What would you like to understand?

To calculate the probability of having the disease given a positive test result, you must count the number of true positives and divide it by the total number of positive results (true positives plus false positives). In the model, this involves identifying the 19 true positives out of the 118 total positive tests, yielding approximately $16.1\%$.

Conditions: Prevalence is $1\%$ in a population of 2,000.; Sensitivity is $95\%$.; Specificity is $95\%$.

To calculate the probability of having the disease given a positive test result, you must count the number of true positives and divide it by the total number of positive results (true positives plus false positives). In the model, this involves identifying the 19 true positives out of the 118 total positive tests, yielding approximately $16.1\%$.

Conditions: Prevalence is $1\%$ in a population of 2,000.; Sensitivity is $95\%$.; Specificity is $95\%$.

The positive predictive value, or the probability of having the disease given a positive test result ($P(D \mid +)$), is calculated by dividing the probability of a true positive by the total probability of a positive result. The total probability of a positive result includes both true positives and false positives.

Conditions: Prevalence $P(D) = 0.12$.; Sensitivity $P(+ \mid D) = 0.95$.; False positive rate $P(+ \mid D^c) = 0.06$.; The events are mutually exclusive and exhaustive for the disease status.

The positive predictive value, or the probability of having the disease given a positive test result ($P(D \mid +)$), is calculated by dividing the probability of a true positive by the total probability of a positive result. The total probability of a positive result includes both true positives and false positives.

Conditions: Prevalence $P(D) = 0.12$.; Sensitivity $P(+ \mid D) = 0.95$.; False positive rate $P(+ \mid D^c) = 0.06$.; The events are mutually exclusive and exhaustive for the disease status.