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Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Meta-Analysis of Observational Studies with Unmeasured Confounders by McCandless, Lawrence C.
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
- A Refreshing Account of Principal Stratification by Mealli, Fabrizia and Mattei, Alessandra
The Two Sample Problem for Multiple Categorical Variables
Citation Information: The International Journal of Biostatistics. Volume 2, Issue 1, Pages –, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1019, July 2006
- Published Online:
Comparing two large multivariate distributions is potentially complicated at least for the following reasons. First, some variable/level combinations may have a redundant difference in prevalence between groups in the sense that the difference can be completely explained in terms of lower-order combinations. Second, the total number of variable/level combinations to compare between groups is very large, and likely computationally prohibitive. In this paper, for both the paired and independent sample case, an approximate comparison method is proposed, along with a computationally efficient algorithm, that estimates the set of variable/level combinations that have a non-redundant different prevalence between two populations. The probability that the estimate contains one or more false or redundant differences is asymptotically bounded above by any pre-specified level for arbitrary data-generating distributions. The method is shown to perform well for finite samples in a simulation study, and is used to investigate HIV-1 genotype evolution in a recent AIDS clinical trial.