Statistical analysis plan of the study titled “A deprescribing programme aimed to optimize blood glucose-lowering medication in older people with type 2 diabetes mellitus — the OMED2 study: a randomized controlled trial”
Authors:
- Charlotte AndriessenCACharlotte Andriessen
- Peter P. HarmsPHPeter P. Harms
- Marieke T. BlomMBMarieke T. Blom
- Anna W. de BoerABAnna W. de Boer
- G. Ardine de WitGWG. Ardine de Wit
- Ron HeringsRHRon Herings
- Rob J. van MarumRMRob J. van Marum
- Jacqueline G. HugtenburgJHJacqueline G. Hugtenburg
- Daniël van RaalteDRDaniël van Raalte
- Liselotte van BloemendaalLBLiselotte van Bloemendaal
- Giel NijpelsGNGiel Nijpels
- Rimke C. VosRVRimke C. Vos
Open Access
- 30 January 2026
- journal article
- update
- Published by Springer Nature in Trials
- Vol. 27 (1)
Abstract
Background: The OMED2 ( Optimization of Medication in Elderly with Diabetes ) study addresses the effect and implementation of integrating a deprescribing programme (DPP) in general practice. The aim of the DPP is to reduce glucose-lowering medication (SU/insulin) in overtreated older patients. The protocol for this study has been published previously. This statistical analysis plan (SAP) contains a more elaborate outline of the (statistical) methods we plan to use for data analysis. Methods: The OMED2 study is a randomized mixed-methods study with a 2-year follow-up period that compares the effect of the implementation of a DPP in general practice to regular care (control). In this SAP, we report on the (statistical) approaches that we plan to use to address the study objectives. The main objective of the OMED2 study is to examine the effect of the implementation of the DPP on diabetes complications, whereby the total number of diabetes complications related to undertreatment and overtreatment will be summed. Generalized linear mixed models with a Poisson distribution and the DPP as the main determinant will be used to test whether the total number of diabetes complications occurring from the start of the 2-year follow-up until the end of follow-up differs between intervention and control. The incident rate of the number of diabetes complications will be calculated to correct for possible differences in follow-up duration. The model will also include a random effect variable to allow for possible clustering effects by general practice. We will perform intention-to-treat analyses, which include all patients eligible for deprescribing, as well as per protocol analyses, which omit patients who were not deprescribed in the intervention arm. Additionally, approaches to study the implementation of the DPP and the cost-effectiveness of the implementation are outlined in the SAP. Trial registration: ISRCTN Registry ISRCTN50008265. Registered on 1 November 2024.Keywords
Funding Information
- ZonMw (848018102)
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