Twitter Facebook LinkedIn
Center for Global Research Data

Comparative safety and effectiveness of cognitive enhancers for Alzheimer’s dementia a systematic review and individual patient data network meta-analysis 

Lead Investigator: Sharon Strauss, St. Michael’s Hospital
Title of Research Proposal: Comparative safety and effectiveness of cognitive enhancers for Alzheimer’s dementia a systematic review and individual patient data network meta-analysis
Vivli Data Request: 4116
Funding Source: Government Funding: This research is funded by the CIHR Drug Safety and Effectiveness Network (grant number 137713)
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Alzheimer’s dementia (AD) is the most common cause of dementia and has an insidious onset with progressive deterioration in cognition (eg, memory, thinking and perception), function, behaviour and mood. To date, 46.8 million people worldwide live with dementia. This number will almost double every 20 years, and it is estimated to reach 131.5 million by 2050. A study showed that as age increases, the rates of AD increase overall for both men and women, but it is more prevalent in women (rate/100 years=2.50 (1.85–3.41)) than men (rate/100 years=1.89 (1.22–2.94)). It is currently unclear if galantamine, rivastigmine or donepezil should be used by patients with severe AD, and whether memantine is the most optimal treatment for severe AD. The use of acetylcholinesterase inhibitors and increased doses of donepezil in patients with dementia increase the risk of bradycardia, as well, cholinesterase inhibitors doubles the risk of hospitalisation for bradycardia in older patients. Also, the use of other medications may increase risk of adverse events. For example, cardiac medications like β-blockers may increase risk of bradycardia, and anti-inflammatories may increase risk for gastrointestinal bleeding.

We previously attempted a systematic review and NMA of aggregated data, but we were unable to provide definitive conclusions regarding the influence of patient characteristics on the results.[9, 10] In this study we tailored results to age, AD severity, comorbidity and study duration via subgroup analysis. These results were similar to 4 Cochrane reviews examining cognitive enhancers for AD.[11-14] Specifically, the reviews showed that donepezil, rivastigmine and galantamine, significantly improved cognition[11-14] against placebo, yet cholinisterase inhibitors overall and donepezil improved behaviour,cholinisterase inhibitors overall and rivastigmine improved function, and rivastigmine improved AD severity. These effects were associated with higher doses of rivastigmine, suggesting that dose may be a treatment effect modifier. The use of IPD will increase power and will help explain the relationship between treatment effects and patient-level characteristics. 

Statistical Analysis Plan:

In the analyses, we aim to include IPD on: (1) patients, including age, sex, severity of Alzheimer’s disease (e.g., baseline MMSE level), presence of behavioural disturbance, comorbid conditions (e.g., stroke, cardiovascular conditions, Parkinson’s disease), other medications used for each patient (such as β-blockers and other antiarrhythmic drugs), drop-outs along with reasons for drop-out, and number of participants; (2) medication, including treatment patient was allocated, dosage; (3) outcomes, including event and date of event and time taken to achieve the event for SAEs, and MMSE values and measurement dates; and (4) date and method of randomisation.

As with the original review, we will appraise the risk of bias using the Cochrane Risk of Bias tool. We will draw a comparison-adjusted funnel plot for both outcomes. Two review authors will also independently assess the quality of evidence in each NMA using the GRADE approach as extended for network meta-analysis.

We will perform a Bayesian hierarchical random-effects meta-analysis for each treatment comparison, as we anticipate clinical and methodological between-study heterogeneity. We will perform a two-stage analysis, where at the first stage each individual patient will be analysed separately in each trial and at the second step the trial parameter estimates will be synthesised in a pairwise meta-analysis. All IPD from included studies will be first aggregated to study-level summary statistics using the SAS portal provided by AbbVie, and then these estimates will be introduced into the random-effects meta-analysis model. We will use the odds ratio for SAE and the mean difference effect size for MMSE. In case we are able to obtain IPD for a subset of trials, then we will use a two-part model with the same between-study variance in both parts and accounting for treatment-by-covariate interactions (including for example co-morbidities such as arrhythmias in the model). The first part will entail the two stage model described above using IPD only, whereas the second part will entail applying a pairwise meta-analysis with aggregate data.

For a connected network of trials, the random-effects NMA model will be used. If possible, we will combine information across a network of trials using only IPD. If we are not successful in obtaining IPD for at least one study, we will combine both IPD and aggregated data in a single model. Again, a two-part analysis will be applied, considering the IPD reduced to aggregate data in the first part, and the aggregate data as identified in the published trials in the second part. Both IPD and aggregate data studies will share the same amount of heterogeneity. Information on patient-level covariates (e.g., AD severity, sex) will be included in the model as secondary analyses. We will evaluate the consistency assumption using the design-by-treatment interaction model and the loop-specific method using aggregated data. If inconsistency is suggested, we will check the data for discrepancies and if none are identified, subgroup or meta-regression analyses will be performed.

We will estimate subgroup effects (e.g., age, sex) using treatment-by-covariate interaction terms within studies and combining these across studies. We will apply 3 model specifications assuming that the regression coefficients are: a) different and unrelated across comparisons, b) different but related, sharing the same distribution, and c) identical across comparisons. We will compare the results of the models by evaluating the statistical significance of the regression coefficients for interactions, monitoring the reduction in the between-study variance, and using the Deviance Information Criterion to compare the overall fit and parsimony of the models. We will rank the interventions for each outcome using the surface under the cumulative ranking curve.

We will conduct multiple sensitivity analyses to examine the robustness of our results. We will: 1) restrict to studies with IPD only, 2) use different priors for the between study variance, 3) restrict to RCTs with a low risk of bias, 4) use different imputation techniques for missing outcome data.

All analyses will be conducted using the Bayesian software OpenBUGS. Two chains will be generated, and convergence will be evaluated by their mixing, after discarding the first 10,000 iterations. We will use vague priors for all parameters of the models apart from the between-study variance for which we will use informative priors.

Requested Studies:

A Randomized Study of H3 Antagonist ABT-288 in Mild-To-Moderate Alzheimer’s Dementia

Sponsor: AbbVie
Study ID: NCT01018875
Sponsor ID: NCT01018875

A phase 2 randomized, controlled trial of the a7 agonist ABT-126 in mild-to-moderate Alzheimer’s dementia

Sponsor: AbbVie
Study ID: NCT00948909
Sponsor ID: NCT00948909


Veroniki AA, Ashoor H, Rios P, Seitidis G, Mavridis D, Holroyd-Leduc J, Straus S, Tricco A. Comparative safety and efficacy of cognitive enhancers for Alzheimer’s dementia: An individual patient data network meta-analysis. In: Advances in Evidence Synthesis: special issue. Cochrane Database of Systematic Reviews 2020;(9 Suppl 1):455.