K-Sample comparisons using propensity analysis.

TitleK-Sample comparisons using propensity analysis.
Publication TypeJournal Article
Year of Publication2019
AuthorsJung, Sin-Ho, Sang Ah Chi, and Hyun Joo Ahn
JournalBiom J
Date Published2019 05
KeywordsBiometry, Decision Trees, Endpoint Determination, Humans, Kaplan-Meier Estimate, Observational Studies as Topic, Propensity Score, Regression Analysis

In this paper, we investigate K-group comparisons on survival endpoints for observational studies. In clinical databases for observational studies, treatment for patients are chosen with probabilities varying depending on their baseline characteristics. This often results in noncomparable treatment groups because of imbalance in baseline characteristics of patients among treatment groups. In order to overcome this issue, we conduct propensity analysis and match the subjects with similar propensity scores across treatment groups or compare weighted group means (or weighted survival curves for censored outcome variables) using the inverse probability weighting (IPW). To this end, multinomial logistic regression has been a popular propensity analysis method to estimate the weights. We propose to use decision tree method as an alternative propensity analysis due to its simplicity and robustness. We also propose IPW rank statistics, called Dunnett-type test and ANOVA-type test, to compare 3 or more treatment groups on survival endpoints. Using simulations, we evaluate the finite sample performance of the weighted rank statistics combined with these propensity analysis methods. We demonstrate these methods with a real data example. The IPW method also allows us for unbiased estimation of population parameters of each treatment group. In this paper, we limit our discussions to survival outcomes, but all the methods can be easily modified for any type of outcomes, such as binary or continuous variables.

Alternate JournalBiom J
Original PublicationK-Sample comparisons using propensity analysis.
PubMed ID30614546
PubMed Central IDPMC6461520
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States