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Results for “longitudinal data”

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PubMed1986

Estimation of growth curves from longitudinal data collected at irregular time intervals.

A general procedure for fitting growth curves is proposed that can be applied to longitudinal data even if observations are missing or irregularly spaced. Maximum likelihood estimates for mean growths are obtained from an EM algorithm. Estimates for standard errors, percentiles, and growth velocities are also produced. The techniques are demonstrated through the use of growth data from a longitudinal study of sickle cell disease.

Algorithms↗

PubMed1996

Non-compliance in elderly people: evaluation of risk factors by longitudinal data analysis.

Studies on risk factors for drug non-compliance have not taken into account the possibility of correlated outcomes. We therefore conducted a study into risk factors for non-compliance by using analysis techniques that adjust for these correlations (longitudinal data analysis). Data were obtained from interviews and pharmacy records in a cross-sectional survey in Amsterdam. The subjects were 157 elderly people aged 70 years or older. Of these subjects, 37 were residents of a home for the elderly, 40 were community-dwelling elderly who needed to be visited regularly by a district nurse, and 80 were community-dwelling elderly who did not need to be visited by a district nurse. Most drugs (78%) were used according to the directions; the remainder (22%) were not used as intended. Odds ratios (95% confidence intervals) for non-compliance for moderate and poor/wrong knowledge of the purpose of a drug as compared with good/correct knowledge were 2.8 (1.2-6.7) and 4.2 (1.5-12), respectively. Drug regimens of two times daily and more than two times daily were associated with odds ratios for non-compliance of 4.5 (1.6-12) and 4.2 (1.7-11), respectively, compared to a regimen of once daily. Compliance increased if a drug was prescribed by a specialist instead of a general practitioner odds ratio 0.1 (0.04-0.4)]. There was no significant relation between compliance and the number of drugs prescribed to a patient, sex, age, living situation, patient group, or perceived effect. This study, which was based on longitudinal data analysis, demonstrates that in elderly people non-compliance with drug therapy is related to the knowledge of purpose of a drug, the complexity of a drug regimen, and the type of prescriber. The positive association between compliance and the number of drugs prescribed found in former studies was not confirmed.

Aged↗

PubMed1994

Introduction to special section: the role of longitudinal data with child psychopathology and treatment: preliminary comments and issues.

Longitudinal data can play an important role in child psychopathology and treatment. This article introduces a review of some of the research questions that longitudinal designs can answer and how longitudinal studies have been used in evaluating traditional syndromes in child clinical psychology. We then introduce the articles in this special section.

Child Behavior Disorders↗

PubMedDate not supplied

Approaches to the nonparametric analysis of limited longitudinal data sets.

The traditional goals of longitudinal studies are many: consideration of stability and change; description of patterns of development and behavior; and understanding of the processes involved in disease, including disease onset, recovery, response to treatment, natural history of the aging process, and identification of factors that predict age-related outcomes. Researchers in aging seek to unravel the impact and interaction of physical and psychological processes on human development, health, and disease. From the point of view of statistical analysis, the critical aspect of data obtained from longitudinal studies is the inherent correlational structure of multiple measurements made on a single subject or other experimental unit, which must be appropriately treated in the analysis of the data. We discuss a series of nonparametric approaches that are both analytically accessible and particularly well suited to the analysis of sparse or otherwise limited longitudinal data.

Humans↗

PubMed2007

Analysis of antiretroviral immunotherapy trials with potentially non-normal and incomplete longitudinal data.

For many HIV-infected patients, use of antiretroviral therapy (ART) results in a sustained suppression of plasma viral load to undetectable levels. However, due to lack of antigenic stimulation, this may also result in a gradual loss of cell-mediated immune (CMI) responses that help control HIV infection. In concept, augmenting ART with periodic administrations of an HIV vaccine that boosts CMI responses could enhance control of viral replication. Researchers are designing 'antiretroviral immunotherapy' (ARI) trials to test this hypothesis. In a typical ARI trial, HIV-infected patients with sustained viral suppression will receive inoculations of an experimental HIV vaccine or a placebo, and subsequently stop taking their antiretroviral drugs. The goal is to assess whether plasma viral loads during the ART interruption phase are generally lower in the vaccine group. Assessment of a vaccine effect will be challenging if some subjects resume ART or drop out before the end of the treatment interruption phase. To tackle this 'missing' data problem and potential non-normality of the viral loads in ARI trials, we propose a two-step approach: multiple imputation of the missing values followed by use of the Wei-Lachin method with Wilcoxon scores. We use a numerical example and extensive simulations to illustrate the robustness and power advantages of our proposed method compared with other methods for incomplete longitudinal data, including REML, weighted GEE, last observation carried forward, and 'worst-rank' methods. Our proposed method is general enough for the robust analysis of longitudinal data in other therapeutic areas as well.

AIDS Vaccines↗

PubMed2002

A copula-based model for multivariate non-normal longitudinal data: analysis of a dose titration safety study on a new antidepressant.

A new model for multivariate non-normal longitudinal data is proposed. In a first step, each longitudinal series of data corresponding to a given response is modelled separately using a copula to relate the marginal distributions of the response at each time of observation. In a second step, at each observation time, the conditional (on the past) distributions of each response are related using another copula describing the relationship between the corresponding variables. Note that there is no need to consider the same family of distributions for these response variables. The technique is illustrated in a dose titration safety study on a new antidepressant. The haemodynamic effect on diastolic blood pressure, systolic blood pressure and heart rate is studied. These three responses are measured repeatedly over time on ten healthy volunteers during the dose escalation. The available covariates are sex and the concentration of drug in the plasma at time of measurement.

Antidepressive Agents↗

PubMed1989

Statistical methods for the analysis of longitudinal data from school-based smoking prevention studies.

The features which make longitudinal data obtained from school-based smoking prevention studies well-suited for efficient analysis by survival analysis methods are discussed. Survival analysis methods, in particular relative risk regression models, are described and illustrated through an example involving data from the Waterloo Smoking Prevention Project--Study 1. Indications of some of the possible applications for these techniques in the evaluation of interventions to prevent smoking and the study of the smoking onset process are provided.

Adolescent↗

PubMed2003

Bayesian meta-analysis for longitudinal data models using multivariate mixture priors.

We propose a class of longitudinal data models with random effects that generalizes currently used models in two important ways. First, the random-effects model is a flexible mixture of multivariate normals, accommodating population heterogeneity, outliers, and nonlinearity in the regression on subject-specific covariates. Second, the model includes a hierarchical extension to allow for meta-analysis over related studies. The random-effects distributions are decomposed into one part that is common across all related studies (common measure), and one part that is specific to each study and that captures the variability intrinsic between patients within the same study. Both the common measure and the study-specific measures are parameterized as mixture-of-normals models. We carry out inference using reversible jump posterior simulation to allow a random number of terms in the mixtures. The sampler takes advantage of the small number of entertained models. The motivating application is the analysis of two studies carried out by the Cancer and Leukemia Group B (CALGB). In both studies, we record for each patient white blood cell counts (WBC) over time to characterize the toxic effects of treatment. The WBCs are modeled through a nonlinear hierarchical model that gathers the information from both studies.

Bayes Theorem↗

PubMed2005

Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deaths.

For observational longitudinal studies of geriatric populations, outcomes such as disability or cognitive functioning are often censored by death. Statistical analysis of such data may explicitly condition on either vital status or survival time when summarizing the longitudinal response. For example a pattern-mixture model characterizes the mean response at time t conditional on death at time S = s (for s > t), and thus uses future status as a predictor for the time t response. As an alternative, we define regression conditioning on being alive as a regression model that conditions on survival status, rather than a specific survival time. Such models may be referred to as partly conditional since the mean at time t is specified conditional on being alive (S > t), rather than using finer stratification (S = s for s > t). We show that naive use of standard likelihood-based longitudinal methods and generalized estimating equations with non-independence weights may lead to biased estimation of the partly conditional mean model. We develop a taxonomy for accommodation of both dropout and death, and describe estimation for binary longitudinal data that applies selection weights to estimating equations with independence working correlation. Simulation studies and an analysis of monthly disability status illustrate potential bias in regression methods that do not explicitly condition on survival.

Activities of Daily Living↗

PubMed2006

Linkage analysis of longitudinal data and design consideration.

BACKGROUND: Statistical methods have been proposed recently to analyze longitudinal data in genetic studies. So far, little attention has been paid to examine the relationship among key factors in genetic longitudinal studies including power, the number of families or sibships, and the number of repeated measures per individual subjects. RESULTS: We proposed a variance component model that extends classic variance component models for a single quantitative trait to mapping longitudinal traits. Our model includes covariate effects and allows genetic effects to vary over time. Using our proposed model, we examined the power, pedigree structures, and sample size through simulation experiments. CONCLUSION: Our simulation results provide useful insights into the study design for genetic, longitudinal studies. For example, collecting a small number of large sibships is much more powerful than collecting a large number of small sibships or increasing the number of repeated measures, when the total number of measurements is comparable.

Computer Simulation↗

PubMed2003

Transformations of covariates for longitudinal data.

This paper develops a general approach for dealing with parametric transformations of covariates for longitudinal data, where the responses are modeled marginally and generalized estimating equations (GEEs) are used for estimation of regression parameters. We propose an iterative algorithm for obtaining regression and transformation parameters from estimating equations, utilizing existing software for GEE problems. The algorithmic technique is closely related to that used in the Box-Tidwell transformation in classical linear regression, but we develop it under the GEE setting and for more general transformation functions. We provide supporting theorems for consistency and asymptotic Normality of the estimates. Inference between two nested models is also considered. This methodology is applied to two data sets. One consists of pill dissolution data, the other is taken from the Pittsburgh Youth Study (PYS). The PYS is a prospective longitudinal study of the development of delinquency, substance use, and mental health in male youth. We use the model-based parametric approach to examine the association between alcohol use at an early stage of adolescent development and delinquency over the course of adolescence.

Adolescent↗

PubMed2005

Simple fitting of subject-specific curves for longitudinal data.

We present a simple semiparametric model for fitting subject-specific curves for longitudinal data. Individual curves are modelled as penalized splines with random coefficients. This model has a mixed model representation, and it is easily implemented in standard statistical software. We conduct an analysis of the long-term effect of radiation therapy on the height of children suffering from acute lymphoblastic leukaemia using penalized splines in the framework of semiparametric mixed effects models. The analysis revealed significant differences between therapies and showed that the growth rate of girls in the study cannot be fully explained by the group-average curve and that individual curves are necessary to reflect the individual response to treatment. We also show how to implement these models in S-PLUS and R in the appendix.

Biometry↗

PubMedDate not supplied

Efficient statistical modelling of longitudinal data.

A new class of statistical models is proposed for the analysis of longitudinal data, especially those from growth studies. The models are all derived from a simple univariate two-level polynomial model. It is shown that they make efficient use of available data, and can handle a very wide range of problems. They have several important advantages over existing procedures.

Age Factors↗

PubMed2002

Dynamic conditionally linear mixed models for longitudinal data.

We develop a new class of models, dynamic conditionally linear mixed models, for longitudinal data by decomposing the within-subject covariance matrix using a special Cholesky decomposition. Here 'dynamic' means using past responses as covariates and 'conditional linearity' means that parameters entering the model linearly may be random, but nonlinear parameters are nonrandom. This setup offers several advantages and is surprisingly similar to models obtained from the first-order linearization method applied to nonlinear mixed models. First, it allows for flexible and computationally tractable models that include a wide array of covariance structures; these structures may depend on covariates and hence may differ across subjects. This class of models includes, e.g., all standard linear mixed models, antedependence models, and Vonesh-Carter models. Second, it guarantees the fitted marginal covariance matrix of the data is positive definite. We develop methods for Bayesian inference and motivate the usefulness of these models using a series of longitudinal depression studies for which the features of these new models are well suited.

Antidepressive Agents↗

PubMed2004

The dynamics of Wuchereria bancrofti infection: a model-based analysis of longitudinal data from Pondicherry, India.

This paper presents a model-based analysis of longitudinal data describing the impact of integrated vector management on the intensity of Wuchereria bancrofti infection in Pondicherry, India. The aims of this analysis were (1) to gain insight into the dynamics of infection, with emphasis on the possible role of immunity, and (2) to develop a model that can be used to predict the effects of control. Using the LYMFASIM computer simulation program, two models with different types of immunity (anti-L3 larvae or anti-adult worm fecundity) were compared with a model without immunity. Parameters were estimated by fitting the models to data from 5071 individuals with microfilaria-density measurement before and after cessation of a 5-year vector management programme. A good fit, in particular of the convex shape of the age-prevalence curve, required inclusion of anti-L3 or anti-fecundity immunity in the model. An individual's immune-responsiveness was found to halve in approximately 10 years after cessation of boosting. Explanation of the large variation in Mf-density required considerable variation between individuals in exposure and immune responsiveness. The mean life-span of the parasite was estimated at about 10 years. For the post-control period, the models predict a further decline in Mf prevalence, which agrees well with observations made 3 and 6 years after cessation of the integrated vector management programme.

Adolescent↗

PubMed2003

Median regression for longitudinal data.

We review and compare three estimators of median regression in linear models with longitudinal data. The estimators are constructed based on well-known ideas of weighting, decorrelating, and the working assumption of independence. Both asymptotic efficiency calculations and finite-sample Monte Carlo studies are used to assess the performance of these estimators. We find that their relative performances depend on the nature of covariates. The estimator under the working assumption of independence is computationally simple and yet has good relative performance when the covariates are invariant over time or when the within-subject correlations are small. Its relative performance in finite samples is also found to be more favourable than suggested by the asymptotic comparisons.

Analgesics↗

PubMed2005

[Longitudinal data related to health. A survey of health related panel studies in Germany].

Individual longitudinal data are indispensable for studying the differential incidence of diseases and of possible chronic conditions, health care interventions and outcomes in the course of time, the effects of changing population structure on morbidity, the demand and use of health services, as well as the efficacy and efficiency of health care guidelines. Here, we present an up to date survey of panel studies in Germany, in so far as these are either still active or were completed only recently, and in so far as they are accessible via public use files or equivalent solutions for the scientific community. There are some health related items on existing general purpose national panel studies, but no health related representative national panel study. In addition to the well known unique potential of panel data, we demonstrate, for special populations (e. g. foreigners and people living in institutionalised settings), as well as for special items (salutogenetic potentials and preventive resources), the particular need for health related panel data in the German context.

Data Collection↗

PubMed2000

You can go home again: evidence from longitudinal data.

In this paper we analyze the economic and demographic factors that influence return migration, focusing on generation 1.5 immigrants. Using longitudinal data from the 1979 youth cohort of the National Longitudinal Surveys (NLSY79), we track residential histories of young immigrants to the United States and analyze the covariates associated with return migration to their home country. Overall, return migration appears to respond to economic incentives, as well as to cultural and linguistic ties to the United States and the home country. We find no role for welfare magnets in the decision to return, but we learn that welfare participation leads to lower probability of return migration. Finally, we see no evidence of a skill bias in return migration, where skill is measured by performance on the Armed Forces Qualifying Test.

Age Factors↗