Biomedical subjects

Patrick J Curran

Publications and source records attributed to Patrick J Curran.

11 recordsLinked to original sources

PubMed2006

Structure-taste relationships for disubstituted phenylsulfamate tastants using classification and regression tree (CART) analysis.

Forty-two new disubstituted phenylsulfamates have been synthesized, and 30 of these have been combined with 40 already available from earlier work to create a training database of 70 compounds. On the basis of panel taste data these were divided into three categories, N (nonsweet), N/S (nonsweet/sweet), and (S) sweet, and a "sweetness value" or weighting was also calculated for each compound. Using these 70 compounds as a training set and a series of nine predictors derived from Corey-Pauling-Koltun (CPK) models, calculated from the PC SPARTAN PRO program and Hammett sigma values taken from the literature, a classification and regression tree analysis (CART) was carried out leading to a regression tree that correctly classified 62 of the 70 compounds (89% overall correct classification). The tree's predictive ability varies for the different taste categories, and for nonsweet compounds it is virtually 100%; for nonsweet/sweet compounds it is 66%, and for sweet compounds it is approximately 75%. This tree correctly predicted taste categories for 10 compounds from a test set of 12 randomly selected from among the 42 new compounds (83% correct classification). Therefore, it can be used with a good degree of confidence to predict the tastes of disubstituted phenylsulfamates. For the design of new sweeteners, appropriate values or ranges of the descriptors are derived.

Cyclamates↗

PubMed2005

Aminothiazole inhibitors of HCV RNA polymerase.

Aminothiazole-based inhibitors designed for HCV polymerase display low micromolar potencies in biochemical assays. These compounds show a stringent preference for a cyclohexyl hydrophobe at the 2-amino position. The composition of these compounds suggests that they may be interacting at a recently discovered allosteric site on the polymerase.

DNA-Directed RNA Polymerases↗

PubMed2004

Substance abuse hinders desistance in young adults' antisocial behavior.

We examined two hypotheses about the developmental relation between substance abuse and individual differences in desistance from antisocial behavior during young adulthood. The "snares" hypothesis posits that substance abuse should result in time-specific elevations in antisocial behavior relative to an individual's own developmental trajectory of antisocial behavior, whereas the "launch" hypothesis posits that substance abuse early in young adulthood slows an individual's overall pattern of crime desistance relative to the population norm during this developmental period. We conducted latent trajectory analyses to test these hypotheses using interview data about antisocial behaviors and substance abuse assessed at ages 18, 21, and 26 in men from the Dunedin Multidisciplinary Health and Development Study (N = 461). We found significant individual variability in initial levels and rates of change in antisocial behavior over time as well as support for both the snares hypothesis and the launch hypothesis as explanations for the developmental relation between substance abuse and crime desistance in young men.

Adolescent↗

PubMed2004

The integration of continuous and discrete latent variable models: potential problems and promising opportunities.

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model, nonnormal continuous measures, and nonlinear relationships among observed and/or latent variables. When the objective of a SEMM analysis is the identification of latent classes, these conditions should be considered as alternative hypotheses and results should be interpreted cautiously. However, armed with greater knowledge about the estimation of SEMMs in practice, researchers can exploit the flexibility of the model to gain a fuller understanding of the phenomenon under study.

Factor Analysis, Statistical↗

PubMed2004

The role of coding time in estimating and interpreting growth curve models.

The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of receding time in growth curve models. Linear and quadratic growth model examples are provided, and the interpretation of estimates given a particular coding of time is illustrated. How and why the precision and statistical power of predictors of lower order growth curve components changes over time is illustrated and discussed. Recommendations include coding time to produce readily interpretable estimates and graphing lower order effects across time with appropriate confidence intervals to help illustrate and understand the growth process.

Child↗

PubMed2004

Testing main effects and interactions in latent curve analysis.

A key strength of latent curve analysis (LCA) is the ability to model individual variability in rates of change as a function of 1 or more explanatory variables. The measurement of time plays a critical role because the explanatory variables multiplicatively interact with time in the prediction of the repeated measures. However, this interaction is not typically capitalized on in LCA because the measure of time is rather subtly incorporated via the factor loading matrix. The authors' goal is to demonstrate both analytically and empirically that classic techniques for probing interactions in multiple regression can be generalized to LCA. A worked example is presented, and the use of these techniques is recommended whenever estimating conditional LCAs in practice.

Humans↗

PubMed2004

An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data.

Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not well-known. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions.

Factor Analysis, Statistical↗

PubMed2003

Implications of latent trajectory models for the study of developmental psychopathology.

The field of developmental psychopathology is faced with a dual challenge. On the one hand, we must build interdisciplinary theoretical models that adequately reflect the complexity of normal and abnormal human development over time. On the other hand, to remain a viable empirical science, we must rigorously evaluate these theories using statistical methods that fully capture this complexity. The degree to which our statistical models fail to correspond to our theoretical models undermines our ability to validly test developmental theory. The broad class of random coefficient trajectory (or growth curve) models allow us to test our theories in ways not previously possible. Despite these advantages, there remain certain limits with regard to the types of questions these models can currently evaluate. We explore these issues through the pursuit of three goals. First, we provide an overview of a variety of trajectory models that can be used for rigorously testing many hypotheses in developmental psychopathology. Second, we highlight what types of research questions are well tested using these methods and what types of questions currently are not. Third, we describe areas for future statistical development and encourage the ongoing interchange between developmental theory and quantitative methodology.

Child↗

PubMed2003

The use of latent trajectory models in psychopathology research.

Despite the recent surge in the development of powerful modeling strategies to test questions about individual differences in stability and change over time, these methods are not currently widely used in psychopathology research. In an attempt to further the dissemination of these new methods, the authors present a pedagogical introduction to the structural equation modeling based latent trajectory model, or LTM. They review several different types of LTMs, discuss matching an optimal LTM to a given question of interest, and highlight several issues that might be particularly salient for research in psychopathology. The authors augment each section with a review of published applications of these methods in psychopathology-related research to demonstrate the implementation and interpretation of LTMs in practice.

Data Interpretation, Statistical↗

PubMed2003

Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes.

Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only 1 group exists in the population. Further, the within-class parameter estimates obtained from these models are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested.

Group Processes↗

PubMed2002

Child, parent, and peer predictors of early-onset substance use: a multisite longitudinal study.

The purpose of this study was to identify kindergarten-age predictors of early-onset substance use from demographic, environmental, parenting, child psychological, behavioral, and social functioning domains. Data from a longitudinal study of 295 children were gathered using multiple-assessment methods and multiple informants in kindergarten and 1st grade. Annual assessments at ages 10, 11, and 12 reflected that 21% of children reported having initiated substance use by age 12. Results from longitudinal logistic regression models indicated that risk factors at kindergarten include being male, having a parent who abused substances, lower levels of parental verbal reasoning, higher levels of overactivity, more thought problems, and more social problem solving skills deficits. Children with no risk factors had less than a 10% chance of initiating substance use by age 12, whereas children with 2 or more risk factors had greater than a 50% chance of initiating substance use. Implications for typology, etiology, and prevention are discussed.

Adolescent↗