This function estimates prevalence ratios (PRs) and their confidence
intervals using logistic models.
The estimation of standard errors for PRs is obtained through use of delta method.
Confidence intervals of (1-alpha)% for PRs are available for standard logistic regression
and for random-effects logistic models (Santos et al, 2008). The function
prLogisticDelta allows estimation of PRs using two
standardization procedures: conditional or marginal (Wilcosky and Chambless, 1985).
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a symbolic description of the model to be fitted. The details of model specification are given below.
logical argument specifying data clustering. The default is cluster=FALSE. If data is clustered or longitudinal, it should be set to cluster=TRUE.
the standardization procedure. If
scalar or vector specifying confidence level(s) for estimation. The default is conf = 0.95.
a required data frame containing the variables named in
optional additional arguments. Currently none are used in any methods.
A typical form used with
glm() function is included in the formula argument as response
~ terms where response is the (binary) response vector and terms is a series of terms which
specifies a linear predictor for response. The
prLogisticDelta assumes a binomial
family associated to the model. The
glmer() function is used when a vertical bar character "|"
separates an expression for a model matrix and a grouping factor. Currently only binary predictors are allowed. If categorization for predictors
is other than (0,1),
factor() should be considered.
Returns prevalence ratio and its 95% confidence intervals.
Raydonal Ospina, Department of Statistics, Federal University of Pernambuco, Brazil
Leila D. Amorim, Department of Statistics, Federal University of Bahia, Brazil
Localio AR, Margolis DJ, Berlin JA (2007). Relative risks and confidence intervals were easily computed indirectly from multivariate logistic regression. Journal of Clinical Epidemiology, 60, 874-882.
Oliveira NF, Santana VS, Lopes AA (1997). Ratio of proportions and the use of the delta method for confidence interval estimation in logistic regression. Journal of Public Health, 31(1), 90-99.
Santos CAST et al (2008).
Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data.
BMC Medical Research Methodology, 8 (80). Available from
Wilcosky TC, Chambless LE (1985). A comparison of direct adjustment and regression adjustment of epidemiologic measures. Journal of Chronic Diseases, 34, 849-856.
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### For independent observations: # Estimates from logistic regression with conditional standardization - # delta method # Not run: # data("titanic", package = "prLogistic") # attach(titanic) # prLogisticDelta(survived~ sex + pclass + embarked, data = titanic) # End (Not run:) # Estimates from logistic regression with marginal standardization - # delta method prLogisticDelta(survived~ sex + pclass + embarked, data = titanic, pattern="marginal") ### For clustered data # Estimates from random-effects logistic regression with conditional # standardization - delta method # Not run: # data("Thailand", package = "prLogistic") # prLogisticDelta(rgi~ sex + pped + (1|schoolid), # data = Thailand, cluster=TRUE) # End (Not run:) # Estimates from random-effects logistic regression with marginal # Not run: # standardization - delta method # prLogisticDelta(rgi ~ sex + pped + (1|schoolid), data = Thailand, # pattern="marginal", cluster=TRUE) # End (Not run:)
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