The collapsing of these multiple subgroups with different rates of asthma into a single race/ethnic stratum is likely to obscure our ability to capture any potential effect of PM on the studied outcomes.
Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear.
When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect.
This could be a chance result arising from multiple subgroup analysis.
Moreover, multiple subgroup analyses increased the risk of spurious chance findings.
There are multiple subgroups within these levels.
The entire set of subcarriers is partitioned into multiple subgroups; each co-existing system uses only one of the subgroups not used by others.
Large sample permitted analysis of multiple subgroups.
Each of the six subgroups individually; None of the subgroups; Multiple subgroups (for each represented subgroup combination).
This finding was consistent when patient data were analyzed using multiple different methods and for multiple subgroups.
