Calculates Prevalence, which is the proportion of cases that are positive for the condition of interest over the total number of cases. Prevalence provides a measure of how widespread a condition is within the population at a given time.
Arguments
- cm
A dx_cm object created by
dx_cm().- detail
Character specifying the level of detail in the output: "simple" for raw estimate, "full" for detailed estimate including 95% confidence intervals.
- ...
Additional arguments to pass to metric_binomial function, such as
citypefor type of confidence interval method.
Value
Depending on the detail parameter, returns a numeric value
representing the calculated metric or a data frame/tibble with
detailed diagnostics including confidence intervals and possibly other
metrics relevant to understanding the metric.
Details
Prevalence is a measure of the burden of a condition or disease in a population. It is an important measure in epidemiology and health service planning as it helps to understand the level of disease in a population at a given time. Unlike other metrics that are based on the classifier's performance, prevalence is a measure of the actual condition being tested.
The formula for Prevalence is: $$Prevalence = \frac{Number of Current Cases (Positives)}{Total Number of Cases}$$
See also
dx_cm() to understand how to create and interact with a
'dx_cm' object.
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
threshold =
0.5, poslabel = 1
)
simple_prevalence <- dx_prevalence(cm, detail = "simple")
detailed_prevalence <- dx_prevalence(cm)
print(simple_prevalence)
#> [1] 0.3754789
print(detailed_prevalence)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Prevalence 37.5% (31.7%,… 0.375 0.317 0.437 98/261 Binomial… ""