Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals; however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. In this guide, we illustrate how to extract Cohen’s f2 for two variables within a mixed-effects regression model using PROC MIXED in SAS® software. Two examples of calculating Cohen’s f2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies.

Introduction The growing popularity of electronic cigarettes (e-cigarettes) among youth raises concerns about possible causal effects on conventional cigarette smoking. However, past research remains inconclusive due to heavy confounding between cigarette and e-cigarette use. This study uses propensity score methods to robustly adjust for shared risk in estimating the relationship between e-cigarette use and conventional smoking. Methods Cross-sectional data from 8th and 10th graders were drawn from the 2015–2016 waves of Monitoring the Future (n = 12 421). The effects of (1) lifetime and (2) current e-cigarette use on (A) lifetime and (B) current conventional cigarette smoking were examined using logistic regression analyses with inverse propensity weighting based on 14 associated risk factors. Results After accounting for the propensity for using e-cigarettes based on 14 risk factors, both lifetime and current e-cigarette use significantly increased the risk of ever smoking a conventional cigarette (OR = 2.49, 95% CI = 1.77 to 3.51; OR = 2.32, 95% CI = 1.66 to 3.25, respectively). However, lifetime (OR = 2.17, 95% CI = 0.62 to 7.63) and current e-cigarette use (OR = 0.95, 95% CI = 0.55 to 1.63) did not significantly increase the risk of current conventional cigarette smoking. Conclusions E-cigarette use does not appear to be associated with current, continued smoking. Instead, the apparent relationship between e-cigarette use and current conventional smoking is fully explained by shared risk factors, thus failing to support claims that e-cigarettes have a causal effect on concurrent conventional smoking among youth. E-cigarette use has a remaining association with lifetime cigarette smoking after propensity score adjustment; however, future research is needed to determine whether this is a causal relationship or merely reflects unmeasured confounding. Implications This study examines the relationship between e-cigarette use and conventional smoking using inverse propensity score weighting, an innovative statistical method that produces less-biased results in the presence of heavy confounding. Our findings show that the apparent relationship between e-cigarette use and current cigarette smoking is entirely attributable to shared risk factors for tobacco use. However, e-cigarette use is associated with lifetime cigarette smoking, though further research is needed to determine whether this is a causal relationship or merely reflects unaccounted-for confounding. Propensity score weighting produced significantly weaker effect estimations compared to conventional regression control.

Background and Aims Recent nicotine use trends raise concerns that electronic cigarettes (ECs) may act as a gateway to cigarettes among adolescents. The aims of this study were to examine prevalence trends of exclusive EC use, exclusive cigarette use and dual use to determine the corresponding ages of initiation and to investigate hypothetical trends in total nicotine use and cigarette use in the absence of ECs among US adolescents. Design Observational study using data from the National Youth Tobacco Survey (NYTS) to statistically model trends in the prevalences of each user group and their initiation ages. Projections from counterfactual models based on data from 1999 to 2009 (before EC introduction) were compared with actual trends based on data from 1999 to 2018. Rigorous error analyses were applied, including Theil proportions. Setting USA. Participants and measurements Adolescents aged 12–17 years who were established exclusive cigarette users (≥ 100 cigarettes smoked and ≤ 100 days vaped), established exclusive EC users (< 100 cigarettes smoked and > 100 days vaped) and established dual users (≥ 100 cigarettes smoked and > 100 days vaped), based on cumulative life‐time exposure (n ≈ 12 500–31 000 per wave). Findings Exclusive cigarette use prevalence declined from 1999 to 2018, while exclusive EC use and dual use prevalences increased since their introduction in 2009. The age of cigarette initiation began a slight increase after 2014, whereas the age for EC use remained approximately constant and was higher than that of cigarettes. The counterfactual comparison results were consistent with ECs not increasing the number of US adolescent nicotine users, and in fact diverting adolescents from cigarettes. Conclusions Electronic cigarettes may have offset conventional smoking among US adolescents between 2010 and 2018 by maintaining the total nicotine use prevalence and diverting them from more harmful conventional smoking. Additionally, electronic cigarette users appear to initiate at older ages relative to conventional smokers, which is associated with lower risk.

Introduction Despite the highly replicated relationship between depression and nicotine dependence, little is known about this association across both time and levels of lifetime smoking exposure. In the present study, we evaluate if symptoms of depression are associated with emerging nicotine dependence after accounting for smoking exposure and whether this relationship varies from adolescence to young adulthood and across increasing levels of smoking. Patients and Methods The sample was drawn from the Social and Emotional Contexts of Adolescent Smoking Patterns Study which measured smoking, nicotine dependence and depression over 6 assessment waves spanning 6 years. Analyses were based on repeated assessment of 941 participants reporting any smoking 30 days prior to individual assessment waves. Mixed-effects regression models were estimated to examine potential time and smoking exposure varying effects in the association between depression and nicotine dependence. Results Inter-individual differences in mean levels of depression and within subject changes in depression from adolescence to young adulthood were each significantly associated with nicotine dependence symptoms over and above lifetime smoking exposure. This association was consistent across both time and increasing levels of lifetime smoking. Discussion Depression is a consistent risk factor for nicotine dependence over and above exposure to cigarettes and this association can be demonstrated from the earliest experiences with smoking in adolescents through the establishment of more regular smoking patterns across the transition to young adulthood. Conclusion Depression remains a prominent risk factor for nicotine dependence, and youth with depression symptoms represent an important subgroup in need of targeted smoking intervention.