Current status data result from a simple form of interval censoring in which the observation consists only of an examination time and knowledge of whether the failure time has occurred before the exam. Semiparametric regression methods which examine the relationship between the failure time and covariates have been studied extensively for current status data. In this thesis, we consider the likelihood ratio test for testing covariate effect under the proportional odds model with current status data and propose an easily implemented algorithm for computing the statistics. The algorithm proposed is based on a set of self-consistency equations and its convergence is proved by contraction principle. The adequacy of the Chi-squared approximation for likelihood ratio statistics and the availability of the algorithm are demonstrated in simulation studies and in the analyses of two real data.