OBJECTIVES

: FDA guidance outline methods for measuring meaningful change, and the results are increasingly used to inform regulatory decisions. Current best practice is to compute meaningful within-patient change (MWPC) using an anchor-based approach. The relationship between change scores and anchor groups is visualized via empirical cumulative distribution functions (eCDFs). However, when sample sizes within anchor groups are small, it may be difficult to compute the distribution function. In contrast, a scatterplot approach does not require any computation. The objective was to evaluate under what study conditions (e.g., sample sizes) would it be advantageous to interpret meaningful change with a scatterplot approach.

METHODS

: Data simulated to emulate real PRO data from a trial, including attrition due to death, were used to highlight shortcomings in current practice and propose a robust alternative. A simulation study permitted a variety of sample sizes and magnitudes of meaningful change to be evaluated. The eCDF was implemented in accordance with current guidance and compared to scatterplots. The scatterplot approach was operationalized as follows: the subject scores were plotted with the baseline scores on the x-axis and the follow-up scores on the y-axis. The scatterplot was stratified by color to represent anchor group-specific scores. Subjects who did not respond at follow-up due to progression or death were plotted in a separate color, using the baseline response and an assigned follow-up score reflecting maximum severity. A 45-degree line represented No Change. The vertical distance between the subject score and the 45-degree line represented the observed change.

RESULTS

: When sample size was below n=10 subjects per anchor group, the eCDF yielded uninterpretable visualizations. At sample sizes n=1-20 per anchor group, the scatterplot approach consistently yielded interpretable visualizations of meaningful change.

CONCLUSIONS

: The scatterplot approach avoids methodological complications and thus ensures transparency in the evaluation of meaningful change.