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K-Sample comparisons using propensity analysis.

Publication ,  Journal Article
Jung, S-H; Chi, SA; Ahn, HJ
Published in: Biom J
May 2019

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.

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Published In

Biom J

DOI

EISSN

1521-4036

Publication Date

May 2019

Volume

61

Issue

3

Start / End Page

698 / 713

Location

Germany

Related Subject Headings

  • Statistics & Probability
  • Regression Analysis
  • Propensity Score
  • Observational Studies as Topic
  • Kaplan-Meier Estimate
  • Humans
  • Endpoint Determination
  • Decision Trees
  • Biometry
  • 4905 Statistics
 

Citation

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Jung, S.-H., Chi, S. A., & Ahn, H. J. (2019). K-Sample comparisons using propensity analysis. Biom J, 61(3), 698–713. https://doi.org/10.1002/bimj.201800049
Jung, Sin-Ho, Sang Ah Chi, and Hyun Joo Ahn. “K-Sample comparisons using propensity analysis.Biom J 61, no. 3 (May 2019): 698–713. https://doi.org/10.1002/bimj.201800049.
Jung S-H, Chi SA, Ahn HJ. K-Sample comparisons using propensity analysis. Biom J. 2019 May;61(3):698–713.
Jung, Sin-Ho, et al. “K-Sample comparisons using propensity analysis.Biom J, vol. 61, no. 3, May 2019, pp. 698–713. Pubmed, doi:10.1002/bimj.201800049.
Jung S-H, Chi SA, Ahn HJ. K-Sample comparisons using propensity analysis. Biom J. 2019 May;61(3):698–713.
Journal cover image

Published In

Biom J

DOI

EISSN

1521-4036

Publication Date

May 2019

Volume

61

Issue

3

Start / End Page

698 / 713

Location

Germany

Related Subject Headings

  • Statistics & Probability
  • Regression Analysis
  • Propensity Score
  • Observational Studies as Topic
  • Kaplan-Meier Estimate
  • Humans
  • Endpoint Determination
  • Decision Trees
  • Biometry
  • 4905 Statistics