Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

From Model Organisms to Humans, the Opportunity for More Rigor in Methodologic and Statistical Analysis, Design, and Interpretation of Aging and Senescence Research.

This review identifies frequent design and analysis errors in aging and senescence research and discusses best practices in study design, statistical methods, analyses, and interpretation. Recommendations are offered for how to avoid these problems. The following issues are addressed: (a) errors in randomization, (b) errors related to testing within-group instead of between-group differences, (c) failing to account for clustering, (d) failing to consider interference effects, (e) standardizing metrics of effect size, (f) maximum life-span testing, (g) testing for effects beyond the mean, (h) tests for power and sample size, (i) compression of morbidity versus survival curve squaring, and (j) other hot topics, including modeling high-dimensional data and complex relationships and assessing model assumptions and biases. We hope that bringing increased awareness of these topics to the scientific community will emphasize the importance of employing sound statistical practices in all aspects of aging and senescence research.

Integration at the round table: marine spatial planning in multi-stakeholder settings.

Marine spatial planning (MSP) is often considered as a pragmatic approach to implement an ecosystem based management in order to manage marine space in a sustainable way. This requires the involvement of multiple actors and stakeholders at various governmental and societal levels. Several factors affect how well the integrated management of marine waters will be achieved, such as different governance settings (division of power between central and local governments), economic activities (and related priorities), external drivers, spatial scales, incentives and objectives, varying approaches to legislation and political will. We compared MSP in Belgium, Norway and the US to illustrate how the integration of stakeholders and governmental levels differs among these countries along the factors mentioned above. Horizontal integration (between sectors) is successful in all three countries, achieved through the use of neutral 'round-table' meeting places for all actors. Vertical integration between government levels varies, with Belgium and Norway having achieved full integration while the US lacks integration of the legislature due to sharp disagreements among stakeholders and unsuccessful partisan leadership. Success factors include political will and leadership, process transparency and stakeholder participation, and should be considered in all MSP development processes.