AB1165 MEDICATION ADHERENCE DATA IN A RANDOMIZED TRIAL: LARGE CHALLENGES TO COME FROM RAW DATA TO A WORKABLE AND RELIABLE DATASET. (2nd June 2020) - Hartman, L.; Alessandri, E.; Bos, R.; Opris-Belinski, D.; Kok, M. R.; Griep-Wentink, H.; Klaasen, R.; Allaart, C.; Bruyn, G.; Raterman, H.; Voshaar, M.; Gomes, N.; et al.
Abstract : Background: Medication adherence in the GLORIA trial, among elderly patients with rheumatoid arthritis, is measured with caps that register openings of the medication bottle. At each study visit, one or two medication bottles with cap (kits) are dispensed, each containing 90 capsules. Multiple steps are needed to come to a workable dataset to describe adherence. Objectives: To describe the steps that are needed to come from raw data to a workable dataset to analyze adherence data that are recorded by electronic caps. Methods: The medication bottle contains a cap with the ability to register cap openings. The raw dataset from the caps consist of an excel file with one opening event per row, recorded as date and time. One cap yields approximately 90 rows. First, the kit numbers were matched to the corresponding patient numbers, that are recorded in another excel file. Instances where two kits were dispensed were recorded with two kit numbers in one cell and need to be copied to two cells with one kit number. Second, the VLOOKUP function was used to combine dates and kit numbers. One row now contains all openings from one kit. Then, the number of days between first opening and each next opening date was calculated. A range of 90 days was made to calculate how many times the bottle was opened on each day of the 90-days period. The results were color-coded to visualize instances of zero, one or ≥two openings on a day. Results: The colored calendar matrix (Figure 1 ) can now be used to categorize adherence patterns. Conclusion: A monitoring cap seems a simple instrument to measure adherence. However, multiple steps and a lot of time are needed to come to a workable dataset for the study of adherence patterns. Acknowledgments: The GLORIA project is funded by the European Union's Horizon 2020 research and innovation programme under the topic ‟Personalizing Health and Care'', grant agreement No 634886. Disclosure of Interests: Linda Hartman: None declared, Elisa Alessandri: None declared, Reinhard Bos: None declared, Daniela Opris-Belinski Speakers bureau: as declared, Marc R Kok Grant/research support from: BMS and Novartis, Consultant of: Novartis and Galapagos, Hanneke Griep-Wentink: None declared, Ruth Klaasen: None declared, Cornelia Allaart: None declared, George Bruyn: None declared, Hennie Raterman Grant/research support from: UCB, Consultant of: Abbvie, Amgen, Bristol-Myers Sqibb, Cellgene and Sanofi Genzyme, Marieke Voshaar Grant/research support from: part of phd research, Speakers bureau: conducting a workshop (Pfizer), Nuno Gomes: None declared, Rui Pinto: None declared, Thomas Klausch: None declared, WIllem Lems Grant/research support from: Pfizer, Consultant of: Lilly, Pfizer, Maarten Boers: None declared