Opportunities:

• Differences across populations are not necessarily the same as differences between individuals – differences in the prevalence, distributions and associations of risk and protective factors with health outcomes may exist.
• Triangulation of risk factor associations across populations and study designs can provide important insights and can help strengthen causal inference.
• Differences in social contexts provide important opportunities for comparison given the importance of social risk factors in the development of dementia.

Considerations around the use of harmonized cognitive functioning measures:

• Essentialist interpretations of differences in mean cognitive functioning should be avoided. Differences should instead be interpreted as the result of different risk and protective factors that are experienced throughout one’s life.
• Although pre-statistical and statistical harmonization processes correct for potential sources of bias and pay careful attention to differences that may be problematic in making comparisons, assumptions, which can never be fully empirically verified, are still required to enable use of these statistical methods. Results using harmonized scores should be interpreted in the context of these assumptions, which may introduce some measurement error.

General considerations:

Theoretical considerations:

• Carefully consider the purpose of the comparative research question.
  • Is a cross-national comparison necessary to answer the research question?
  • What is the hypothesis regarding cross-national differences?
• Incorporate knowledge of relevant social, cultural, economic, political, and historical contextual factors of the countries under study.
  • Is the research question appropriately informed by relevant country-specific contextual factors?
  • Is the interpretation of results appropriately informed by relevant country-specific contextual factors?
  • Are local collaborators needed to provide appropriate expertise and experience?

Methodological considerations:

• Ensure the primary exposure variable can be harmonized.
  • Do the measured exposure variables conceptually represent the same construct across countries?
  • Do any measurement differences prohibit harmonization of the exposure variable?
  • If the exposure variable cannot be harmonized, what are the implications for the project?
• Ensure data on desired model covariates are available for all countries under study.
  • Does the same confounding structure apply to the research question across all countries under study?
  • Are data available on all suspected confounders within each country?
  • If the suspected confounding structure varies across countries, will country-specific models with unique covariate sets be sufficient to answer the research question of interest?
• Choose the most appropriate modeling strategy for the research question and data.
  • Is a pooled analysis or parallel analyses the most appropriate?
  • If a pooled analysis is conducted, are fixed effects for country sufficient to answer the question?
  • If a pooled analysis with fixed effects for country is conducted, have interaction terms between confounders and country been tested or the outcome residualized for country-specific confounders?
  • If a pooled analysis is conducted and random effects preferred, is the group-level N sufficient?
  • If a pooled analysis is conducted, what is the desired approach to handling sampling weights?

Please see Kobayashi et al. [https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.13694] for a more detailed discussion of these issues and potential solutions.