In this study, we aimed to evaluate interrater agreement statistics (IRAS) for use in research on low base rate clinical diagnoses or observed behaviors. Establishing and reporting sufficient interrater agreement is essential in such studies. Yet the most commonly applied agreement statistic, Cohen's κ, has a well known sensitivity to base rates that results in a substantial penalization of interrater agreement when behaviors or diagnoses are very uncommon, a prevalent and frustrating concern in such studies. We performed Monte Carlo simulations to evaluate the performance of 5 of κ's alternatives (Van Eerdewegh's V, Yule's Y, Holley and Guilford's G, Scott's π, and Gwet's AC₁), alongside κ itself. The simulations investigated the robustness of these IRAS to conditions that are common in clinical research, with varying levels of behavior or diagnosis base rate, rater bias, observed interrater agreement, and sample size. When the base rate was 0.5, each IRAS provided similar estimates, particularly with unbiased raters. G was the least sensitive of the IRAS to base rates. The results encourage the use of the G statistic for its consistent performance across the simulation conditions. We recommend separately reporting the rates of agreement on the presence and absence of a behavior or diagnosis alongside G as an index of chance corrected overall agreement.

The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In this article, 2 propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.

Cigarette use is declining among youth in the United States, whereas cannabis use and e-cigarette use are increasing. Cannabis use has been linked with increased uptake and persistence of cigarette smoking among adults. The goal of this study was to examine whether cannabis use is associated with the prevalence and incidence of cigarette, e-cigarette, and dual product use among U.S. youth. Data included U.S. youth ages 12–17 from two waves of the Population Assessment of Tobacco and Health (PATH) Study (Wave 1 youth, n = 13 651; Wave 1 tobacco-naive youth, n = 10 081). Weighted logistic regression models were used to examine the association between Wave 1 cannabis use and (1) Wave 1 prevalence of cigarette/e-cigarette use among Wave 1 youth and (2) Wave 2 incidence of cigarette/e-cigarette use among Wave 1 tobacco-naive youth. Analyses were run unadjusted and adjusted for demographics and internalizing/externalizing problem symptoms. Wave 1 cigarette and e-cigarette use were significantly more common among youth who used versus did not use cannabis. Among Wave 1 tobacco-naive youth, Wave 1 cannabis use was associated with significantly increased incidence of cigarette and e-cigarette use by Wave 2. Youth who use cannabis are more likely to report cigarette and e-cigarette use, and cannabis use is associated with increased risk of initiation of cigarette and e-cigarette use over 1 year. Continued success in tobacco control—specifically toward reducing smoking among adolescents—may require focusing on cannabis, e-cigarette, and cigarette use in public health education, outreach, and intervention efforts. These data extend our knowledge of cigarette and e-cigarette use among youth by showing that cannabis use is associated with increased prevalence and incidence of cigarette and e-cigarette use among youth, relative to youth who do not use cannabis. The increasing popularity of cannabis use among youth and diminished perceptions of risk, coupled with the strong link between cannabis use and tobacco use, may have unintended consequences for cigarette control efforts among youth.

The psychometrics of the Parenting Scale's Overreactivity and Laxness subscales were evaluated using item response theory (IRT) techniques. The IRT analyses were based on 2 community samples of cohabiting parents of 3- to 8-year-old children, combined to yield a total sample size of 852 families. The results supported the utility of the Overreactivity and Laxness subscales, particularly in discriminating among parents in the mid to upper reaches of each construct. The original versions of the Overreactivity and Laxness subscales were more reliable than alternative, shorter versions identified in replicated factor analyses from previously published research and in IRT analyses in the present research. Moreover, in several cases, the original versions of these subscales, in comparison with the shortened versions, exhibited greater 6-month stabilities and correlations with child externalizing behavior and couple relationship satisfaction. Reliability was greater for the Laxness than for the Overreactivity subscale. Item performance on each subscale was highly variable. Together, the present findings are generally supportive of the psychometrics of the Parenting Scale, particularly for clinical research and practice. They also suggest areas for further development.

Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR), may be handled. While the mechanism giving rise to the missing data cannot be determined by the observations, the sensitivity of parameter estimates to missing data assumptions can be studied, for example, by fitting multiple models that make different assumptions about the missing data process. Sensitivity analysis of a mixed model that may include nonlinear parameters when some data are missing is discussed. An example is provided.

Effective, accessible prevention programs are needed for adults at heightened risk for intimate partner violence (IPV). This parallel group randomized controlled trial examines whether such couples receiving the American version of Couple CARE for Parents of Newborns (CCP; Halford et al. 2009) following the birth of a child, compared with controls, report fewer first occurrences of clinically significant IPV, less frequent physical and psychological IPV, and improved relationship functioning. Further, we test whether intervention effects are moderated by level of risk for IPV. Couples at elevated risk for IPV (N = 368) recruited from maternity units were randomized to CCP (n = 188) or a 24-month waitlist (n = 180) and completed measures of IPV and relationship functioning at baseline, post-program (when child was 8 months old), and two follow-ups (at 15 and 24 months). Intervention effects were tested using intent to treat (ITT) as well as complier average causal effect (CACE; Jo and Muthén 2001) structural equation models. CCP did not significantly prevent clinically significant IPV nor were there significant main effects of CCP on clinically significant IPV, frequency of IPV, or most relationship outcomes in the CACE or ITT analyses. Risk moderated the effect of CCP on male-to-female physical IPV at post-program, with couples with a planned pregnancy declining, but those with unplanned pregnancies increasing. This study adds to previous findings that prevention programs for at-risk couples are not often effective and may even be iatrogenic for some couples.