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

• Published in: Journal of biopharmaceutical statistics Vol. 14; no. 4; pp. 1005 - 1019
• Main Authors: Neuenschwander, Beat, Branson, Michael
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis Group 31-12-2004
• Subjects: Aged; Algorithms; Bayesian; Continuation ratio model; Data Interpretation, Statistical; Female; Fractures, Bone - epidemiology; Fractures, Bone - etiology; Humans; Interval censored; Joint Diseases - etiology; Likelihood Functions; Longitudinal Studies; Markov Chains; Middle Aged; Models, Statistical; Monte Carlo Method; Nonignorable missing; Osteoporosis - complications; Osteoporosis - drug therapy; Osteoporosis - epidemiology; Patient Dropouts; Postmenopause; Proportional Hazards Models; Regression Analysis; Reproducibility of Results; Software; Terminology as Topic; Time Factors
• ISSN: 1054-3406; 1520-5711
• DOI: 10.1081/BIP-200035478

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, emphasize the importance of modeling the dropout and response mechanisms jointly, and highlight critical points arising in missing data problems.