Equivalence Testing
Subsequently, we further test our hypothesis by comparing the
frequencies of the variants with the highest \(\mathbb{P}(j=4)\)
against the frequencies of schizophrenia and bipolar disorder across
Canada. We manually extract demographic distributions for each of the
top 10% of variants for SCZ and BPD with the highest probability of
being placed in \(j\) = 4 from the Genome Aggregation Database
(gnomAD) browser v2.1.1 and v3.1.2 \citep{r2014}. Any variant
that was not recorded in either version of gnomAD was excluded from the
study, resulting in a variant population size of n = 66 and n = 37 for
SCZ and BPD, respectively. Additionally, the age and sex frequency
distributions of SCZ and BPD crude prevalence in Canada from the
2019–2020 fiscal year were recorded from the Canadian Chronic Disease
Surveillance System (CCDSS; \citealp{canada2019}). Any
individual outside of >30, 50–55, or <80 age
brackets in addition to any individual who could not be classified as
male or female at birth were excluded from analysis.
A two one–sided test (TOST) was used to find a possible correlation
between the predicted frequency sets (based on AKAP11 variants)
and observed frequency sets (based on CCDSS). Lower and upper
equivalence bounds are \(\mathrm{\pm}\)1.1 for SCZ and\(\mathrm{\pm}\)2.3 for BPD, and a failure to show that each data set
violates these bounds using t tests is sufficient evidence for
equivalence \citep{Lakens2017}. Similarly, we perform the Shapiro–Wilk and
F–Test to test for equal variance and normal distribution, ensuring
appropriate use of the t test.