the 100 observations
should have been agreed to by the two
observers. (93.12 + 0.12)
EXPECTED MD #1
Yes No
MD#2 Yes 0.12 3.88 4
No 2.88 93.12 96
3 97 100
42. Below is the formula for calculating Kappa
from expected agreement
Observed agreement - Expected Agreement
1 - Expected Agreement
95% - 93.24% = 1.76% = .26
1 - 93.24% 6.76%
43. How good is a Kappa of 0.26?
Kappa > .80 is considered excellent
Kappa .60 - .80 is considered good
Kappa .40 - .60 is considered fair
Kappa < .40 is considered poor
44. In the second example, the observed
agreement was also 95%, but the marginal
totals were very different
ACTUAL MD #1
Yes No
MD#2 Yes 46
No 54
45 55 100
45. Using the same procedure as before, we calculate the expected
N in any one cell, based on the marginal totals. For example, the
lower right cell is 54% of 55, which is 29.7
ACTUAL MD #1
Yes No
MD#2 Yes 46
No 29.7 54
45 55 100
46. And, by subtraction the other cells are
as below. The cells which indicate
agreement are highlighted in yellow,
and add up to 50.4%
ACTUAL MD #1
Yes No
MD#2 Yes 20.7 25.3 46
No 24.3 29.7 54
45 55 100
47. Enter the two agreements into the formula
Observed agreement - Expected Agreemen
1 - Expected Agreement
95% - 50.4% = 44.6% = .90
1 - 50.4% 49.6%
In this example, the observers have the
same % agreement, but now they are
much different from chance.
Kappa of 0.90 is considered excellent