partOR: PARTIAL ODDS RATIO ESTIMATION

In partialOR: Partial Odds Ratio

Description

Estimates odds ratio adjusted for a vector of covariates

Usage

partialOR(dd,ci=0.95)

Arguments

dd	Data frame with binary 0/1 response variables x,y and covariates z1,...,zm (in that order)
ci	Confidence level (default ci=0.95)

Details

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.

Value

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.

Author(s)

Vaclav Fidler and Nico Nagelkerke

References

Fidler, V. and Nagelkerke, N.J.D. (2012) The Mantel-Haenszel procedure revisited: models and generalizations. Submitted.

Examples

## simulate data from the H-model 
dd <- simData(n=50, m=2, rr=1.5, rseed=123) 
## estimate the OR
partialOR(dd)   

Example output

Loading required package: nnet
# weights:  8 (3 variable)
initial  value 69.314718 
final  value 69.114278 
converged
# weights:  16 (9 variable)
initial  value 69.314718 
iter  10 value 61.070235
final  value 61.060359 
converged

  Partial Odds Ratio estimation 

  Model        Deviance
  Null           138.23 
  Full           122.12 
  Homogen.       125.69 
  LR-test of homogeneity: LR = 3.568 ,df = 2 , P-value = 0.168 

  Unadjusted OR: 
    log(OR) = 0.3212 , SE = 0.568 
    OR = 1.379 ,  95%-CI:  0.453 to 4.197
  MH-type Full model estimate: 
    log(OR) = 0.6423 , SE = 0.6016 
    OR = 1.901 ,  95%-CI:  0.585 to 6.181
  Homogeneity-model estimate: 
    log(OR) = 0.6696 , SE = 0.6535 
    OR = 1.953 ,  95%-CI:  0.543 to  7.032 

partialOR documentation built on May 1, 2019, 10:29 p.m.