Estimates odds ratio adjusted for a vector of covariates
Data frame with binary 0/1 response variables x,y and covariates z1,...,zm (in that order)
Confidence level (default ci=0.95)
partialOR() estimates the adjusted odds ratio OR(X,Y | Z1,...,Zm)
between two binary variables X and Y adjusted for a vector
(Z1,...,Zm) of m numerical covariates ("confounders"). It is
based on fitting a multinomial logistic regression model to the data.
In this model the categorical response variable corresponds to
the four possible outcomes of (X,Y), namely (0,0), (0,1), (1,0)
and (1,1). The program fits the null model (without covariates),
the full F-model and the H-modelwith parameters restricted by
the hypothesis of homogeneity of odds ratio's.
The homogeneity hypothesis is tested by comparing the two
models by the Likelihood Ratio test.
The program reports OR estimates under the respective models,
the standard errors of log(OR) and confidence intervals.
Note: to include categorical covariates the user has to transform
them into dummy variables.
The program prints information about the convergence
of the optimizer, the model deviances, the LR-test and the
adjusted odds ratios. It calls the function
fitOR() which, when called separatelly, returns detailed
information on model fitting.
Vaclav Fidler and Nico Nagelkerke
Fidler, V. and Nagelkerke, N.J.D. (2012) The Mantel-Haenszel procedure revisited: models and generalizations. Submitted.
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