Biomedical subjects

C Hendricks Brown

Publications and source records attributed to C Hendricks Brown.

7 recordsLinked to original sources

PubMed2006

Dynamic wait-listed designs for randomized trials: new designs for prevention of youth suicide.

BACKGROUND: The traditional wait-listed design, where half are randomly assigned to receive the intervention early and half are randomly assigned to receive it later, is often acceptable to communities who would not be comfortable with a no-treatment group. As such this traditional wait-listed design provides an excellent opportunity to evaluate short-term impact of an intervention. We introduce a new class of wait-listed designs for conducting randomized experiments where all subjects receive the intervention, and the timing of the intervention is randomly assigned. We use the term "dynamic wait-listed designs" to describe this new class. PURPOSE: This paper examines a new class of statistical designs where random assignment to intervention condition occurs at multiple times in a trial. As an extension of a traditional wait-listed design, this dynamic design allows all subjects to receive the intervention at a random time. Motivated by our search for increased statistical power in an ongoing school-based trial that is testing a program of gatekeeper training to identify suicidal youth and refer them to treatment, this new design class is especially useful when the primary outcome is a count or rate of occurrence, such as suicidal behavior, whose rate can fluctuate over time due to uncontrolled factors. METHODS: Statistical power is computed for various dynamic wait-listed designs under conditions where the underlying rate of occurrence is allowed to vary nonsystematically. We also present as an example a large ongoing trial to evaluate a gatekeeper training suicide prevention program in 32 schools which we initially began as a classic randomized wait-listed design. The primary outcome of interest in this study is the count of the number of children who are identified by the school system as having suicidal thoughts or behaviors who are then validated as being suicidal by mental health professionals in the community. RESULTS: A general result shows that dynamic wait-listed designs always have higher statistical power over a traditional wait-listed design. This power increase can be substantial. Efficiency gains of 33% are easy to obtain for situations where the intervention has a small effect and the variation in rate across time is quite high. When the rate variation for an outcome is very low or the intervention effect is large, efficiency gains approach 100%. A small increase in the number of times where random assignment occurs from 2 - for the standard wait-listed design, to say 4 can provide a large reduction in variance. Efficiency gains can also be high when converting standard wait-listed design to a dynamic one half-way into the study. LIMITATIONS: As with all wait-listed designs, dynamic wait-listed designs can only be used to evaluate short-term impact. Since all subjects eventually receive the intervention, no comparison can be made after the end of the random assignment period. The statistical power benefits are primarily limited to outcomes that can be treated as count or time to event data. CONCLUSIONS: A dynamic design randomly assigns units - either individuals or groups - to start the intervention at varying times during the course of the study. This design is useful in testing interventions that screen for new or existing cases, as well as testing the scalability of interventions as they are disseminated or expanded system wide. They can improve on the traditional wait-listed design both in terms of statistical power and robustness in the presence of exogenous factors. This paper demonstrates that such designs yield smaller standard errors and can achieve higher statistical power than that of a standard wait-listed design. Just as important, dynamic designs can also help reduce the logistical challenges of implementing an intervention on a wide scale. When the intervention requires that significant training resources be allocated throughout the study, the dynamic wait-listed design is likely to increase the rate of training and lead to a higher level of program implementation.

Adolescent↗

PubMed2005

A survey of prevention science training: implications for educating the next generation.

Several reviews of the emerging, transdisciplinary field of prevention science have identified the need for improved and expanded training of researchers as one of the central issues facing the field. A starting place for such an endeavor is an assessment of the current state of training. In that regard, we queried several groups of researchers about training in the prevention of mental disorders and closely related areas. Training experts from federally funded prevention intervention research and training centers were interviewed regarding the content of existing and ideal prevention science training programs. Based on these interviews and a literature search, we identified 13 content areas for prevention training. Through an internet-based survey, we interviewed trainees, early career researchers, and established researchers on their knowledge of and training in these areas. There was no content area in which the majority of early career researchers had a high level of training or knowledge. In contrast, the majority of established researchers were highly knowledgeable about each of six content areas that have represented the "traditional" areas of training in prevention science for the past several decades. Early career researchers had particularly low levels of knowledge and training in the history of prevention research and practice, how to obtain funding for prevention research, and how to conduct economic analyses. Implications of the findings for the education of the next cohort of prevention researchers are discussed.

Adult↗

PubMed2005

Multilevel methods for modeling observed sequences of family interaction.

Observation of interaction plays a central role in family research. This article discusses how to analyze sequential data generated by discrete microcoding methods to test hypotheses about family interaction. Current methods for studying sequential data are presented, and their limits are discussed. Building on recent applications of contingency table analysis to such data, a multilevel log-linear model is presented that can specify and estimate indicators of individual behavioral tendencies and antecedent-consequent relationships among behaviors, both within and across samples of families. An example of this method is presented using data from a study of couples facing job loss. Potential extensions of this framework for future research are discussed.

Algorithms↗

PubMed2005

A brief experimental alcohol beverage-tailored program for adolescents.

OBJECTIVE: The objective of this study was to develop and test a brief, experimental alcohol preventive intervention matched to the use of specific alcohol beverages among adolescents. METHOD: A total of 232 high school students who drank within the last year participated in this study. Participants were randomly assigned to either the experimental intervention or a minimal intervention control. Four-month postintervention data are reported. RESULTS: Overall multivariate analyses of covariance (MANCOVAs) were significant (p's < .05) on risk factors (influenceability, perceived severity, perceived susceptibility and perceived peer prevalence) for three of six beverages (beer, wine and distilled spirits), with a fourth, malt liquor, approaching significance (p = .06). These tests showed intervention adolescents experiencing less risk for alcohol use than control adolescents. In addition, whereas the overall MANCOVA for malt liquor use was not significant, univariate analyses were significant for 30-day frequency (F = 5.69, 1/195 df, p = .01) and 30-day quantity of malt liquor use (F = 4.03, 1/195 df, p = .04) with intervention adolescents showing less consumption than control adolescents. A post hoc analysis examining differential intervention effects using preintervention drug use as a factor (i.e., 30-day cigarette or marijuana use) showed a significant overall factorial MANCOVA interaction (F = 6.90, 4/189 df, p = .000), with drug-using intervention adolescents consuming cigarettes and marijuana less frequently than drug-using control adolescents at postintervention. CONCLUSIONS: The findings of this study suggest the brief, beverage-tailored intervention reduced certain risk factors mediating individual alcohol beverage use and consumption of malt liquor (4 months after intervention) and may have reduced the frequency of cigarette and marijuana use among those already using drugs.

Adolescent↗

PubMed2002

Hierarchical modeling of sequential behavioral data: an empirical Bayesian approach.

The authors review the common methods for measuring strength of contingency between 2 behaviors in a behavioral sequence, the binomial z score and the adjusted cell residual, and point out a number of limitations of these approaches. They present a new approach using log odds ratios and empirical Bayes estimation in the context of hierarchical modeling, an approach not constrained by these limitations. A series of hierarchical models is presented to test the stationarity of behavioral sequences, the homogeneity of sequences across a sample of episodes, and whether covariates can account for variation in sequences across the sample. These models are applied to observational data taken from a study of the behavioral interactions of 254 couples to illustrate their use.

Bayes Theorem↗

PubMed2002

General growth mixture modeling for randomized preventive interventions.

This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finite-mixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identifies subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.

Journal Article↗

PubMed2002

Preventing schizophrenia and psychotic behaviour: definitions and methodological issues.

Although schizophrenia onset usually occurs in late adolescence or early adulthood, much research shows that its seeds are planted early in life and that eventual onset occurs at the end of a neurodevelopmental process leading to aberrant brain functioning. This idea, along with the fact that current therapies are far from fully effective, suggests that preventive treatments may be needed to achieve an ideal outcome for schizophrenia patients and those predisposed to the disorder. In this article, we review the methodological challenges that must be overcome before effective preventive interventions can be created. Prevention studies will need to define the target population. This requires the identification of risk factors that will be useful in selecting at-risk people for preventive treatment. We review currently identified risk factors for schizophrenia: genes, psychosocial factors, pregnancy and delivery complications, and viruses. We also review 3 different types of prevention programs: universal, indicated, and selective. For schizophrenia, we distinguish prevention programs that target prodromal cases and those that target the disorder's premorbid precursors. Although those targeting prodromal cases provide a useful framework for early treatment of the disorder, studies of premorbid individuals are needed to design a truly preventive treatment.

Affect↗