A weighted Jackknife method for clustered data

ABSTRACT

We propose a weighted delete-one-cluster Jackknife based framework for few clusters with severe cluster-level heterogeneity. The proposed method estimates the mean for a condition by a weighted sum of estimates from each of the Jackknife procedures. Influence from a heterogeneous cluster can be weighted appropriately, and the conditional mean can be estimated with higher precision. An algorithm for estimating the variance of the proposed estimator is also provided, followed by the cluster permutation test for the condition effect assessment. Our simulation studies demonstrate that the proposed framework has good operating characteristics.

KEYWORDS:

• Clustered data
• Few clusters
• Heterogeneity
• Weighted delete-one-cluster Jackknife

Acknowledgments

The authors are thankful to Tawny Boyce, MS, MPH, University of New Mexico Comprehensive Cancer Center, for reading the manuscript and providing the suggestions to improve the writing quality.

Additional information

Funding

This research was supported in part by the UNM Comprehensive Cancer Center, a recipient of NCI Cancer Support Grant 2 P30 CA118100-11 (PI: Cheryl L. Willman, MD).