"multiple subgroup analysis" is a correct and usable phrase in written English.
It is typically used in scientific or academic writing to refer to a statistical method where data is analyzed for multiple subgroups or subpopulations within a larger sample size. Example: The study conducted multiple subgroup analyses to determine the effect of age, gender, and income level on participants' response to the treatment.
A large literature, much of it devoted to the topic of multiple comparisons, subgroup analysis, pre-specification of hypotheses, and related topics, are aimed at reducing type I errors [ 4].
The studies investigating different cancers [38], multiple ethnicity [26], or multi-center collaboration [30] were separated into multiple studies in subgroup analysis.
In addition, multiple comparisons and subgroup analysis could have led to chance findings.
An obvious limitation is one inherent in any post hoc subgroup analysis: multiple comparisons can lead to misleading conclusions.
Because of the multiple comparisons in the subgroup analysis, we used α≤0.01 to establish statistical significance and we report 99% CIs.
Finally, a multivariable risk-stratified analysis reduces the chance of false positive results because it represents a single statistical comparison (thereby avoiding the multiple comparisons of conventional subgroup analysis).
Since ACS, interval between repeated tests and age were found to be associated with different SEN and/or SPE in the subgroup analysis, multiple meta-regressions were performed to further determine the effect of these factors on the DOR.
Bonferroni adjustment of α-error rate was considered in case of multiple comparisons, in particular performing subgroup analysis.
The evidence presented raises the possibility of a mono-allelic effect for Y179C, but the effect is low (OR 1.34; 95% CI: 1.00 1.80) and is sensitive to variations in population allele frequency because of the rarity of the variant (allele frequency 0.002), as well as potential issues of subgroup analysis and multiple testing (indeed the overall significance is lost after Bonferoni correction).
The second section briefly describes the main statistical tests used in clinical trials, as well as certain situations that may increase the risk of false positive findings (type 1 error), such as multiple, subgroup, intermediate and non-inferiority analysis.
