The code is for calculating bias corrected estimates of the parameter based on the modified score approach (Sun et al., 2008). The following abbreviations have used in the subroutines. Input variables: y: response variable which takes only 0 or 1 (depending on case or control) covariates: values of the covariate (it can take maximum two covariates) ncase: number of cases in the dataset M: the number of controls in each stratum eps: tolerance beta0: the initial value of the parameter Output variables: estimae: parameter estimate std: standard error of the parameter Reference: Sun, J., Sinha, S., Wang, S. J., and Maity, T. (2008). Bias corrected inference for the conditional logistic regression. Submitted. Example: # Suppose we have one 1:3 matched case-control data with two covariates, # and given as follows. The number of strata is n=29. >data y cov1 cov2 1 1.3 0 0 1.12 0 0 1.35 0 0 0.95 0 1 1.3 1 0 1.03 0 0 1.22 0 0 1.13 0 1 1.2 0 0 1.13 0 0 1.19 0 0 1.19 0 1 1.48 0 0 1 0 0 0.9 0 0 2.29 0 1 1.1 1 0 1.07 0 0 1 0 0 0.9 0 1 0.91 1 0 1.38 0 0 1.89 0 0 1.47 0 1 1.02 0 0 1.5 0 0 2.35 0 0 1.84 0 1 1.12 0 0 1.82 0 0 0.95 0 0 1.32 0 1 1.5 0 0 1.2 0 0 1.05 0 0 1.41 1 1 1.2 0 0 1.03 0 0 1.27 0 0 1.7 0 1 1.21 1 0 1.69 1 0 1.21 0 0 1.2 0 1 2 0 0 1.08 0 0 1.24 0 0 1.85 0 1 1 1 0 1.6 0 0 1.1 0 0 1.15 0 1 1.3 1 0 0.85 0 0 1.3 0 0 1.25 0 1 1.3 0 0 1.2 0 0 1.12 1 0 1.69 0 1 0.97 0 0 1.3 0 0 1.19 0 0 1.23 0 1 1.1 1 0 1.28 0 0 1.9 0 0 1.1 0 1 1.32 0 0 1.15 0 0 1.15 0 0 1.1 0 1 1.38 0 0 0.9 1 0 1.33 0 0 1.16 0 1 0.85 0 0 1.18 0 0 1.25 0 0 1.2 0 1 0.92 0 0 1.2 0 0 1.4 0 0 2.41 0 1 1.05 1 0 1.55 0 0 0.95 1 0 1.3 0 1 1.9 0 0 1.13 0 0 1.68 0 0 1.6 0 1 1.2 1 0 1.4 0 0 2.5 0 0 1.34 0 1 0.95 0 0 1.2 0 0 1.2 0 0 1.3 0 1 1.3 0 0 1.5 0 0 1.35 0 0 1.3 0 1 1.42 1 0 1.07 1 0 1.53 0 0 1.37 0 1 1.02 1 0 1 0 0 1.5 0 0 1.2 0 1 1.05 0 0 1.21 0 0 1.32 0 0 1.34 1 >y <- data[,1] >covariate <- data[,c(2,3)] >out <- MDS(y, covariate, ncase=29, M=3, eps=0.0001, beta0=c(0.2,0.4)) > out betaP sdP 1 -0.6653633 0.7779791 2 1.7590951 0.5502278