Daniel Li and L.J. Wei Harvard University Utilizing multiple outcomes from comparative studies for assessing totality of evidence on treatment efficacy/safety Daniel Li and L.J. Wei Harvard University

Example: Clinical studies for treating DMD Duchenne muscular dystrophy (DMD) is a genetic disorder characterized by progressive muscle degeneration and weakness. Two independent comparative studies were conducted. Primary endpoint is the change in 6 minute walking distance (6MWD) from baseline to week 48. Secondary endpoints are changes in “10-meter walk/run,” “4-stair climb” and “4-stair descend” times.

How to analyze data from multiple outcomes conventionally? For each endpoint, use a single statistic such as the average difference between two arms to estimate the treatment effect. Obtain p-value and confidence interval. Apply a multi-stage testing procedure to claim statistical significance for the secondary endpoint.

How to quantify totality of treatment effect over multiple endpoints from independent trials? “Statistically significant” may not be “clinically significant.” However, it is the first hurdle we need to pass. How to use the estimated differences to quantify/enhance statistical significance for totality of evidence? Wei-Lachin (1984, JASA); Wei-Johnson (1985, Biometrika)

How to choose thEse endpoints? Measure disease burden or progression in different domains. If the estimated population averages are highly correlated, not much is gained via this approach. Need to prespecify these endpoints.

How do we connect thEse estimated population differences at THE patient level?

Such utilization of patient’s multiple outcomes to evaluate treatment does not match clinical practice Following the patient over time. Having periodic clinical/lab exams/tests. Collecting patient’s outcomes, say, muscle function measures. Assessing the disease burden/progression over time via totality of multiple outcomes. Deciding on a treatment plan.

Graphical display for patient level data Treatment Placebo No. 1 2 3 4 5

Graphical display for patient level data Treatment Placebo 1 2 3 4 5

How to define patient’s response via multiple outcomes? All red dots: complete response All black dots: worsening Mixed dots: partial response or stable disease

Ordinal categorical observations Statistical methods readily available for analyzing such data. However, how to estimate the treatment effect with a clinical interpretation? May need a large study size for discrete data? How to create a totality measure beyond ordinal categorical type with multiple outcomes (patient level)?