Abstract
Using the EffectLiteR framework, researchers can test classical null hypotheses about effects of interest via Wald and F-tests, while taking into account the stochastic nature of group sizes. This paper aims at extending EffectLiteR to test informative hypotheses, assuming for example that the average effect of a new treatment is greater than the average effect of an old treatment, which in turn is greater than zero. We present a simulated data example to show two methodological novelties. First, we illustrate how to use the Fbar- and generalized linear Wald test to assess informative hypotheses. While the classical test did not reach significance, the informative test correctly rejected the null hypothesis, indicating the need to take into account the order of the treatment groups. Second, we demonstrate how to account for stochastic group sizes in informative hypotheses using the generalized non-linear Wald statistic. The paper concludes with a short data example.
Highlights
IntroductionThe background of the EffectLiteR framework is explained
Into the EffectLiteR FrameworkIn the following, the background of the EffectLiteR framework is explained
We showed how to account for stochastic group sizes when testing informative hypothe ses within the EffectLiteR framework
Summary
The background of the EffectLiteR framework is explained. This will be illustrated by means of a non-randomized experiment, whose data of size N = 1000 are generated along the lines of Mayer et al (2016). Three treatment groups are considered, namely a group receiving innovative therapy (X = 2), a group receiving conventional therapy (X = 1) and a wait-list control group (X = 0). The dichotomous variable gender with values male (K = 0) and female (K = 1) and the continuous covariate mental health pre-test (Z), are considered. The outcome variable is the post-test of mental health (Y ).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have