Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.

In recent years, latent class models have proven useful for analyzing relationships between measured multiple indicators and covariates of interest. Such models summarize shared features of the multiple indicators as an underlying categorical variable, and the indicators' substantive associations with predictors are built directly and indirectly in unique model parameters. In this paper, we provide a detailed study on the theory and application of building models that allow mediated relationships between primary predictors and latent class membership, but that also allow direct effects of secondary covariates on the indicators themselves. Theory for model identification is developed. We detail an Expectation-Maximization algorithm for parameter estimation, standard error calculation, and convergent properties. Comparison of the proposed model with models underlying existing latent class modeling software is provided. A detailed analysis of how visual impairments affect older persons' functioning requiring distance vision is used for illustration.

Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the “Lifespan and Life Course Research: integrating strategies” “Un-Meeting” to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.

Older adults are often underrepresented in clinical research, even though older adults are major consumers of novel therapies. We present major themes and recommendations from the 2021 "Inclusion of Older Adults in Clinical Research" Workshop, convened by the Clinical and Translational Science Award (CTSA) Inclusion of Older Adults as a Model for Special Populations Workgroup and the Research Centers Collaborative Network (RCCN). The goal of this workshop was to develop strategies to assist the research community in increasing the inclusion of older adults in clinical research. Major identified barriers include historical lack of federal guidelines, ageist biases and stereotypes, and lack of recruitment and retention techniques or infrastructure focused on older adults. Three key recommendations emerged: 1) engaging with the policymaking process to further promote inclusion; 2) using the CTSA Workgroup Presentation Materials Library and other resources to overcome ageism, and 3) building institutional capacity to support age inclusion.

This study examined whether participation in a variety of lifestyle activities was comparable to frequent participation in cognitively challenging activities in mitigating impairments in cognitive abilities susceptible to aging in healthy, community-dwelling older women. Frequencies of participation in various lifestyle activities on the Lifestyle Activities Questionnaire (LAQ) were divided according to high (e.g., reading), moderate (e.g., discussing politics), and low (e.g., watching television) cognitive demand. We also considered the utility of participation in a variety of lifestyle activities regardless of cognitive challenge. Immediate and delayed verbal recall, psychomotor speed, and executive function were each measured at baseline and at five successive exams, spanning a 9.5-year interval. Greater variety of participation in activities, regardless of cognitive challenge, was associated with an 8 to 11% reduction in the risk of impairment in verbal memory and global cognitive outcomes. Participation in a variety of lifestyle activities was more predictive than frequency or level of cognitive challenge for significant reductions in risk of incident impairment on measures sensitive to cognitive aging and risk for dementia. Our findings offer new perspectives in promoting a diverse repertoire of activities to mitigate age-related cognitive declines. (JINS, 2012, 18, 286–294)