Longitudinal data analysis in medical device clinical studies

Several statistical approaches are discussed for analysis of longitudinal clinical data from a two independent groups, randomized, prospective, multicenter study of patients with benign prostate hyperplasia (BPH) who are treated with either a new transurethral microwave thermotherapy device (TUMT, n = 147) or sham control (n = 73) at baseline and at 1, 3, and 6 months follow up. The primary clinical endpoint, based on the American Urological Association (AUA) symptom score from 7 questions (each scored 0-5) with 35 as the worst total score, is illustrated in this paper. The primary effectiveness null hypothesis is no treatment effect at any follow-up time against the alternative hypothesis of a favorable treatment effect at one or more follow-up times. Based on the prespecified clinical input of TUMT improvement in AUA mean score as a possible early effect, the expected change of slope between baseline to month 1 (baseline slope) and from month 1 to the end of month 6 (longitudinal slope), with knot at one month, were analyzed. The primary effectiveness endpoint was analyzed by a piecewise linear spline regression model with knot at month 1. Statistically significant lower mean AUA score was found in the TUMT group compared to the sham group at month 3 (P = 0.01) and at month 6 (P < 0.0001) follow-up; no significant difference in mean AUA score was found at baseline or at month 1 follow-up, after an overall statistically significant difference in mean AUA score (P = 0.004) was found between the TUMT and sham groups.