Dataset Information

Integrating prospective longitudinal data: modeling personality and health in the Terman Life Cycle and Hawaii Longitudinal Studies.

ABSTRACT: The present study used a collaborative framework to integrate 2 long-term prospective studies: the Terman Life Cycle Study and the Hawaii Personality and Health Longitudinal Study. Within a 5-factor personality-trait framework, teacher assessments of child personality were rationally and empirically aligned to establish similar factor structures across samples. Comparable items related to adult self-rated health, education, and alcohol use were harmonized, and data were pooled on harmonized items. A structural model was estimated as a multigroup analysis. Harmonized child personality factors were then used to examine markers of physiological dysfunction in the Hawaii sample and mortality risk in the Terman sample. Harmonized conscientiousness predicted less physiological dysfunction in the Hawaii sample and lower mortality risk in the Terman sample. These results illustrate how collaborative, integrative work with multiple samples offers the exciting possibility that samples from different cohorts and ages can be linked together to directly test life span theories of personality and health.

SUBMITTER: Kern ML

PROVIDER: S-EPMC3758911 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Integrating prospective longitudinal data: modeling personality and health in the Terman Life Cycle and Hawaii Longitudinal Studies.

Kern Margaret L ML Hampson Sarah E SE Goldberg Lewis R LR Friedman Howard S HS

Developmental psychology 20121210 5

The present study used a collaborative framework to integrate 2 long-term prospective studies: the Terman Life Cycle Study and the Hawaii Personality and Health Longitudinal Study. Within a 5-factor personality-trait framework, teacher assessments of child personality were rationally and empirically aligned to establish similar factor structures across samples. Comparable items related to adult self-rated health, education, and alcohol use were harmonized, and data were pooled on harmonized item ...[more]

PMID: 23231689

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Project description:BACKGROUND:Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington's disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis. METHODS:Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status. RESULTS:The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC?=?.83 (range .77-.90), and the 10-year mean AUC?=?.86 (range .82-.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington's disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes. CONCLUSIONS:Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.

Project description:PurposeThis study seeks to provide a framework for integrating animal welfare as a fourth pillar into a life cycle sustainability assessment and presents three alternative animal welfare indicators.MethodsAnimal welfare is assessed during farm life and during slaughter. The indicators differ in how they value premature death. All three consider (1) the life quality of an animal such as space allowance, (2) the slaughter age either as life duration or life fraction, and (3) the number of animals affected for providing a product unit, e.g. 1 Mcal. One of the indicators additionally takes into account a moral value denoting their intelligence and self-awareness. The framework allows for comparisons across studies and products and for applications at large spatial scales. To illustrate the framework, eight products were analysed and compared: beef, pork, poultry, milk, eggs, salmon, shrimps, and, as a novel protein source, insects.Results and discussionInsects are granted to live longer fractions of their normal life spans, and their life quality is less compromised due to a lower assumed sentience. Still, they perform worst according to all three indicators, as their small body sizes only yield low product quantities. Therefore, we discourage from eating insects. In contrast, milk is the product that reduces animal welfare the least according to two of the three indicators and it performs relatively better than other animal products in most categories. The difference in animal welfare is mostly larger for different animal products than for different production systems of the same product. This implies that, besides less consumption of animal-based products, a shift to other animal products can significantly improve animal welfare.ConclusionsWhile the animal welfare assessment is simplified, it allows for a direct integration into life cycle sustainability assessment. There is a trade-off between applicability and indicator complexity, but even a simple estimate of animal welfare is much better than ignoring the issue, as is the common practice in life cycle sustainability assessments. Future research should be directed towards elaborating the life quality criterion and extending the product coverage.

Dataset Information

Integrating prospective longitudinal data: modeling personality and health in the Terman Life Cycle and Hawaii Longitudinal Studies.

Publications

Integrating prospective longitudinal data: modeling personality and health in the Terman Life Cycle and Hawaii Longitudinal Studies.

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