The Performance of Two Data-Generation Processes for Data with Specified Marginal Treatment Odds Ratios

• Published in: Communications in statistics. Simulation and computation Vol. 37; no. 6; pp. 1039 - 1051
• Main Authors: Austin, Peter C., Stafford, James
• Format: Journal Article
• Language: English
• Published: Colchester Taylor & Francis Group 19.05.2008; Taylor & Francis
• Subjects: Average treatment effect; Data-generating process; Distribution theory; Exact sciences and technology; Global analysis, analysis on manifolds; Linear inference, regression; Marginal odds ratios; Marginal treatment effect; Mathematics; Monte Carlo simulations; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Sciences and techniques of general use; Simulations; Statistics; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds; Conditional distribution; Statistical distribution; Data-generating process; Iterative process; Iterative method; Probability distribution; Average treatment effect; Marginal treatment effect; Computing; Stochastic method; Marginal distribution; Series expansion; Distribution function; Marginal odds ratios; Approximation theory; Simulations. 65C05; Mathematical expansion; Expansion; Random variable; Monte Carlo method; Numerical integration; Treatment efficiency; Average; Probability; Taylor series; Statistical estimation; Random distribution; Mean estimation; Statistical method; Statistical regression; Numerical analysis; Monte Carlo simulations; Regression model; Treatment effect; Numerical simulation; Application; 05A15
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610910801942430

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, we 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.