Calculates odds ratio by median-unbiased estimation (mid-p), conditional maximum likelihood estimation (Fisher), unconditional maximum likelihood estimation (Wald), and small sample adjustment (small). Confidence intervals are calculated using exact methods (mid-p and Fisher), normal approximation (Wald), and normal approximation with small sample adjustment (small).
oddsratio(x, y = NULL,
method = c("midp", "fisher", "wald", "small"),
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE)
oddsratio.midp(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE,
interval = c(0, 1000))
oddsratio.fisher(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE)
oddsratio.wald(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE)
oddsratio.small(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE)input data can be one of the following: r x 2 table, vector
of numbers from a contigency table (will be transformed into r x 2
table in row-wise order), or single factor or character vector that
will be combined with y into a table.
single factor or character vector that will be combined with
x into a table (default is NULL)
method for calculating odds ratio and confidence interval
confidence level (default is 0.95)
reverse order of "rows", "colums", "both", or "neither" (default)
set to TRUE for Yate's continuity correction (default is FALSE)
set to TRUE to return more detailed results (default is FALSE)
interval for the uniroot that finds the
odds ratio median-unbiased estimate and mid-p exact confidence
interval for oddsratio.midp
table that was used in analysis (verbose = TRUE)
same table as x but with marginal totals
proportions exposed (verbose = TRUE)
proportions experienced outcome (verbose = TRUE)
risk ratio and confidence interval
confidence level used (verbose = TRUE)
p value for test of independence
number of replicates used in Monte Carlo simulation p value (verbose = TRUE)
logical specifying if continuity correction was used
Calculates odds ratio by median-unbiased estimation (mid-p), conditional maximum likelihood estimation (Fisher), unconditional maximum likelihood estimation (Wald), and small sample adjustment (small). Confidence intervals are calculated using exact methods (mid-p and Fisher), normal approximation (Wald), and normal approximation with small sample adjustment (small).
This function expects the following table struture:
disease=0 disease=1
exposed=0 (ref) n00 n01
exposed=1 n10 n11
exposed=2 n20 n21
exposed=3 n30 n31
The reason for this is because each level of exposure is compared to the reference level.
If you are providing a 2x2 table the following table is preferred:
disease=0 disease=1
exposed=0 (ref) n00 n01
exposed=1 n10 n11
however, for odds ratios from 2x2 tables, the following table is equivalent:
disease=1 disease=0
exposed=1 n11 n10
exposed=0 n01 n00
If the table you want to provide to this function is not in the
preferred form, just use the rev option to "reverse" the rows,
columns, or both. If you are providing categorical variables (factors
or character vectors), the first level of the "exposure" variable is
treated as the reference. However, you can set the reference of a
factor using the relevel function.
Likewise, each row of the rx2 table is compared to the exposure reference level and test of independence two-sided p values are calculated using mid-p exact, Fisher's Exact, Monte Carlo simulation, and the chi-square test.
Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, Lippincott-Raven Publishers
Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press
Nicolas P. Jewell (2004), Statistics for Epidemiology, 1st Edition, 2004, Chapman & Hall, pp. 73-81
# NOT RUN {
##Case-control study assessing whether exposure to tap water
##is associated with cryptosporidiosis among AIDS patients
tapw <- c("Lowest", "Intermediate", "Highest")
outc <- c("Case", "Control")
dat <- matrix(c(2, 29, 35, 64, 12, 6),3,2,byrow=TRUE)
dimnames(dat) <- list("Tap water exposure" = tapw, "Outcome" = outc)
oddsratio(dat, rev="c")
oddsratio.midp(dat, rev="c")
oddsratio.fisher(dat, rev="c")
oddsratio.wald(dat, rev="c")
oddsratio.small(dat, rev="c")
# }
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