Withdrawn: 19 Missing data strategy in bioavailability studies at Danone Research & Innovation

Thomas M. Menino Convention & Exhibition Center 

Missing data are common in bioavailability studies with dense sampling. In the current approach, at Danone Research & Innovation, if a value is missing near the expected peak, the visit is marked unevaluable and a replacement participant recruited; otherwise simple interpolation may be used. This can reduce power, introduce bias in Cmax (peak concentration), Tmax (time to peak), and iAUC (incremental Area Under the Curve), and increase cost and burden. We aim to retain as many analysable visits as possible without compromising inference. We are developing a Bayesian multiple‑imputation workflow that models the time course jointly, borrowing strength from neighbouring time points, visit indicators, design covariates, and (optionally) historical healthy‑volunteer datasets. Priors allow nonlinear trends, and imputation uncertainty propagates to endpoints. We present simulations reflecting internal trials showing impacts on Cmax, Tmax, and iAUC, with diagnostics, MAR and plausible MNAR sensitivity and early rules on when imputation supports analysis without replacing participants. This pragmatic approach seeks to protect inference, reduce waste, and remain aligned with good practice.

Missing data

Bayesian

Imputation

Clinical trials

Bioavailability 

Biometrics Section