OBJECTIVES: Clinical trials often gather diaries of participants' health-related quality of life (HRQoL). Often, data from these diaries use only a few time points to assess efficacy. This study presents a novel descriptive framework to assess longitudinal diary data. This work is critical for understanding disease dynamics and informing endpoint development.

METHODS: Data were simulated for 20 patients, with 48 days, randomized 1:1 to active or placebo. Ordinal response data were generated for a 7-category item using a longitudinal mixed effects model, with a positive effect for the treatment group. Differences in response between the groups were generated using novel intra-patient heat maps. Case-wise longitudinal descriptive summaries were calculated using dominance, Shannon’s entropy index, and the log-skew.

RESULTS: Novel intra-participant heat maps displayed differences in the responses for active and placebo. A dominance statistic established the proportion of observations in the modal rating category. Shannon’s entropy index provides insight about the evenness of the ratings split across categories. The log-skew statistic will show information for rare category endorsement. Contrasts were drawn between select active and placebo participants to visualize differences in these descriptive statistics and to lend interpretative value to these summaries.

CONCLUSIONS: Results demonstrate that both the data visualizations and descriptive methods capture nuanced differences in the patterns of responses for placebo and treatment.