Objectives Alzheimer’s dementia (AD) is the most common type of dementia. However, it is unclear which cognitive enhancer is optimal for severe AD. Patient-level data from people with AD can be helpful to explore patient-level variation per treatment response. Pooling individual patient data (IPD) from multiple randomised clinical trials (RCTs) of clinical interventions is considered the ‘gold standard’ analysis. To examine the comparative efficacy and safety of cognitive enhancers by patient characteristics, such as AD severity and sex, and to assess treatment-by-covariate interactions through IPD network meta-analysis (NMA).

Method We searched for RCTs with adults comparing cognitive enhancers, and addressing cognition using the Mini-Mental State Examination (MMSE) and/or serious adverse events (SAEs). For eligible RCTs, we requested IPD from authors, sponsors and data sharing platforms. We assessed for consistency between results from published RCTs and provided IPD. We applied an available case analysis for each study, but we plan to explore the impact of missing data through the use of informative missingness parameters in NMA. We captured reasons for missing participants and time to SAE. We conducted a 2-stage analysis: at 1st stage IPD from included studies were aggregated to study-level summary; at 2^nd stage the trial parameter estimates were synthesized in a random-effects NMA. We summarized evidence using the odds ratio (OR) and mean difference (MD), respectively. We combined aggregated data from RCTs for which we were unable to obtain IPD.

Results We included 108 RCTs and received IPD for 17 (16%) RCTs. Of the 17 RCTs, we were able to include 12 RCTs in our NMA with complete data. Access to IPD via proprietary sponsor-specific platforms restricted us from combining IPD in a one-stage NMA model. In most IPD, we encountered a high dropout rate (up to 72%), for which most publications used the last observation carried forward imputation method. NMA results including IPD and/or aggregate data will be presented at the EBMLive 2020.

Conclusions An advantage of our IPD-NMA is that we were able to include outcome data, which were not reported in the original publications. Our study will provide insight on personalized medicine for patients with AD.

Acknowledgements We would like to thank the following sponsors for sharing the data with us:

• “This publication is based on research using data from data contributors, AbbVie, Inc, that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.”

• “This study, carried out under YODA Project #2017-1671, used data obtained from the Yale University Open Data Access Project, which has an agreement with JANSSEN RESEARCH & DEVELOPMENT, L.L.C.. The interpretation and reporting of research using this data are solely the responsibility of the authors and does not necessarily represent the official views of the Yale University Open Data Access Project or JANSSEN RESEARCH & DEVELOPMENT, L.L.C..”

• This publication used data obtained from Eisai, GlaxoSmithKline, and Novartis carried under www.ClinicalStudyDataRequest.com

• This publication used data obtained from Lundbeck