Review ArticleOptimal Sampling Strategy Development Methodology Using Maximum A Posteriori Bayesian Estimationvan der Meer, A Franciscus MD*; Marcus, Marco A E MD, PhD*; Touw, Daniël J PhD†; Proost, Johannes H PhD‡; Neef, Cees PhD*Author Information From *Maastricht University Medical Centre, Maastricht, The Netherlands; †Apotheek Haagse Ziekenhuizen, The Hague, The Netherlands; and ‡University of Groningen, Groningen, The Netherlands. Received for publication July 1, 2010; accepted January 7, 2011. A grant was received from the hospital development fund, grant number PF 231. Correspondence: A. Franciscus van der Meer, MD, Department of Anesthesiology, Postbus 5800, 6202 AZ Maastricht, The Netherlands (e-mail: email@example.com). Therapeutic Drug Monitoring: April 2011 - Volume 33 - Issue 2 - p 133-146 doi: 10.1097/FTD.0b013e31820f40f8 Buy Metrics Abstract Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility. © 2011 Lippincott Williams & Wilkins, Inc.