Linda M. Collins

A comparison of inclusive and restrictive strategies in modern missing data procedures.

Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.

New methods for the analysis of change

Differential Structural Equation Modeling of Intraindividual Variability - Steven M. Boker Toward a Coherent Framework for Comparing Trajectories of Individual Change - Stephen W. Raudenbush A Structural Equation Modeling Approach to the General Linear Mixed Model - Michael J. Rovine and Peter C. M. Molenaar The Best of Both Worlds: Combining Autoregressive and Latent Curve Models - Patrick J. Curran and Kenneth A. Bollin Latent Differences Score Structural Models for Linear Curve Models - John J. McArdle and Fumiaki Hamagami Second-Order Latent Growth Model - Aline G. Sayer and Patricio E. Cumsille The Role of Factorial Invariance in Modeling Growth and Change - William Meredith and John Horn Trait-State Models for Longitudinal Data - David A. Kenny and Alex Zautra Reliability for Static and Dynamic Categorical Latent Variables: Developing Measurement Instruments Based on a Model of the Growth Process - Linda M. Collins Second-Generation Structural Equation Modeling With a Combination of Categorical and Continuous Latent Variables: New Opportunities for Latent Class-Latent Growth Modeling - Bent Muthen Planned Missing Data Design in an Analysis of Change - John W. Graham, Bonnie J. Taylor, and Patricio E. Cumsille Multiple Imputation With PAN - Joseph L. Schafer

PROC LCA: A SAS Procedure for Latent Class Analysis

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.

A conceptual framework for adaptive preventive interventions.

Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participants values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed.

An Alternative Framework for Defining Mediation.

The present article provides an alternative framework for evaluating mediated relationships. From this perspective. a mediated process is a chain reaction, beginning with an independent variable that affects a mediator that in turn affects an outcome. The definition of mediation offered here, presented for stage sequences, states three conditions for establishing mediation: (a) the independent variable affects the probability of the sequence no mediator to mediator to outcome; (b) the independent variable affects the probability of a transition into the mediator stage; (c) the mediator affects the probability of a transition into the outcome stage at every level of the independent variable. This definition of mediation is compared and contrasted with the well-known definition of mediation for continuous variables discussed in Baron and Kenny (1986), Judd and Kenny (1981), and Kenny, Kashy, and Bolger (1997). The definition presented in this article emphasizes the intraindividual, time-ordered nature of mediation.

Attrition in prevention research

Selective attrition can detract from the internal and external validity of longitudinal research. Four tests of selective attrition applicable to longitudinal prevention research were conducted on data bases from two recent studies. These tests assessed (1) differences between dropouts and stayers in terms of pretest indices of primary outcome variables (substance use), (2) differences in change scores for dropouts and stayers, (3) differences in rates of attrition among experimental conditions, and (4) differences in pretest indices for dropouts among conditions. Results of these analyses indicate that cigarette smokers, alcohol drinkers, and marijuana users are more likely to drop out than nonusers, limiting the external validity of both studies. For one project, differential rates of attrition among conditions suggested a possible attrition artifact which will interfere with interpretation of outcome results, possibly masking true program effectiveness. Recommendations for standardizing reports of attrition and for avoiding attrition through second efforts are made.

Web-Based Smoking-Cessation Programs : Results of a Randomized Trial

BACKGROUND Initial trials of web-based smoking-cessation programs have generally been promising. The active components of these programs, however, are not well understood. This study aimed to (1) identify active psychosocial and communication components of a web-based smoking-cessation intervention and (2) examine the impact of increasing the tailoring depth on smoking cessation. DESIGN Randomized fractional factorial design. SETTING Two HMOs: Group Health in Washington State and Henry Ford Health System in Michigan. PARTICIPANTS 1866 smokers. INTERVENTION A web-based smoking-cessation program plus nicotine patch. Five components of the intervention were randomized using a fractional factorial design: high- versus low-depth tailored success story, outcome expectation, and efficacy expectation messages; high- versus low-personalized source; and multiple versus single exposure to the intervention components. MEASUREMENTS Primary outcome was 7 day point-prevalence abstinence at the 6-month follow-up. FINDINGS Abstinence was most influenced by high-depth tailored success stories and a high-personalized message source. The cumulative assignment of the three tailoring depth factors also resulted in increasing the rates of 6-month cessation, demonstrating an effect of tailoring depth. CONCLUSIONS The study identified relevant components of smoking-cessation interventions that should be generalizable to other cessation interventions. The study also demonstrated the importance of higher-depth tailoring in smoking-cessation programs. Finally, the use of a novel fractional factorial design allowed efficient examination of the study aims. The rapidly changing interfaces, software, and capabilities of eHealth are likely to require such dynamic experimental approaches to intervention discovery.

Pubertal Timing and the Onset of Substance Use in Females During Early Adolescence

The goal of this study is to examine in detail the relationship between pubertal timing and substance use onset using a sample of females from The National Longitudinal Study of Adolescent Health. The sample includes 966 females who were in 7th grade at Wave 1 and 8th grade at Wave 2. Participants in the sample are approximately 69% White, 20% African American, 4% Asian or Pacific Islander, 2% American Indian, 4% other, of Hispanic origin, and 1% other, not of Hispanic origin. Twenty percent of the females were identified as early maturers based on self-reports of body changes (increased breast size and body curviness) measured in 7th grade. These participants are hypothesized to be at increased risk for substance use onset. Important differences in substance use onset were found between early maturers and their on-time and late-maturing counterparts. During 7th grade, females in the early-maturing group are three times more likely to be in the most advanced stage of substance use (involving alcohol use, drunkenness, cigarette use, and marijuana use) than are those in the on-time/late group. Prevalence rates indicate that early maturers are more likely to have tried alcohol, tried cigarettes, been drunk, and tried marijuana. Prospective findings show that early developers are significantly more likely to transition out of the “No Substance Use” stage between 7th and 8th grade (47% for early developers vs. 22% for on-time and late developers). In addition, early developers are more likely to advance in substance use in general, regardless of their level of use at Grade 7.

The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions

The multiphase optimization strategy (MOST) is a new methodological approach for building, optimizing, and evaluating multicomponent interventions. Conceptually rooted in engineering, MOST emphasizes efficiency and careful management of resources to move intervention science forward steadily and incrementally. MOST can be used to guide the evaluation of research evidence, develop an optimal intervention (the best set of intervention components), and enhance the translation of research findings, particularly type II translation. This article uses an ongoing study to illustrate the application of MOST in the evaluation of diverse intervention components derived from the phase-based framework reviewed in the companion article by Baker et al. (Ann Behav Med, in press, 2011). The article also discusses considerations, challenges, and potential benefits associated with using MOST and similar principled approaches to improving intervention efficacy, effectiveness, and cost-effectiveness. The applicability of this methodology may extend beyond smoking cessation to the development of behavioral interventions for other chronic health challenges.

Modeling transitions in latent stage-sequential processes: a substance use prevention example

This article illustrates the use of latent transition analysis (LTA), a methodology for testing stage-sequential models of individual growth. LTA is an outgrowth of latent class theory and is a particular type of latent Markov model emphasizing the use of multiple manifest indicators. LTA is used to compare the fit of two models of early adolescent substance use onset and to assess the effects of a school-based substance use prevention program on Ss measured in 7th grade and again in 8th grade. Several interesting findings emerged. First, a model of substance use onset including both alcohol and tobacco use as possible starting points fit better than a model that included alcohol use as the only starting point. Second, Ss who had tried tobacco but not alcohol in in 7th grade seemed to be on an accelerated onset trajectory. Third, the normative education prevention program was generally successful, except for the students who had tried only tobacco in 7th grade.