Measurements Calculated Using a 2x2 Table PDF

### Measurements Calculated Using a 2x2 Table and Their Usefulness in Studies The **2x2 table** is a simple method to summarize data for two binary variables, such as test results (positive/negative) and disease status (present/absent). The table structure is as follows: ------------------- ----...

### Measurements Calculated Using a 2x2 Table and Their Usefulness in Studies The **2x2 table** is a simple method to summarize data for two binary variables, such as test results (positive/negative) and disease status (present/absent). The table structure is as follows: ------------------- ------------------------- ------------------------- **Disease Present (+)** **Disease Absent (-)** Test Positive (+) True Positive (TP = a) False Positive (FP = b) Test Negative (-) False Negative (FN = c) True Negative (TN = d) ------------------- ------------------------- ------------------------- ### 1. Sensitivity (True Positive Rate) - - - - ### 2. Specificity (True Negative Rate) - - - - ### 3. Positive Predictive Value (PPV) - - - - ### 4. Negative Predictive Value (NPV) - - - - ### 5. Accuracy - - - - ### 6. Likelihood Ratios (LR) - - - - - - ### 7. Prevalence - - - - ### 8. Diagnostic Odds Ratio (DOR) - - - - ### Usefulness of the 2x2 Table Measures 1. 2. 3. 4. These measures are vital for interpreting diagnostic test performance and making evidence-based clinical and public health decisions. ### Simple Table for 2x2 Table Measures ------------------------------- ------------------ ------------------------------------------------------------------------------------- --------------------------------------------------------------------------- -------------------------------------------------------------------- **Measurement** **Abbreviation** **Interpretation** **Usefulness** **Example Studies** **Sensitivity** Sens Proportion of diseased individuals correctly identified by the test. Important for screening tests to minimize missed cases. Screening for cancer, infectious diseases like tuberculosis. **Specificity** Spec Proportion of non-diseased individuals correctly identified by the test. Important for diagnostic tests to minimize false positives. Confirmatory tests for HIV, diabetes diagnosis. **Positive Predictive Value** PPV Probability that someone with a positive test truly has the disease. Indicates reliability of positive results; depends on disease prevalence. Diagnostic tests in high-prevalence areas (e.g., endemic regions). **Negative Predictive Value** NPV Probability that someone with a negative test truly does not have the disease. Indicates reliability of negative results; depends on disease prevalence. Screening for diseases in low-prevalence areas. **Accuracy** Acc Proportion of all test results correctly classified as true positives or negatives. Provides an overall performance measure of the test. Evaluating overall reliability of a new diagnostic test. **Positive Likelihood Ratio** LR+ Likelihood of a positive result in diseased vs. non-diseased individuals. Useful for calculating post-test probability of disease. Risk analysis in clinical diagnosis (e.g., imaging results). **Negative Likelihood Ratio** LR- Likelihood of a negative result in diseased vs. non-diseased individuals. Useful for ruling out diseases in clinical diagnosis. Evaluating diagnostic reliability in emergency settings. **Prevalence** Prev Proportion of individuals in a population who have the disease. Guides understanding of disease burden in the population. Public health surveys (e.g., diabetes in adults). **Diagnostic Odds Ratio** DOR Odds of a positive result in diseased vs. non-diseased individuals. Combines sensitivity and specificity into a single performance measure. Validation of novel diagnostic too ------------------------------- ------------------ ------------------------------------------------------------------------------------- --------------------------------------------------------------------------- -------------------------------------------------------------------- ### ###