Monte Carlo simulation methods are increasingly being used to evaluate the property of statistical estimators in a variety of settings.  The utility of these methods depends upon the existence of an appropriate data-generating process.  Observational studies are increasingly being used to estimate the effects of exposures and interventions on outcomes.  Conventional regression models allow for the estimation of conditional or adjusted estimates of treatment effects.

There is an increasing interest in statistical methods for estimating marginal or average treatment effects.  However, in many settings, conditional treatment effects can differ from marginal treatment effects.  Therefore, existing data-generating processes for conditional treatment effects are of little use in assessing the performance of methods for estimating marginal treatment effects.

In the current study, the authors describe and evaluate the performance of two different data-generation processes for generating data with a specified marginal odds ratio.  The first process is based upon computing Taylor Series expansions of the probabilities of success for treated and untreated subjects.  The expansions are then integrated over the distribution of the random variables to determine the marginal probabilities of success for treated and untreated subjects.  The second process is based upon an iterative process of evaluating marginal odds ratios using Monte Carlo integration.  The second method was found to be computationally simpler and to have superior performance compared to the first method.