OBJECTIVES The nested case-control (NCC) design offers a simple method for avoiding unreasonable assumptions in the evaluation of time-dependent treatment effect. Its results are easy to interpret. Its strength rests largely on the appropriateness of the controls which are matched to the cases- suggesting a matching strategy that ensures maximum number of discordant case-control pairs may be more efficient since effect estimation is based entirely on the off-diagonal data of the resulting 2x2 tables in the conditional logistic regression. In theory, the more off-diagonal pairs that are generated by the random sampling scheme, the more improvement we can expect on efficiency. The objective of this study is to assess the efficiency of counter-matching on treatment compared with the classical matching approach in the NCC design based on results from the cohort design using simulated data.  METHODS In each simulation of 1000 patients at 100 replications per run, we assumed an underlying event hazard of Weibull distribution using inputted values for the scale and shape parameters, treatment, age and sex as the factors. Each run involved distinct treatment prevalence of between 10%-50% with event rate varying from common to very rare. We compared the proportion of matched pairs (at 1 control per case) used in the analyses of the resulting data between the classical and counter matching strategies. RESULTS The counter-matched strategy was more efficient than the classical approach. The proportion of matched pairs used was on average over ten times more and it also gave mean effect estimates which were more consistent with the full cohort values, particularly for low treatment exposure and rare events. CONCLUSIONS Our results suggest counter matching is more efficient and more accurate estimates than classical matching in nested case-control design. These benefits may be particularly important for studies involving rare events or low treatment exposure.