Biomedical subjects
Publications and source records attributed to John J McArdle.
Despite the proliferation of longitudinal trauma research, careful attention to timing of assessments is often lacking. Patterns in timing of assessments, alternative time structures, and the treatment of time as an outcome are discussed and illustrated using trauma data.
In this article, the authors introduce a latent difference score (LDS) approach to analyzing longitudinal data in trauma research. The LDS approach accounts for internal sources of change in an outcome variable, including the influence of prior status on subsequent levels of that variable and the tendency for individuals to experience natural change (e.g., a natural decrease in posttraumatic stress disorder [PTSD] symptoms over time). Under traditional model assumptions, the LDSs are maximally reliable and therefore less likely to introduce biases into model testing. The authors illustrate the method using a sample of children who experienced significant burns or other injuries to examine potential influences (i.e., age of child-adolescent at time of trauma and ongoing family strains) on PTSD symptom severity over time.
OBJECTIVE: The purpose of this study was (a) to investigate whether attrition due to death and nonresponse leads to bias in estimated growth-decline trajectories when only complete data are used in longitudinal research, and (b) to examine the extent of the bias and possible solutions. METHODS: The study sample was a subset of the Longitudinal Study of Generations and included data from 208 G1-G2 parent-child dyads and 538 G2-G3 dyads over 30 years. We used a latent growth-decline curve model based on full information maximum likelihood estimation in order to compare parents' and adult children's reports on older respondents' health and intergenerational solidarity by parents' attrition status. RESULT: Results indicated that attrition due to mortality biased estimates of respondents' assessments of their functional health status over time, and parents' perceptions of the quality of the parent-child relationship deteriorated more rapidly among those who died by Time 7, but nonresponse did not seriously bias estimates of these measures. Using proxies, we found that functional impairment increased more rapidly when children reported about parents, especially in advanced old age. DISCUSSION: These results support the use of full information in estimating growth curves where mortality is present but raise concerns when using child proxies to evaluate parental health or the quality of intergenerational relationships.
OBJECTIVES: The prevalence rates of disorders among a community-based sample of Hawaiian youths were determined and compared to previously published epidemiological studies. METHOD: Using a two-phase design, 7,317 adolescents were surveyed (60% participation rate), from which 619 were selected in a modified random sample during the 1992-1993 to 1995-1996 school years: 590 selected randomly and 29 at risk (i.e., Center for Epidemiologic Studies-Depression score of >or=35 and suicidal risk) from grades 9-12. The Diagnostic Interview Schedule for Children-Version 2.3, was used to determine DSM-III-R diagnoses. Prevalence rates, weighted for ethnicity, Center for Epidemiologic Studies-Depression scores, and suicide attempts, were calculated for any diagnosis and various disorders. Meta-analyses compared the Hawai'i sample to four community-based studies (randomly selected youths from community populations) and two high-risk studies (homeless, low-income, or high unemployment communities). RESULTS: Hawaiian females had the highest rate for any diagnosis (37.7%; 95% confidence interval [CI] 28.4%-48.0%) and non-Hawaiian males had the lowest rate (19.6%; 95% CI 14.8%-25.5%). Hawaiian males (26.8%; 95% CI 18.2%-37.5%) and non-Hawaiian females (27.9%; 95% CI 22.2%-34.4%) had intermediate and comparable rates. Overall, Hawaiians had significantly higher rates (32.7%; 95% CI 26.1%-40.1%) than non-Hawaiians (23.7%; 95% CI 19.9%-28.0%) when controlling for gender, and girls had significantly higher rates (30.8%; 95% CI 25.8%-36.3%) than boys (21.1%; 95% CI 16.8%-26.1%) when controlling for ethnicity. These findings were primarily the result of the significant differences in rates regarding anxiety disorders. Meta-analyses showed the Hawaiian youth rate for any diagnosis was comparable to high-risk studies and nearly three times higher than the community studies. CONCLUSIONS: Hawaiian youths, especially females, are at high risk. Research on the sociocultural factors that underpin both the genesis of and protection from psychopathology is imperative for Hawaiian and non-Hawaiian mixed-ethnicity youths.
In a recent article McArdle and Prescott (2005) showed how simultaneous estimation of the biometric parameters can be easily programmed using current mixed-effects modeling programs (e.g., SAS PROC MIXED). This article extends these concepts to deal with mixed-effect modeling of longitudinal twin data. The biometric basis of a polynomial growth curve model was used by Vandenberg and Falkner (1965) and this general class of longitudinal models was represented in structural equation form as a latent curve model by McArdle (1986). The new mixed-effects modeling approach presented here makes it easy to analyze longitudinal growth-decline models with biometric components based on standard maximum likelihood estimation and standard indices of goodness-of-fit (i.e., chi(2), df, epsilon(a)). The validity of this approach is first checked by the creation of simulated longitudinal twin data followed by numerical analysis using different computer programs (i.e., Mplus, Mx, MIXED, NLMIXED). The practical utility of this approach is examined through the application of these techniques to real longitudinal data from the Swedish Adoption/Twin Study of Aging (Pedersen et al., 2002). This approach generally allows researchers to explore the genetic and nongenetic basis of the latent status and latent changes in longitudinal scores in the absence of measurement error. These results show the mixed-effects approach easily accounts for complex patterns of incomplete longitudinal or twin pair data. The results also show this approach easily allows a variety of complex latent basis curves, such as the use of age-at-testing instead of wave-of-testing. Natural extensions of this mixed-effects longitudinal approach include more intensive studies of the available data, the analysis of categorical longitudinal data, and mixtures of latent growth-survival/frailty models.
Recent substantive research on biometric analyses of twin and family data has used both a biometric path analysis model (PAM) and a biometric variance components model (VCM). Methodological research on these same topics have suggested benefits of using linear structural equation model algorithms (SEMA) as well as mixed effect multilevel algorithms (MEMA). To better understand the potential similarities and differences among these approaches we first highlight the algebraic equivalence between the standard biometric PAM and the corresponding biometric VCM models for family data. Second, we demonstrate how several SEMA programs based on either the PAM or VCM approach produce equivalent estimates for all phenotypic and biometric parameters. Third, we show how the biometric VCM approach (but not the PAM approach) can be easily programmed using current MEMA programs (e.g., SAS PROC MIXED). We then expand the scope of these different approaches to include measured covariates, observed variable interactions and multiple relatives within each family. MEMA software is compared to SEMA software for programming complex models, including the flexibility of data input, treatment of missing data, inclusion of covariates, and ease of accommodating varying numbers of observations (per family or individual).
Goals of the present study were to investigate the relationship between age changes in speed and cognition and the genetic and environmental influences on that relationship. Latent growth models and quantitative genetic methods were applied to data from the Swedish Adoption/Twin Study of Aging. The sample included 778 individuals from both complete and incomplete twin pairs who participated in at least 1 of 4 testing occasions over a 13-year-period. Four factors were constructed from 11 cognitive measures: verbal, spatial, memory, and processing speed. Results indicate that for measures of fluid abilities, the explanatory value of processing speed is paramount for both mean cognitive performance and acceleration with age. A significant proportion of the genetic influences on cognitive ability arose from genetic factors affecting processing speed. For measures of fluid abilities, it is not the linear age changes but the accelerating age changes in cognition that share genetic variance with processing speed.
Though many cognitive abilities exhibit marked decline over the adult years, individual differences in rates of change have been observed. In the current study, biometrical latent growth models were used to examine sources of variability for ability level (intercept) and change (linear and quadratic effects) for verbal, fluid, memory, and perceptual speed abilities in the Swedish Adoption/Twin Study of Aging. Genetic influences were more important for ability level at age 65 and quadratic change than for linear slope at age 65. Expected variance components indicated decreasing genetic and increasing nonshared environmental variation over age. Exceptions included one verbal and two memory measures that showed increasing genetic and nonshared environmental variance. The present findings provide support for theories of the increasing influence of the environment with age on cognitive abilities.
Longitudinal multivariate mixed models were used to examine the correlates of change between memory and processing speed and the contribution of age and retest to such change correlates. Various age- and occasion-mixed models were fitted to 2 longitudinal data sets of adult individuals (N>1,200). For both data sets, the results indicated that the correlation between the age slopes of memory and processing speed decreased when retest effects were included in the model. If retest effects existed in the data but were not modeled, the correlation between the age slopes was positively biased. The authors suggest that although the changes in memory and processing speed may be correlated over time, age alone does not capture such a covariation.
Vocabulary knowledge may not be a unidimensional construct, and the relations between vocabulary knowledge and age may depend on the aspect of vocabulary knowledge being assessed. In this study, we examined the factor structure of a vocabulary test given to a large nationally representative sample of individuals (N approximately 20,500). Results indicated that the vocabulary test is not unidimensional but bidimensional, with Basic Vocabulary and Advanced Vocabulary factors. An analysis of age differences indicates that basic vocabulary is highest around the age of 30, with a negative relation to age in late adulthood; in contrast, advanced vocabulary is unrelated to age between ages 35 and 70. Cohort effects may explain some of the differential age trend.
This article explores new statistical methodologies for using longitudinal data in the early prediction of Alzheimer's disease (AD). Specifically, the authors examine some new techniques that allow the joint or "shared" estimation of longitudinal components based on both duration (survival) and quantitative changes (growth curves). These new shared growth-survival parameter models may be used to characterize the declining functions that anticipate the onset of AD. The authors apply these models to data from the Kungsholmen Project, a longitudinal study of aging in Stockholm, Sweden. They examine age-based survival-frailty models for the onset of AD, latent growth-decline curve models for changes in cognition over age, and 3 alternative forms of models for the shared relationships of survival and early cognitive decline. The accuracy and reliability of this approach is considered for a better understanding of the developmental course of AD in these data, including the potential removal of biases due to subject selection.
This study examined the dynamics of cognitive abilities and academic achievement from childhood to early adulthood. Predictions about time-dependent "coupling" relations between cognition and achievement based on R. B. Cattell's (1971, 1987) investment hypothesis were evaluated using linear dynamic models applied to longitudinal data (N=672). Contrary to Cattell's hypothesis, a first set of findings indicated that fluid and crystallized abilities, as defined by the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R; R. W. Woodcock & M. B. Johnson, 1989-1990), were not dynamically coupled with each other over time. A second set of findings provided support for the original predictions and indicated that fluid ability was a leading indicator of changes in achievement measures (i.e., quantitative ability and general academic knowledge). The findings of this study suggest that the dynamics of cognitive abilities and academic achievement follow a more complex pattern than that specified by Cattell's investment hypothesis.
This is an application of new longitudinal structural equation modeling techniques to time-dependent associations of memory and brain structure measurements. There were 225 participants aged 30-80 years at baseline who were measured again after a 7-year interval on both the lateral ventricular size and Wechsler memory score. Multiple regression analyses show nonlinear associations with age but no relationships among longitudinal changes. Mixed-effects latent growth curve analyses and analyses based on latent difference scores indicate that longitudinal changes in both variables are reasonably well described by an exponential or dual change model. Bivariate dynamic structural equation modeling analyses indicate age-lagged changes operate in a coupled-over-time fashion, with the brain measure (lateral ventricular size) as a leading indicator in time of memory (Wechsler memory score) declines.
A great deal of prior research using structural equation models has focused on longitudinal analyses and biometric analyses. Some of this research has even considered the simultaneous analysis of both kinds of analytic problems. The key benefits of these kinds of analyses come from the estimation of novel parameters, such as the heritability of changes. This paper discusses some recent extensions of longitudinal multivariate models that can be informative within biometric designs. In the methods section we review a previous latent growth structural equation analysis of the New York Twin (NYT) longitudinal data (from McArdle et al., 1998). In the models section we recast this growth model in terms of latent difference scores, add several new dynamic components, including coupling parameters, and consider biometric components and examine model stability. In the results section we present new univariate and bivariate dynamic estimates and tests of various dynamic hypotheses for the NYT data, and we consider a few ways to interpret the age-related biometric components of these models. In the discussion we consider our limitations and present suggestions for future dynamic-genetic research.
Latent growth models were applied to data from the Swedish Adoption/Twin Study of Aging to discover if the rate of change in cognitive performance increased from middle age to later adulthood. The sample included 590 participants aged 44 to 88 years at first measurement. Data were gathered at 2 follow-up occasions at intervals of 3 years. Cognitive ability was assessed through 11 tests that tapped crystallized, fluid, memory, and spatial abilities and perceptual speed. Results indicated stability for measures of crystallized ability, linear age changes for many cognitive abilities, and a significant acceleration in linear decline after age 65 for measures with a large speed component. Gender differences were found only in mean level, not in rate of decline.
Latent growth curve techniques and longitudinal data are used to examine predictions from the theory of fluid and crystallized intelligence (Gf-Gc theory; J. L. Horn & R. B. Cattell, 1966, 1967). The data examined are from a sample (N approximately 1,200) measured on the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R). The longitudinal structural equation models used are based on latent growth models of age using two-occasion "accelerated" data (e.g., J. J. McArdle & R. Q. Bell, 2000; J. J. McArdle & R. W. Woodcock, 1997). Nonlinear mixed-effects growth models based on a dual exponential rate yield a reasonable fit to all life span cognitive data. These results suggest that most broad cognitive functions fit a generalized curve that rises and falls. Novel multilevel models directly comparing growth curves show that broad fluid reasoning (Gf) and acculturated crystallized knowledge (Gc) have different growth patterns. In all comparisons, any model of cognitive age changes with only a single g factor yields an overly simplistic view of growth and change over age.