A systematic approach for imputing missing dose information in population pharmacokinetic analysis datasets
Authors: Dombrowsky Erin , Sukumar Prema , Thanneer Neelima 
Published: February 2025

Objectives: Accurately capturing all dosing events is critical in the creation of population pharmacokinetic (popPK) analysis datasets. However, due to deficiencies in case report form design or inadequate data reconciliation, missing dose dates or times are common. It is essential to perform imputations on such data so that a complete dosing history is available to ensure the correct interpretation of the data for the popPK analysis and provide meaningful results. Failure to apply standard imputation rules can lead to variations between projects and impact the quality of the analysis and results.Methods: Imputation rules have been developed for imputing missing dose date and time for oral, intravenous (IV) and subcutaneous (SC) doses.Dose records missing both start and end date are not included in the popPK analysis dataset. Typically, complete dosing history is available for IV and SC dosing. However, there are instances where date is available, while start and/or end time is missing. Interval dosing is common for oral dosing where only start and stop dates are recorded. Day one pre-dose, trough, or post-dose pharmacokinetic (PK) collection times, and previously/next-available dose times are used to impute the missing dose time(s). For oral dosing, non-recorded doses can be imputed with the number of additional doses (ADDL) and inter-dose interval (II) variables. For IV and SC dosing, the infusion duration is required to determine the rate of administration. When infusion start and/or end time is not available, the protocol-defined duration is used for imputation.The forthcoming poster will provide comprehensive examples covering various scenarios for IV, SC and oral dosing. It will serve as a reference for adapting standard imputation rules in popPK dataset preparation. Results: The implementation of standardized dose imputation rules provides pharmacometric programmers a systematic way to account for source data deficiencies and ensures the quality of the analysis and results. To maintain transparency in dataset assembly, imputed records are flagged, and the imputation rules are documented in the dataset specification and pharmacometric report.Conclusions: Standardized dose imputation rules enable efficiency in the popPK dataset preparation process. Similar standards have been included in the Clinical Data Interchange Standards Consortium Analysis Data Model popPK Implementation Guide, aiming to establish these as a best-practice industry-wide.Citations: 1. Basic Data Structure for ADaM popPK Implementation Guide v1.0. Available from: https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0 2. Thanneer N, Roy A, Sukumar P, Bandaru J, Carleen E. Best Practices for Preparation of Pharmacometric Analysis Data Sets. Poster session presented at: 5th American Conference on Pharmacometrics; 2014 Oct 12-15; Las Vegas, NV.3. Dombrowsky E, Sukumar P, Bandaru J, Roy A, Thanneer N. Best Practices for Preparation of Submission Quality Data Sets for Pharmacometric Analysis. Poster session presented at: 7th American Conference on Pharmacometrics; 2016 Oct 23-26; Bellevue, WA.