Lecture on validation measures in epidemiology for master students in publiv health and epidemiology at Karolinska Institutet in Stockholm, Sweden on 24 October 2013.
The document discusses various study designs used in epidemiology and statistics, including observational and experimental designs. It provides details on descriptive and analytical observational studies. Descriptive studies generate hypotheses, while analytical studies allow determination of causal associations by including a comparison or control group. Experimental designs are randomized studies that can establish causal relationships. The document also covers topics like odds ratios, relative risks, attributable risks, chi-square tests, sensitivity and specificity in diagnostic testing.
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 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.
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 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.
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
Probability.pdf.pdf and Statistics for RSakhileKhoza2
This document discusses probability and diagnostic tests from a statistical perspective. It covers:
1. Types of probability including frequency, model-based, and subjective probabilities.
2. Properties of probability including that all probabilities lie between 0 and 1.
3. Diagnostic tests which aim to determine the probability of disease. Sensitivity measures the probability of a positive test given disease, while specificity measures the probability of a negative test given no disease.
4. Analysis of diagnostic tests using 2x2 tables to calculate measures like sensitivity, specificity, predictive values, and likelihood ratios. Distributions like the normal distribution are also discussed.
The document discusses various study designs used in epidemiology and statistics, including observational and experimental designs. It provides details on descriptive and analytical observational studies. Descriptive studies generate hypotheses, while analytical studies allow determination of causal associations by including a comparison or control group. Experimental designs are randomized studies that can establish causal relationships. The document also covers topics like odds ratios, relative risks, attributable risks, chi-square tests, sensitivity and specificity in diagnostic testing.
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 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.
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 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.
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
Probability.pdf.pdf and Statistics for RSakhileKhoza2
This document discusses probability and diagnostic tests from a statistical perspective. It covers:
1. Types of probability including frequency, model-based, and subjective probabilities.
2. Properties of probability including that all probabilities lie between 0 and 1.
3. Diagnostic tests which aim to determine the probability of disease. Sensitivity measures the probability of a positive test given disease, while specificity measures the probability of a negative test given no disease.
4. Analysis of diagnostic tests using 2x2 tables to calculate measures like sensitivity, specificity, predictive values, and likelihood ratios. Distributions like the normal distribution are also discussed.
Validity and reliability expressesions means as to how measurements and diagnostic approaches can more efficiently and maintaning the accuracy with many repeated tests. In validity we basically speak of specificity and sensitivity of tests, which can be affected by prevalence.
This document discusses screening and diagnostic tests. It defines screening and diagnostic tests as tools used to distinguish people who have a disease from those who do not. The quality and accuracy of these tests is important to understand. Tests are evaluated based on their sensitivity, specificity, predictive values, and likelihood ratios compared to a gold standard. Factors like disease prevalence can impact predictive values. Receiver operating characteristic curves are used to evaluate test performance across all thresholds. Screening tests aim to identify disease early but must account for biases and show effectiveness of interventions.
Critical appraisal of diagnostic studiesSamir Haffar
This document summarizes the steps involved in critically appraising a diagnostic study. It discusses formulating a clinical question using PICO (Patient, Intervention, Comparison, Outcome), searching for evidence, and assessing the validity and results of included studies. Key points addressed include evaluating a diagnostic test compared to a gold standard, calculating measures of diagnostic accuracy like sensitivity and specificity, and interpreting likelihood ratios and predictive values. The document uses an example of using ferritin levels to diagnose iron deficiency anemia in elderly patients to demonstrate these concepts.
This document discusses screening and its key aspects. It defines screening as identifying unrecognized disease through simple tests applied rapidly to large populations. Screening is important because many diseases present asymptomatically initially. The document contrasts screening tests with diagnostic tests, and describes different types of screening like mass, high-risk, and opportunistic screening. It outlines criteria for introducing screening programs and evaluating screening tests. Factors that impact screening test validity, biases in screening evaluations, and the ethics and economics of screening are also covered briefly.
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 key concepts in biostatistics. It defines biostatistics as the application of statistical methods in the fields of biology, public health, and medicine. Some key points covered include:
- The types of data: qualitative, quantitative, discrete, continuous
- Descriptive statistics like mean, median, and mode
- Inferential statistics like hypothesis testing and estimating parameters
- Important statistical tests like t-tests, ANOVA, and chi-squared tests
- Measures of diagnostic accuracy like sensitivity, specificity, and predictive values
- The process of determining sample size for studies based on factors like confidence interval, power, and allowable error.
Screening for Disease (Epidemiology)
Define screening
Describe the aims and objectives of the screening
Describe the differences between Screening & Diagnostic tests
List the uses of screening
Explain the types of screening, criteria for screening
Discuss the Validity of the screening test
Calculate and interpret the evaluation of the screening test
This document discusses disease screening and provides information on various aspects of screening programs and tests. It defines screening as actively searching for unrecognized disease in apparently healthy individuals using simple tests. The key points are:
- Screening is part of secondary prevention and aims to detect diseases early when they may be still curable. It involves testing populations, not individuals with symptoms.
- An ideal screening test is both highly sensitive and specific, but in practice these factors typically have an inverse relationship. Sensitivity and specificity can be adjusted by changing the test cutoff criteria.
- For a screening program to be effective, the disease must be an important health problem that can be detected early and treated effectively to improve outcomes. The screening test
This document provides an overview of case-control study design. It defines key elements such as cases and controls, and explains how exposures are measured and odds ratios are calculated to evaluate associations between exposures and diseases. Some advantages are that case-control studies are relatively inexpensive and allow evaluation of multiple exposures, but limitations include potential for recall and selection bias. Examples provided demonstrate how case-control studies can identify risk factors for conditions like venous thrombosis from oral contraceptive use.
Describing the performance of a diagnostic testAmany El-seoud
This document discusses various methods used to define diagnostic test results as normal or abnormal. It describes qualitative and quantitative tests, and issues with defining normals based on cultural opinions, percentiles, or presence of risk factors. Common statistical approaches like normal distribution curves and their limitations are explained. The document also covers predictive value, using multiple tests, and receiver operating characteristic curves to evaluate diagnostic tests.
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.
Evaluating a diagnostic test presentation www.eyenirvaan.com - part 1Eyenirvaan
This document discusses key concepts for evaluating diagnostic tests, including accuracy, precision, sensitivity, specificity, and predictive values. It defines a diagnostic test as one that provides evidence for or against a pathology. Key factors for evaluating tests are described as accuracy, precision, sensitivity, specificity, and predictive values. Accuracy refers to how close a test value is to the gold standard, while precision refers to reproducibility of values. Sensitivity and specificity measure a test's ability to correctly identify diseases and non-diseases. Predictive values measure the probability that a positive or negative test result correctly identifies a disease or non-disease. Examples are provided to illustrate these concepts.
This document provides an overview of inferential statistics presented by Dr. Mandar Baviskar. It begins by defining inferential statistics and explaining why they are needed when examining samples rather than entire populations. The document then covers key aspects of inferential statistics including tests of significance, p-values, limitations of statistical significance, and parametric vs non-parametric tests. Examples are provided to demonstrate selecting the appropriate test, interpreting outputs, and statistical fallacies to avoid. The presentation concludes by emphasizing the importance of consulting statisticians and having a planned analysis before data collection.
CAT 1 -MPH 5101 - FOUNDATIONS OF EPIDEMIOLOGY (1).pptxShafici Almis
1. Validity and reliability are important concepts for evaluating diagnostic and screening tests. Validity refers to a test's ability to accurately measure what it is intended to, while reliability is its consistency.
2. Key validity measures include sensitivity (ability to correctly identify those with the disease), specificity (ability to correctly identify those without the disease), and use of a gold standard for comparison.
3. Reliability is assessed through measures like positive and negative predictive values, which indicate the probability that a positive or negative test result accurately reflects disease status. Contingency tables allow calculating these measures from data on true positives, false positives, and other outcomes.
This document discusses statistical concepts and tests relevant to epidemiology and biomedical research. It begins by defining key terms like mean, standard deviation, confidence intervals, and p-values. It then discusses different types of data and variables, measures of central tendency, the central limit theorem, and applications of standard error. The document provides examples of choosing appropriate statistical tests for different study designs, including t-tests, ANOVA, chi-square, correlation, and comparing means between two or more groups. Finally, it presents a case study analyzing water-borne disease deaths before and after a water supply installation using appropriate statistical tests.
Overview of different statistical tests used in epidemiologicalshefali jain
This document provides an overview of different statistical tests used in epidemiological studies and their applications. It discusses topics such as data types (quantitative, categorical), variables, statistics, null and alternative hypotheses, errors in significance testing, and choices between parametric and nonparametric tests. The key information provided includes classifications of variable types, definitions of common statistics, explanations of hypotheses testing and p-values, and guidance on selecting appropriate tests based on the scale and distribution of the data.
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 defines and describes different types of variables that are commonly used in research and statistics. It discusses variables as things that are measured, manipulated, or controlled in studies. The main types discussed are:
- Independent variables that can be manipulated or controlled by researchers to study their effects on dependent variables.
- Dependent variables that are outcomes or effects that change in relation to independent variables.
- Confounding variables that are associated with both the exposure/independent variable and the outcome/dependent variable and can distort the observed relationship between them.
- Control variables that are held constant to assess relationships between other variables.
- Intervening variables that hypothetically explain causal links between other variables but cannot
Validity and reliability expressesions means as to how measurements and diagnostic approaches can more efficiently and maintaning the accuracy with many repeated tests. In validity we basically speak of specificity and sensitivity of tests, which can be affected by prevalence.
This document discusses screening and diagnostic tests. It defines screening and diagnostic tests as tools used to distinguish people who have a disease from those who do not. The quality and accuracy of these tests is important to understand. Tests are evaluated based on their sensitivity, specificity, predictive values, and likelihood ratios compared to a gold standard. Factors like disease prevalence can impact predictive values. Receiver operating characteristic curves are used to evaluate test performance across all thresholds. Screening tests aim to identify disease early but must account for biases and show effectiveness of interventions.
Critical appraisal of diagnostic studiesSamir Haffar
This document summarizes the steps involved in critically appraising a diagnostic study. It discusses formulating a clinical question using PICO (Patient, Intervention, Comparison, Outcome), searching for evidence, and assessing the validity and results of included studies. Key points addressed include evaluating a diagnostic test compared to a gold standard, calculating measures of diagnostic accuracy like sensitivity and specificity, and interpreting likelihood ratios and predictive values. The document uses an example of using ferritin levels to diagnose iron deficiency anemia in elderly patients to demonstrate these concepts.
This document discusses screening and its key aspects. It defines screening as identifying unrecognized disease through simple tests applied rapidly to large populations. Screening is important because many diseases present asymptomatically initially. The document contrasts screening tests with diagnostic tests, and describes different types of screening like mass, high-risk, and opportunistic screening. It outlines criteria for introducing screening programs and evaluating screening tests. Factors that impact screening test validity, biases in screening evaluations, and the ethics and economics of screening are also covered briefly.
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 key concepts in biostatistics. It defines biostatistics as the application of statistical methods in the fields of biology, public health, and medicine. Some key points covered include:
- The types of data: qualitative, quantitative, discrete, continuous
- Descriptive statistics like mean, median, and mode
- Inferential statistics like hypothesis testing and estimating parameters
- Important statistical tests like t-tests, ANOVA, and chi-squared tests
- Measures of diagnostic accuracy like sensitivity, specificity, and predictive values
- The process of determining sample size for studies based on factors like confidence interval, power, and allowable error.
Screening for Disease (Epidemiology)
Define screening
Describe the aims and objectives of the screening
Describe the differences between Screening & Diagnostic tests
List the uses of screening
Explain the types of screening, criteria for screening
Discuss the Validity of the screening test
Calculate and interpret the evaluation of the screening test
This document discusses disease screening and provides information on various aspects of screening programs and tests. It defines screening as actively searching for unrecognized disease in apparently healthy individuals using simple tests. The key points are:
- Screening is part of secondary prevention and aims to detect diseases early when they may be still curable. It involves testing populations, not individuals with symptoms.
- An ideal screening test is both highly sensitive and specific, but in practice these factors typically have an inverse relationship. Sensitivity and specificity can be adjusted by changing the test cutoff criteria.
- For a screening program to be effective, the disease must be an important health problem that can be detected early and treated effectively to improve outcomes. The screening test
This document provides an overview of case-control study design. It defines key elements such as cases and controls, and explains how exposures are measured and odds ratios are calculated to evaluate associations between exposures and diseases. Some advantages are that case-control studies are relatively inexpensive and allow evaluation of multiple exposures, but limitations include potential for recall and selection bias. Examples provided demonstrate how case-control studies can identify risk factors for conditions like venous thrombosis from oral contraceptive use.
Describing the performance of a diagnostic testAmany El-seoud
This document discusses various methods used to define diagnostic test results as normal or abnormal. It describes qualitative and quantitative tests, and issues with defining normals based on cultural opinions, percentiles, or presence of risk factors. Common statistical approaches like normal distribution curves and their limitations are explained. The document also covers predictive value, using multiple tests, and receiver operating characteristic curves to evaluate diagnostic tests.
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.
Evaluating a diagnostic test presentation www.eyenirvaan.com - part 1Eyenirvaan
This document discusses key concepts for evaluating diagnostic tests, including accuracy, precision, sensitivity, specificity, and predictive values. It defines a diagnostic test as one that provides evidence for or against a pathology. Key factors for evaluating tests are described as accuracy, precision, sensitivity, specificity, and predictive values. Accuracy refers to how close a test value is to the gold standard, while precision refers to reproducibility of values. Sensitivity and specificity measure a test's ability to correctly identify diseases and non-diseases. Predictive values measure the probability that a positive or negative test result correctly identifies a disease or non-disease. Examples are provided to illustrate these concepts.
This document provides an overview of inferential statistics presented by Dr. Mandar Baviskar. It begins by defining inferential statistics and explaining why they are needed when examining samples rather than entire populations. The document then covers key aspects of inferential statistics including tests of significance, p-values, limitations of statistical significance, and parametric vs non-parametric tests. Examples are provided to demonstrate selecting the appropriate test, interpreting outputs, and statistical fallacies to avoid. The presentation concludes by emphasizing the importance of consulting statisticians and having a planned analysis before data collection.
CAT 1 -MPH 5101 - FOUNDATIONS OF EPIDEMIOLOGY (1).pptxShafici Almis
1. Validity and reliability are important concepts for evaluating diagnostic and screening tests. Validity refers to a test's ability to accurately measure what it is intended to, while reliability is its consistency.
2. Key validity measures include sensitivity (ability to correctly identify those with the disease), specificity (ability to correctly identify those without the disease), and use of a gold standard for comparison.
3. Reliability is assessed through measures like positive and negative predictive values, which indicate the probability that a positive or negative test result accurately reflects disease status. Contingency tables allow calculating these measures from data on true positives, false positives, and other outcomes.
This document discusses statistical concepts and tests relevant to epidemiology and biomedical research. It begins by defining key terms like mean, standard deviation, confidence intervals, and p-values. It then discusses different types of data and variables, measures of central tendency, the central limit theorem, and applications of standard error. The document provides examples of choosing appropriate statistical tests for different study designs, including t-tests, ANOVA, chi-square, correlation, and comparing means between two or more groups. Finally, it presents a case study analyzing water-borne disease deaths before and after a water supply installation using appropriate statistical tests.
Overview of different statistical tests used in epidemiologicalshefali jain
This document provides an overview of different statistical tests used in epidemiological studies and their applications. It discusses topics such as data types (quantitative, categorical), variables, statistics, null and alternative hypotheses, errors in significance testing, and choices between parametric and nonparametric tests. The key information provided includes classifications of variable types, definitions of common statistics, explanations of hypotheses testing and p-values, and guidance on selecting appropriate tests based on the scale and distribution of the data.
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 defines and describes different types of variables that are commonly used in research and statistics. It discusses variables as things that are measured, manipulated, or controlled in studies. The main types discussed are:
- Independent variables that can be manipulated or controlled by researchers to study their effects on dependent variables.
- Dependent variables that are outcomes or effects that change in relation to independent variables.
- Confounding variables that are associated with both the exposure/independent variable and the outcome/dependent variable and can distort the observed relationship between them.
- Control variables that are held constant to assess relationships between other variables.
- Intervening variables that hypothetically explain causal links between other variables but cannot
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
TEST BANK For An Introduction to Brain and Behavior, 7th Edition by Bryan Kol...rightmanforbloodline
TEST BANK For An Introduction to Brain and Behavior, 7th Edition by Bryan Kolb, Ian Q. Whishaw, Verified Chapters 1 - 16, Complete Newest Versio
TEST BANK For An Introduction to Brain and Behavior, 7th Edition by Bryan Kolb, Ian Q. Whishaw, Verified Chapters 1 - 16, Complete Newest Version
TEST BANK For An Introduction to Brain and Behavior, 7th Edition by Bryan Kolb, Ian Q. Whishaw, Verified Chapters 1 - 16, Complete Newest Version
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
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Accuracy lecture 131024
1. ACCURACY
(AND OTHER VALIDATION MEASURES)
Adina L. Feldman, M.Sc.
Karolinska Institutet
Department of Medical Epidemiology and Biostatistics
e-mail: adina.feldman@ki.se
tel. 08 5248 2313
24 October 2013
Adina L. Feldman
1
3. Validity
Accuracy is a type of systematic error (potential bias)
(Random error/precision is related to power, e.g. size of study sample)
Validity is what we call the certainty (accuracy) of a proxy measure/test
Why is knowing the validity of a measure important?
Consider these examples:
What is the validity of breast cancer screening (mammography)?
What is the validity of home pregnancy tests?
What is the validity of self-reported height? …weight?
What is the validity of register-based Parkinson’s disease diagnoses?
24 October 2013
Adina L. Feldman
3
8. Gold Standard
= The best possible available measure agianst which the measure under study is
validated
Discuss: What gold standard was used in these validations?
Breast cancer screening (mammography)?
Home pregnancy tests?
Self-reported height? …weight?
Register-based Parkinson’s disease diagnoses?
24 October 2013
Adina L. Feldman
8
9. Gold Standard
Binary
Continuous
24 October 2013
Binary
Breast cancer screening
(mammography)?
Home pregnancy tests?
Contiuous
Test measure
Discuss: Where do these validations fit in?
Self-reported height? …weight?
Register-based Parkinson’s disease
diagnoses?
Adina L. Feldman
9
10. Gold Standard
24 October 2013
Binary
Contiuous
Test measure
Binary
Reg PDx
Continuous
X
Discuss: Where do these validations fit in?
Breast cancer screening
(mammography)?
Home pregnancy tests?
Self-reported height? …weight?
Preg test
BC screening
Height
Weight
Register-based Parkinson’s disease
diagnoses?
Adina L. Feldman
10
11. Gold Standard
24 October 2013
Binary
Contiuous
Test measure
Binary
Sensitivity,
Specificity,
etc.
ROC-curves
Continuous
Different validation methods are used for
different types of validation studies!
X
These are covered (or at least
mentioned) today
Correlations,
BlandAltman plots
Adina L. Feldman
11
12. Outcome measure
Gold Standard
Positive
+
Positive
+
Negative
-
True Positive
(TP)
False Positive
(FP)
Positive
Predictive Value
(PPV)
=TP/
(TP+FP)
True Negative
(TN)
Negative
Predictive Value
(NPV)
=TN/
(TN+FN)
Negative False Negative
(FN)
-
Sensitivity
Specificity
=TP/
(TP+FN)
24 October 2013
Validity measures for
binary outcomes
(Print and pin to your
office wall!)
=TN/
(TN+FP)
Adina L. Feldman
12
13. Outcome measure
Gold Standard
Positive
+
Positive
+
Negative
-
True Positive
(TP)
False Positive
(FP)
Positive
Predictive Value
(PPV)
=TP/
(TP+FP)
True Negative
(TN)
Negative
Predictive Value
(NPV)
=TN/
(TN+FN)
Negative False Negative
(FN)
-
Sensitivity
=TP/
(TP+FN)
24 October 2013
Specificity
=TN/
(TN+FP)
Adina L. Feldman
13
14. Outcome measure
Gold Standard
Positive
+
Negative
-
These are less commonly
used measures, but
still good to know
24 October 2013
False Positive
(FP)
=FP/
(TP+FP)
True Negative
(TN)
False Negative
Rate (FNR),
cNPV
(=1-NPV)
=FN/
(TN+FN)
True Positive
Rate
FPR (OBS!!)
(=1-Spec.)
Accuracy
=Sens.
Positive
+
False Positive
Rate (FPR),
cPPV
(=1-PPV)
=FP/
(FP+TN)
=TP+TN/
(TP+TN+FP+FN)
True Positive
(TP)
Negative False Negative
(FN)
-
Adina L. Feldman
14
15. Misclassification
FN and FP are misclassifications
Consider cause of misclassification
FN: Why are some cases not detected?
FP: Why are some noncases given erroneous diagnoses?
Differential misclassification:
Non-random distribution of TP and FN (with regards to the exposure)
24 October 2013
Adina L. Feldman
15
16. Misclassification
Discuss: What could be the cause of FP and FN in these validations?
What could be the consequences of misclassification here?
Breast cancer screening (mammography)?
Home pregnancy tests?
Self-reported height? …weight?
Register-based Parkinson’s disease diagnoses?
24 October 2013
Adina L. Feldman
16
17. Fictional Example 1
Cohort study of 10,000 participants (random population-based sample)
Binary proxy measure
e.g. self-reported myocardial infarction (”heart attack”) ever/never
Binary Gold Standard
e.g. myocardial infarction confirmed according to best clinical practice
24 October 2013
Adina L. Feldman
17
20. Fictional Example 2
Cohort study of 10,000 participants (random population-based sample)
Binary proxy measure
e.g. self-reported influenza during one winter season yes/no
Binary Gold Standard
e.g. laboratory-confirmed infection with influenza virus
24 October 2013
Adina L. Feldman
20
23. Discussion points
Many validation study have only available either:
Only Gold Standard positive cases
Only proxy outcome positive cases
What validity measures can be calculated in each instance?
Two-phase screening is a very common approach to diagnosing disease,
e.g. Breast cancer (mammography followed by ultrasound, cytology)
What type of validity is most important in each phase?
24 October 2013
Adina L. Feldman
23
26. Gold Standard
24 October 2013
Binary
Contiuous
Test measure
Binary
Sensitivity,
Specificity,
etc.
ROC-curves
Continuous
Different validation methods are used for
different types of validation studies!
X
These are covered (or at least
mentioned) today
Correlations,
BlandAltman plots
Adina L. Feldman
26
27. Measures with discrimination threshold for binary outcomes
Frequency of cases
GS-
GS+
E.g. biomarker concentration in blood
24 October 2013
Adina L. Feldman
27
28. Measures with discrimination threshold for binary outcomes
Frequency of cases
GS-
GS+
TN
TP
FN FP
E.g. biomarker concentration in blood
24 October 2013
Adina L. Feldman
28
30. Gold Standard:
Reduced insulin sensitivity based
on established clinical index
cutoff
Proxy test:
Appendicular lean body mass
(LBM) index (kg/m2)
The threshold for LBM is varied
and for each step the sensitivity
and 1-specificity for the GS are
calculated and plotted
The goal is to determine the
optimal threshold for LBM in
predicting reduced insulin
sensitivity
AUC = Area Under the Curve (%)
(Bigger = Better)
24 October 2013
Adina L. Feldman
30
33. Gold Standard
24 October 2013
Binary
Contiuous
Test measure
Binary
Sensitivity,
Specificity,
etc.
ROC-curves
Continuous
Different validation methods are used for
different types of validation studies!
X
These are covered (or at least
mentioned) today
Correlations,
BlandAltman plots
Adina L. Feldman
33
41. Afternoon group excercise:
Ad hoc study of the validity of self-reported height
Define
Gold Standard
Method of ascertainment of self-reported height
Collect data
Proxy
Gold Standard
Using Excel
Plot correlation (scatter plot)
Brand-Altman plot
Draw conclusion
24 October 2013
Adina L. Feldman
41
42. Thank You!
(See you this afternoon)
Welcome to my PhD dissertation defence
10 Januari 2014, at 9 am in Andreas Vesalius,
Karolinska Institutet Campus Solna
Dissertation title:
”If I Only Had a Brain
– Epidemiological Studies of Parkinson’s Disease”
24 October 2013
Adina L. Feldman
43