Quality of life research often collects daily information and averages this over a week, producing a summary score. When data are missing, arbitrary rules (such as requiring at least 4/7 observations) are used to determine whether a patient’s summary score is created or set to missing. This simulation work aimed to assess the impact of missing data on the estimates produced by summary scores, the psychometric properties of the resulting summary score estimates and the impact on interpretation thresholds.
Complete longitudinal data were simulated for 1000 samples of 400 patients with different day-to-day variability. Data were deleted from these samples in line with missingness mechanisms to create scenarios with up to six days of missing data. Summary scores were created for complete and missing data scenarios. Summary score estimates, psychometric properties and meaningful change estimates were assessed for missing data scenarios compared to complete data.
In most cases, the 4/7 day rule was supported, but this depended on daily variability. Fewer days of data were sometimes acceptable, but this was also dependent on the proportion of patients with missing data. Tables and figures allow researchers to assess the potential impact of missing data in their own studies.
This work suggests that the missing data rule used to create summary scores impacts on the estimate, measurement properties and interpretation thresholds. Although a general rule of 4/7 days is supported, the way the summary score is derived does not have a uniform impact across psychometric analyses. Recommendations are to use the 4/7 rule, but plan for sensitivity analyses with other missing data rules.