English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Althoughtest and  F  test are commonly used for multiple group comparisons in experimental data, these methods can not be directly used to examine group differences in observational studies because of the confounding factors. Since the seminal work by Rosenbaum and Rubin ( 1983 ), the inverse probability weighting (IPW) method has been applied to compare pairs among multiple treatment groups without controlling the family-wise error rate (FWER). In this article, we propose to examine whether there is an overall significant group difference using two weightedtests for categorical/continuous outcomes. Our extensive simulation studies show that the proposed methods can control the FWER, while the traditional tests have an inflated type I error rate. We apply the proposed weightedtests to investigate whether fruit/vegetable intakes are associated with heart attack using the 2015 Kentucky BRFSS dataset and to examine the effect of physical/recreational exercise on weight gain using the NHEFS dataset.

Weighted χ 2 tests for multiple group comparisons in observational studies