R. J. Wirth

Externalizing symptoms among children of alcoholic parents: Entry points for an antisocial pathway to alcoholism.

The authors examined heterogeneity in risk for externalizing symptoms in children of alcoholic parents, as it may inform the search for entry points into an antisocial pathway to alcoholism. That is, they tested whether the number of alcoholic parents in a family, the comorbid subtype of parental alcoholism, and the gender of the child predicted trajectories of externalizing symptoms over the early life course, as assessed in high-risk samples of children of alcoholic parents and matched controls. Through integrative analyses of 2 independent, longitudinal studies, they showed that children with either an antisocial alcoholic parent or 2 alcoholic parents were at greatest risk for externalizing symptoms. Moreover, children with a depressed alcoholic parent did not differ from those with an antisocial alcoholic parent in reported symptoms. These findings were generally consistent across mother, father, and adolescent reports of symptoms; child gender and child age (ages 2 through 17); and the 2 independent studies examined. Multialcoholic and comorbid-alcoholic families may thus convey a genetic susceptibility to dysregulation along with environments that both exacerbate this susceptibility and provide few supports to offset it.

A SAS Macro for Estimating and Visualizing Individual Growth Curves.

Longitudinal data analyses can be usefully supplemented by the plotting of individual growth curves. Unfortunately, such graphics can be challenging and tedious to produce. This article presents and demonstrates a SAS macro designed to automate this task. The OLStraj macro graphically depicts ordinary least squares (OLS)-estimated individual trajectories, describes interindividual variability in OLS-estimated growth parameters, and identifies possible outlier observations. Analytical developments are briefly outlined, and the use of the macro is demonstrated, with particular attention paid to the potential utility of the macro as both a data screening and post hoc diagnostic device. Potential limitations of the macro and suggestions for future developments are discussed. It is hoped that the program will be of use to applied researchers who seek to maximize the effectiveness of growth curve models in answering questions about stability and change.

Validation of the yale‐brown obsessive compulsive scale modified for binge eating

OBJECTIVE Establish the Yale-Brown obsessive compulsive scale modified for binge eating (YBOCS-BE) as a fit for purpose measure of treatment benefit in clinical trials of binge eating disorder (BED). METHODS YBOCS-BE psychometric properties were evaluated with data from a Phase 2 randomized controlled trial of lisdexamfetamine dimesylate in 260 adults with BED. Assessments included: Cohens effect size estimates of item-level sensitivity and scale-level external responsiveness; item-to-total correlations; Cronbachs alpha for internal consistency reliability; Spearman correlations against reference measures for construct validity; known-groups analyses for discriminating ability; t tests of within-group differences between baseline and post baseline visits for internal responsiveness; and multiple anchor-based approaches to estimate minimum clinically important change (MCIC). RESULTS No significant distribution anomalies were seen. Items appear sensitive to treatment group differences. Item-to-total correlations were positive. Internal consistency is 0.81. Large correlations (>0.50) were seen between YBOCS-BE score change and the Clinical Global Impression-Improvement (CGI-I; 0.58) and score changes for the following; number of binge days (0.38), Clinical Global Impression-Severity (CGI-S; 0.57), the disinhibition (0.57) and hunger (0.52) subscales of the Three-Factor Eating Questionnaire (TFEQ), and the Barratt Impulsiveness Scale (BIS-11; 0.58). MCIC estimates range from -4 to -17. DISCUSSION The YBOCS-BE was found to be a reliable and valid measure of an important and unique concept in BED-related clinical studies. Study limitations include using protocol-defined BED severity level and the exclusion of psychiatric comorbidities.

The incorporation of categorical measurement models in the analysis of individual growth

Researchers often grapple with the idea that an observed relationship may be part of a more complex chain of effects. These complex relationships are described in terms such as indirect influences, distal vs. proximal causes, intermediate outcomes, and ultimate causes; all of which share the concept of mediation. Similarly, researchers must often consider that an observed relationship may be part of a more complex, qualified system. These relationships are described using concepts such as interactions, subgroup differences, and shocks; all of which share the concept of moderation. Generally speaking, a mediator can be thought of as the carrier or transporter of information along the causal chain of effects. A moderator, on the other hand, is the changer of a relationship in as ystem. In this chapter, we explore both empirical and theoretical considerations in modeling mediation and moderation using structural equation modeling. OurThe multilevel approach uses a data set in which the records are person-days. On each record variables called “couple” (couple number), “exmprt” (examinee vs. partner), and “day” indicate which kind of person and which day is represented on the record. Before the analysis is run, some new variables are created that contain the same information. The variable “daycl” is identical to “day,” but will be used to define day as a class variable. The variable “exmnee” is a dummy code with 1 for examinee and 0 for partner. The variable “partner” is the complement of the latter: It is a dummy code with 1 for partner and 0 for examinee. With these variables one can use the following PROC MIXED syntax. PROC MIXED DATA=anger COVTEST METHOD=REML; TITLE ‘Examinee and Partner random effects and correlated errors’; CLASS couple exmprt daycl ; MODEL anger=exmnee partner day / S NOINT;Contents: Preface. N.A. Card, T.D. Little, J.A. Bovaird, Modeling Ecological and Contextual Effects in Longitudinal Studies of Human Development. S.M. Hofer, L. Hoffman, Statistical Analysis With Incomplete Data: A Developmental Perspective. K.J. Preacher, L. Cai, R.C. MacCullum, Alternatives to Traditional Model Comparison Strategies for Covariance Structure Models. S.E. Embretson, Impact of Measurement Scale in Modeling Developmental Processes and Ecological Factors. P.J. Curran, M.C. Edwards, R.J. Wirth, A.M. Hussong, L. Chassin, The Incorporation of Categorical Measurement Models in the Analysis of Individual Growth. T.D. Little, N.A. Card, D.W. Slegers, E.C. Ledford, Representing Contextual Effects in Multiple-Group MACS Models. J.A. Bovaird, Multilevel Structural Equation Models for Contextual Factors. D. Hedeker, R.J. Mermelstein, Mixed-Effects Regression Models With Heterogeneous Variance: Analyzing Ecological Momentary Assessment (EMA) Data of Smoking. T.D. Little, N.A. Card, J.A. Bovaird, K.J. Preacher, C.S. Crandel, Structural Equation Modeling of Mediation and Moderation With Contextual Factors. D.B. Flora, S.T. Khoo, L. Chassin, Moderating Effects of a Risk Factor: Modeling Longitudinal Moderated Mediation in the Development of Adolescent Heavy Drinking. D.J. Bauer, M.J. Shanahan, Modeling Complex Interactions: Person-Centered and Variable-Centered Approaches. N. Bolger, P.E. Shrout, Accounting for Statistical Dependency in Longitudinal Data on Dyads. S.M. Boker, J-P. Laurenceau, Coupled Dynamics and Mutually Adaptive Context. N. Ram, J.R. Nesselroade, Modeling Intraindividual and Intracontextual Change: Rendering Developmental Contextualism Operational. J.L. Rodgers, The Shape of Things to Come: Using Developmental Curves From Adolescent Smoking and Drinking Reports to Diagnose the Type of Social Process that Generated the Curves. K.J. Grimm, J.J. McArdle, A Dynamic Structural Analysis of the Impacts of Context on Shifts in Lifespan Development. K.F. Widaman, Intrauterine Environment Affects Infant and Child Intellectual Outcomes: Environment as Direct Effect. H. Jelicic, C. Theokas, E. Phelps, R.M. Lerner, Conceptualizing and Measuring the Context Within Person Context Models of Human Development: Implications for Theory, Research, and Application.Longitudinal studies are increasingly common in psychological and social sciences research. In these studies, subjects are measured repeatedly across time and interest often focuses on characterizing their growth or development across time. Mixed-effects regression models (MRMs) have become the method of choice for modeling of longitudinal data; variants of MRMs have been developed under a variety of names: Random-effects models. Laird and Ware (1982),variance component models (Dempster, Rubin, & Tsutakawa, 1981) , multilevel models (Goldstein, 1995), hierarchical linear models (Bryk & Raudenbush, 1992), two-stage models. Bock (1989), random coefficient models (Leeuw & Kreft, 1986), mixed models (Longford, 1987; Wolfinger, 1993), empirical Bayes models (Hui & Berger, 1983; Strenio, Weisberg, & Bryk, 1983), and random regression models (Bock, 1983b, 1983a; Gibbons, Hedeker, Waternaux, & Davis, 1988). A basic characteristic of these models is the inclusion of random subject effects into regression models in order to account for the influence of subjects on their repeated observations. These random effects reflect each person’s growth or development across time, and explain the correlational structure of the longitudinal data. Additionally, they indicate the degree of subject variation that exists in the population of subjects. There are several features that make MRMs especially useful in longitudinal research. First, subjects are not assumed to be measured on the same number of timepoints, thus, subjects with incomplete data across time are included in the

Development of the Contact Lens User Experience: CLUE Scales.

Purpose The field of optometry has become increasingly interested in patient-reported outcomes, reflecting a common trend occurring across the spectrum of healthcare. This article reviews the development of the Contact Lens User Experience: CLUE system designed to assess patient evaluations of contact lenses. CLUE was built using modern psychometric methods such as factor analysis and item response theory. Methods The qualitative process through which relevant domains were identified is outlined as well as the process of creating initial item banks. Psychometric analyses were conducted on the initial item banks and refinements were made to the domains and items. Following this data-driven refinement phase, a second round of data was collected to further refine the items and obtain final item response theory item parameters estimates. Results Extensive qualitative work identified three key areas patients consider important when describing their experience with contact lenses. Based on item content and psychometric dimensionality assessments, the developing CLUE instruments were ultimately focused around four domains: comfort, vision, handling, and packaging. Item response theory parameters were estimated for the CLUE item banks (377 items), and the resulting scales were found to provide precise and reliable assignment of scores detailing users’ subjective experiences with contact lenses. Conclusions The CLUE family of instruments, as it currently exists, exhibits excellent psychometric properties.

A review of empirical research related to the use of small quantitative samples in clinical outcome scale development

IntroductionThere has been a notable increase in the advocacy of using small-sample designs as an initial quantitative assessment of item and scale performance during the scale development process. This is particularly true in the development of clinical outcome assessments (COAs), where Rasch analysis has been advanced as an appropriate statistical tool for evaluating the developing COAs using a small sample.MethodsWe review the benefits such methods are purported to offer from both a practical and statistical standpoint and detail several problematic areas, including both practical and statistical theory concerns, with respect to the use of quantitative methods, including Rasch-consistent methods, with small samples.ConclusionsThe feasibility of obtaining accurate information and the potential negative impacts of misusing large-sample statistical methods with small samples during COA development are discussed.

Fit for purpose and modern validity theory in clinical outcomes assessment

PurposeThe US Food and Drug Administration (FDA), as part of its regulatory mission, is charged with determining whether a clinical outcome assessment (COA) is “fit for purpose” when used in clinical trials to support drug approval and product labeling. In this paper, we will provide a review (and some commentary) on the current state of affairs in COA development/evaluation/use with a focus on one aspect: How do you know you are measuring the right thing? In the psychometric literature, this concept is referred to broadly as validity and has itself evolved over many years of research and application.ReviewAfter a brief introduction, the first section will review current ideas about “fit for purpose” and how it has been viewed by FDA. This section will also describe some of the unique challenges to COA development/evaluation/use in the clinical trials space. Following this, we provide an overview of modern validity theory as it is currently understood in the psychometric tradition. This overview will focus primarily on the perspective of validity theorists such as Messick and Kane whose work forms the backbone for the bulk of high-stakes assessment in areas such as education, psychology, and health outcomes.ConclusionsWe situate the concept of fit for purpose within the broader context of validity. By comparing and contrasting the approaches and the situations where they have traditionally been applied, we identify areas of conceptual overlap as well as areas where more discussion and research are needed.

Measurement invariance, the lack thereof, and modeling change

PurposeMeasurement invariance issues should be considered during test construction. In this paper, we provide a conceptual overview of measurement invariance and describe how the concept is implemented in several different statistical approaches. Typical applications look for invariance over things such as mode of administration (paper and pencil vs. computer based), language/translation, age, time, and gender, to cite just a few examples. To the extent that the relationships between items and constructs are stable/invariant, we can be more confident in score interpretations.MethodsA series of simulated examples are reported which highlight different kinds of non-invariance, the impact it can have, and the effect of appropriately modeling a lack of invariance. One example focuses on the longitudinal context, where measurement invariance is critical to understanding trends over time. Software syntax is provided to help researchers apply these models with their own data.ResultsThe simulation studies demonstrate the negative impact an erroneous assumption of invariance may have on scores and substantive conclusions drawn from naively analyzing those scores.ConclusionsMeasurement invariance implies that the links between the items and the construct of interest are invariant over some domain, grouping, or classification. Examining a new or existing test for measurement invariance should be part of any test construction/implementation plan. In addition to reviewing implications of the simulation study results, we also provide a discussion of the limitations of current approaches and areas in need of additional research.

Scale development with small samples: a new application of longitudinal item response theory

PurposeMeasurement development in hard-to-reach populations can pose methodological challenges. Item response theory (IRT) is a useful statistical tool, but often requires large samples. We describe the use of longitudinal IRT models as a pragmatic approach to instrument development when large samples are not feasible.MethodsThe statistical foundations and practical benefits of longitudinal IRT models are briefly described. Results from a simulation study are reported to demonstrate the model’s ability to recover the generating measurement structure and parameters using a range of sample sizes, number of items, and number of time points. An example using early-phase clinical trial data in a rare condition demonstrates these methods in practice.ResultsSimulation study results demonstrate that the longitudinal IRT model’s ability to recover the generating parameters rests largely on the interaction between sample size and the number of time points. Overall, the model performs well even in small samples provided a sufficient number of time points are available. The clinical trial data example demonstrates that by using conditional, longitudinal IRT models researchers can obtain stable estimates of psychometric characteristics from samples typically considered too small for rigorous psychometric modeling.ConclusionCapitalizing on repeated measurements, it is possible to estimate psychometric characteristics for an assessment even when sample size is small. This allows researchers to optimize study designs and have increased confidence in subsequent comparisons using scores obtained from such models. While there are limitations and caveats to consider when using these models, longitudinal IRT modeling may be especially beneficial when developing measures for rare conditions and diseases in difficult-to-reach populations.