Missing data cause challenging issues, particularly in phase III registration trials, as highlighted by the European Medicines Agency (EMA) and the US National Research Council. We explore, as a case study, how the issues from missing data were tackled in a double-blind phase III trial in subjects with autosomal dominant polycystic kidney disease. A total of 1445 subjects were randomized in a 2:1 ratio to receive active treatment (tolvaptan), or placebo. The primary outcome, the rate of change in total kidney volume, favored tolvaptan (P < .0001). The key secondary efficacy endpoints of clinical progression of disease and rate of decline in kidney function also favored tolvaptan. However, as highlighted by Food and Drug Administration and EMA, the interpretation of results was hampered by a high number of unevenly distributed dropouts, particularly early dropouts. In this paper, we outline the analyses undertaken to address the issue of missing data thoroughly. "Tipping point analyses" were performed to explore how extreme and detrimental outcomes among subjects with missing data must be to overturn the positive treatment effect attained in those subjects who had complete data. Nonparametric rank-based analyses were also performed accounting for missing data. In conclusion, straightforward and transparent analyses directly taking into account missing data convincingly support the robustness of the preplanned analyses on the primary and secondary endpoints. Tolvaptan was confirmed to be effective in slowing total kidney volume growth, which is considered an efficacy endpoint by EMA, and in lessening the decline in renal function in patients with autosomal dominant polycystic kidney disease.
Keywords: missing data; missing not at random; multiple imputations; tipping point analysis; tolvaptan.
Copyright © 2017 John Wiley & Sons, Ltd.