Jochen Wilhelm

Our study aims to assess whether there has been an increase in overall happiness among participants who used our interventions—such as dance classes, art classes, meditation, and other resources—available prior to the study's start. Once these tools were accessible, we began conducting quarterly surveys with volunteers to track any improvements in their happiness over time.

Our initial plan was to use repeated measures ANOVA, but we've encountered issues with participant retention across time points. For example, some participants joined only in later surveys, some appeared in the first and then skipped others, while others returned sporadically. This inconsistency led me to consider alternative approaches, particularly linear mixed models (LMM), which handle missing data more flexibly.

For participants who completed at least two surveys across all four time points, we found:

• 21 participants in Time 1, 34 in Time 2, 45 in Time 3, and 47 in Time 4, resulting in 37% missing data.

For those who completed at least two surveys across three time points (Time 2, 3, and 4), we found:

• 32 participants in Time 2, 43 in Time 3, and 44 in Time 4, resulting in 22% missing data.

I also reviewed generalized estimating equations (GEE), which offer population-averaged estimates suitable for analyzing general trends across the sample rather than individual trajectories. GEE appears robust with missing data, but I’m unfamiliar with its assumptions on correlation structures, like "exchangeable" or "autoregressive."

I'm unsure of the best approach, as I initially thought LMM might be a simple alternative to repeated measures ANOVA. If you have any recommendations, I would greatly appreciate your guidance.

Thank you very much.