Presentation on theme: "Validity and Reliability of Analytical Tests. Analytical Tests include both: Screening Tests Diagnostic Tests."— Presentation transcript:
Validity and Reliability of Analytical Tests
Analytical Tests include both: Screening Tests Diagnostic Tests
Two Important Objectives To distinguish between people in the population who have the diseases and those who do not To determine how good the test is in separating populations of people with and without the disease in question?
Epidemiological Surveillance vs. Screening
Epidemiological Surveillance What is it? Why do it?
Epidemiological Surveillance Definition - ongoing & systematic collection, analysis & interpretation of data related to health, disease & conditions Two types Passive Surveillance – uses available data or reporting from health care provider or regional health officer Active Surveillance – periodic field visits to health care facilities to identify new cases The present approach is the survey
Epidemiological Surveillance Why do it? Can help discover and control the transmission of infectious diseases Prevention and control programs can be planned and implemented
Screening Definition - use of quick and simple testing procedures to identify and separate persons: who have a disease from those that do not OR who are apparently (appear to be) well, but who may be at risk of a disease, from those who probably dont have the disease.
Terms Related to Screening Tests Validity - relates to accuracy (correctness) Reliability - repeatability Yield - the # of tests that can be done in a time period
Terms Related to Screening Tests (contd) Sensitivity - ability of a test to identify those who have disease Specificity - ability of a test to exclude those who dont have disease
Terms Related to Screening Tests (contd) Tests with dichotomous results – tests that give either positive or negative results Tests of continuous variables – tests that do not yield obvious positive or negative results, but require a cutoff level to be established as criteria for distinguishing between positive and negative groups
An important public health consideration, particularly in screening free-living populations, is: How good is the test at identifying people with the disease and without the disease? In other words: If we screen a population, what proportion of people who have the disease will be correctly identified?
POPULATION Test ResultsWith Disease Without Disease Positive True Positive (TP) False Positive (FP) Negative False Negative (FN) True Negative (TN)
Sensitivity == X 100 True positives True positives + false negatives True positives All persons with the disease = TP TP + FN
Specificity == X 100 True negatives True negatives+ false positives True negatives All persons without the disease = TN TN + FP
Percent false negatives = % of people with the disease who were not detected by the test FN FN + TP X 100
Percent false positives = % of people without the disease who were incorrectly labeled by the test as having the disease FP FP + TN X 100
In the clinical setting, a more important question is: If the test results are positive (or negative) in a given patient, what is the probability that this patient has (or does not have) the disease? In other words: What proportion of patients who test positive (or negative) actually have (or do not have) the disease in question?
Predictive Value Pos. PV = X 100 = % True Positives TP + FP Neg. PV = X 100 = % True Negatives TN + FN
Biologic Variation of Human Populations & Diagnostic Issues
Distribution of Tuberculin Reactions Bimodal Distribution Easy to distinguish between exposed group and those not exposed.
Distribution of Systolic Blood Pressure Unimodal Distribution With continuous variables, a cutoff level must be established to separate the hypertensive group. Could choose based on statistics, but better to base on biologic considerations.
Effects of Choosing Different Cutoff Levels for Diabetes Diagnosis in Population with 50% Prevalence Real World Pseudo- Real World
The major issue with deciding to set a cutoff high or low is the problem of false positives and false negatives.
Possible Groups with Dichotomous Test True Disease Status is Known, as with dichotomous tests.
Grouping All Positives and All Negatives True Disease Status is Unknown, as with continuous variables. Artificial Cutoff
Use of Multiple Screening Tests Sequential (Two-stage) Testing Simultaneous Testing
Hypothetical Two-Stage Screening Only Pos. Test 1 are given Test 2
Hypothetical Two-Stage Screening (cont.) TEST 2 (Glucose Tolerance Test) Sensitivity = 90% Specificity = 90% DIABETES +- TEST RESULTS Net Sensitivity = 315/500 = 63% Net Specificity = = 98% 9500
Prevalence & Predictive Value Positive As prevalence increases, positive predictive value increases.
Prevalence & Predictive Value Note: Test has 95% sensitivity and 95% specificity
Specificity & Predictive Value As specificity increases, positive predictive value increases. As sensitivity increases, positive predictive value also increases, but to a much lesser extent.
Specificity & Predictive Value As specificity increases, positive predictive value increases.
Results reliable but NOT valid Results reliable and valid Reliability (Repeatability) of Tests