To address socioeconomic challenges associated with its increasing prevalence, data are needed on country-level resource use and costs associated with Alzheimer's disease (AD). GERAS is an 18-month observational study being conducted in France, Germany, and the U.K. (with an 18-month extension in France and Germany), aimed at determining resource use and total costs associated with AD, stratified by AD severity at baseline. Resource use information and time spent on informal care by non-professional caregivers was obtained using the Resource Utilization in Dementia instrument. Total baseline societal costs were based on four cost components: patient health care costs, patient social care costs, caregiver health care costs, and caregiver informal care costs. Overall, 1,497 community-dwelling patients with AD were analyzed at baseline. Estimated mean monthly total societal costs per patient at baseline differed significantly between groups with mild, moderate, and moderately severe/severe AD (p < 0.001 in each country): euro $1,418, euro 1,737, and euro 2,453 in France; euro 1,312, euro $2,412, and euro 3,722 in Germany; and euro 1,621, euro 1,836, andeuro 2,784 in the U.K., respectively. All cost components except caregiver health care costs increased with AD severity. Informal caregiver costs were the largest cost component accounting for about half to just over 60% of total societal costs, depending on country and AD severity group. In conclusion, GERAS study baseline results showed that country-specific costs increase with AD severity. Informal care costs formed the greatest proportion of total societal costs, increasing with AD severity independent of costing method. Longitudinal data will provide information on cost trends with disease progression.

Non-randomized studies aim to reveal whether or not interventions are effective in real-life clinical practice and there is a growing interest in including such evidence in the decision-making process. We evaluate existing methodologies and present new approaches to using non-randomized evidence in a network meta-analysis (NMA) of randomized controlled trials (RCTs) when the aim is to assess relative treatment effects. We first discuss how to assess compatibility between the two types of evidence. We then present and compare an array of alternative methods that allow the inclusion of non-randomized studies in an NMA of RCTs: the naïve data synthesis, the designadjusted synthesis, the use of non-randomized evidence as prior information and the use of threelevel hierarchical models. We apply some of the methods in two previously published clinical examples comparing percutaneous interventions for the treatment of coronary in-stent restenosis and antipsychotics in patients with schizophrenia. We discuss in depth the advantages and limitations of each method and we conclude that the inclusion of real-world evidence from nonrandomized studies has the potential to corroborate findings from RCTs, increase precision and enhance the decision-making process.

Summary Standard network meta‐analysis (NMA) and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. Population adjustment methods relax this assumption using individual patient data from one or more studies. However, current matching‐adjusted indirect comparison and simulated treatment comparison methods are limited to pairwise indirect comparisons and cannot predict into a specified target population. Existing meta‐regression approaches incur aggregation bias. We propose a new method extending the standard NMA framework. An individual level regression model is defined, and aggregate data are fitted by integrating over the covariate distribution to form the likelihood. Motivated by the complexity of the closed form integration, we propose a general numerical approach using quasi‐Monte‐Carlo integration. Covariate correlation structures are accounted for by using copulas. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution. We illustrate the method with a network of plaque psoriasis treatments. Estimated population‐average treatment effects are similar across study populations, as differences in the distributions of effect modifiers are small. A better fit is achieved than a random effects NMA, uncertainty is substantially reduced by explaining within‐ and between‐study variation, and estimates are more interpretable.

Background/Aims: To examine factors influencing the caregiver burden in adult-child and spousal caregivers of community-dwelling patients with Alzheimer's disease (AD). Methods: Baseline data from the 18-month, prospective, observational GERAS study of 1,497 patients with AD in France, Germany, and the UK were used. Analyses were performed on two groups of caregivers: spouses (n = 985) and adult children (n = 405). General linear models estimated patient and caregiver factors associated with subjective caregiver burden assessed using the Zarit Burden Interview. Results: The caregiver burden increased with AD severity. Adult-child caregivers experienced a higher burden than spousal caregivers despite spending less time caring. Worse patient functional ability and more caregiver distress were independently associated with a greater burden in both adult-child and spousal caregivers. Additional factors were differentially associated with a greater caregiver burden in both groups. In adult-child caregivers these were: living with the patient, patient living in an urban location, and patient with a fall in the past 3 months; in spouses the factors were: caregiver gender (female) and age (younger), and more years of patient education. Conclusion: The perceived burden differed between adult-child and spousal caregivers, and specific patient and caregiver factors were differentially associated with this burden.

Several patient and caregiver factors, including factors associated with informal care, should be included when evaluating care options for patients with AD.