phe_rate | R Documentation |
Calculates rates with confidence limits using Byar's (1) or exact (2) CI method.
phe_rate(data, x, n, type = "full", confidence = 0.95, multiplier = 1e+05)
data |
the data.frame containing the data to calculate rates for, pre-grouped if proportions required for group aggregates; unquoted string; no default |
x |
field name from data containing the rate numerators (eg observed number of events); unquoted string; no default |
n |
field name from data containing the rate denominators (eg populations); unquoted string; no default |
type |
defines the data and metadata columns to be included in output; can be "value", "lower", "upper", "standard" (for all data) or "full" (for all data and metadata); quoted string; default = "full" |
confidence |
the required level of confidence expressed as a number between 0.9 and 1 or a number between 90 and 100 or can be a vector of 0.95 and 0.998, for example, to output both 95 percent and 99.8 percent percent CIs; numeric; default 0.95 |
multiplier |
the multiplier used to express the final values (eg 100,000 = rate per 100,000); numeric; default 100,000 |
When type = "full", returns the original data.frame with the following appended: rate, lower confidence limit, upper confidence limit, confidence level, statistic and method
For numerators >= 10 Byar's method (1) is applied using the internal byars_lower and byars_upper functions. For small numerators Byar's method is less accurate and so an exact method (2) based on the Poisson distribution is used.
(1) Breslow NE, Day NE. Statistical methods in cancer research,
volume II: The design and analysis of cohort studies. Lyon: International
Agency for Research on Cancer, World Health Organisation; 1987.
(2) Armitage P, Berry G. Statistical methods in medical research (4th edn).
Oxford: Blackwell; 2002.
Other PHEindicatormethods package functions:
assign_funnel_significance()
,
calculate_ISRate()
,
calculate_ISRatio()
,
calculate_funnel_limits()
,
calculate_funnel_points()
,
phe_dsr()
,
phe_life_expectancy()
,
phe_mean()
,
phe_proportion()
,
phe_quantile()
,
phe_sii()
# ungrouped data frame
df <- data.frame(area = rep(c("Area1","Area2","Area3","Area4"), each=3),
obs = c(NA,82,9,48, 6500,8200,10000,10000,8,7,750,900),
pop = rep(c(100,10000,10000,10000), each=3))
phe_rate(df, obs, pop)
phe_rate(df, obs, pop, type="standard")
phe_rate(df, obs, pop, confidence=99.8, multiplier=100)
# grouped data frame
library(dplyr)
dfg <- df %>% group_by(area)
phe_rate(dfg, obs, pop)
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