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Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to ad-hoc methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which ad-hoc procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts ad-hoc methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of ad-hoc procedures may be muted when missing data percentages are modest.

Youth at-risk for HIV are also at-risk for mental health disorders and psychiatric hospitalization. Understanding the association between engagement in HIV prevention, concurrent risk behaviors, and psychiatric hospitalization may lead to improvements in integrated prevention and mental health treatment efforts. Youth at-risk for HIV, aged 14-24 years old, predominantly Black/African American and Latinx (75%) were recruited through youth-serving clinics and community sites in Los Angeles (n = 839) and New Orleans (n = 647). We compared youth with and without histories of psychiatric hospitalization on engagement in HIV prevention, concurrent risk behaviors, and demographic characteristics. We examined predictors of hospitalization using multiple imputations for missing data. Hospitalized youth (30%) were more involved in HIV programs, but were less likely to use PrEP/PEP or condoms than non-hospitalized youth. The odds of hospitalization were higher for transgender/gender nonconforming youth relative to cisgender youth; the OR was increased after adjustment for concurrent risk behaviors. Hospitalization was associated with homelessness, trauma, incarceration, substance use, and involvement in substance abuse treatment programs. There is a continuing need to integrate the diagnosis and treatment of mental health disorders into HIV prevention programs to better address multiple challenges faced by vulnerable youth.

Background To slow the spread of the COVID-19 virus, governments across the globe instituted stay-at-home orders leading to increased stress and social isolation. Not surprisingly, alcohol sales increased during this period. While most studies primarily focused on alcohol consumption among college students or adults, this study investigates alcohol misuse among marginalized youth in the USA. We examined risk factors associated with hazardous alcohol use and binge drinking including risk behaviors, life stressors and demographic characteristics. Methods In October 2020, youth living with or at high risk for acquiring human immunodeficiency virus (HIV), participating in community-based research to improve HIV prevention and care, were invited to complete an online survey to assess the impact of the stay-at-home orders on multiple aspects of their daily life. Results Respondents (n  = 478) were on average 23 years old; cisgender (84%), not-heterosexual (86.6%), Latino or Black/African American (73%) and assigned male at birth (83%); 52% reported being employed and 14% reported living with HIV. White participants and those who use drugs had higher odds of hazardous alcohol use and binge drinking, compared with other race categories and non-drug users, respectively. Conclusion Contrary to findings from adult studies, we did not observe an increase in hazardous or binge drinking among youth at risk for HIV. Hazardous alcohol use and binge drinking was more likely among White participants, those who use drugs and those who were hazardous/binge drinkers prior to the COVID-19 lockdown, which points to the importance of identifying and treating youth who misuse alcohol early to prevent future alcohol misuse. [ABSTRACT FROM AUTHOR]