Modeling Missingness for Time-to-Event Data: A Case Study in Osteoporosis

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Abstract
Clinical trials of long duration are often hampered by high dropout rates, making statistical inference and interpretation of results difficult. Statistical inference should be based on models selected according to whether missingness is independent of response [missing completely at random (MCAR)], or depends on response either through observed responses only [missing at random (MAR)] or through unobserved responses [nonignorable missing (NIM)]. If the dropout rate is high and little is known about the dropout mechanism, plausible nonignorable missing scenarios should be investigated as a sensitivity tool, offering the data analyst an understanding of the robustness of conclusions. Modeling missingness is illustrated by an analysis of an interval censored time-to-event outcome from a 5-year clinical trial on fracture response in osteoporosis in which the overall dropout rate was substantial. In this article, we provide an overview of a reanalysis accounting for possible nonignorable missingness,...