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PubMed1992

Aging and total cholesterol levels: cohort, period, and survivorship effects.

This analysis describes the association of age with the serum total cholesterol level in 5,010 participants in a geriatric health screening program. Cholesterol levels were measured annually in participants monitored for up to 12 years. The association of age with cholesterol level is described via three approaches: cross-sectional analysis, descriptive longitudinal analysis, and longitudinal analysis using statistical modeling. The results were compared to examine the influence of cohort, period, and survivorship effects on the association between age and cholesterol. In cross-sectional analysis, the cholesterol level was fairly constant for the ages of 65 to 75 years, but decreased by 21% over the age range from 75 to 95 years. Descriptive longitudinal analysis suggested that both cohort and period effects were influencing the cross-sectional findings. In longitudinal analysis adjusting for both cohort and period effects, the findings were similar to those from cross-sectional analysis for the ages of 65 to 75 years, but from the ages of 75 to 95 years, cholesterol decreased by only 9%--half as great a decline as that estimated from cross-sectional analysis. When longitudinal data were limited to those with complete follow-up, the predicted decline for the age range from 75 to 95 years was only 6%. Although this flattening of the age trend was suggestive, there was no conclusive evidence that it reflected an association between baseline cholesterol and loss to follow-up.

Age Factors↗
PubMed1999

Age-related changes in intraocular pressure in a large Japanese population: a cross-sectional and longitudinal study.

OBJECTIVE: To assess the influence of aging, blood pressure, and body mass index (BMI) on intraocular pressure (IOP) in a large Japanese population. DESIGN: Cross-sectional and longitudinal study. PARTICIPANTS: The participants in this study were 69,643 Japanese men and women 20 to 79 years of age. They were office workers and their family members who had received annual health examinations from 1989 to 1997. The records of the participants who received health examinations were reviewed retrospectively. Each participant was examined according to a standard protocol, including tonometry with a noncontact tonometer, anthropometric measurements, and blood pressure measurements. The data from the subjects' most recent visit were analyzed cross-sectionally. The data from the 68,998 men and women of the total participants who were born between the 1920s and 1960s were used in longitudinal analysis. TESTING: Tonometric and anthropometric measurements. MAIN OUTCOME MEASURES: Mean values of IOP, systolic blood pressure, diastolic blood pressure, and BMI were determined. The relationship among IOP, age, systolic blood pressure, diastolic blood pressure, and BMI was studied using the multiple linear regression model with cross-sectional analysis. In longitudinal analysis, regression coefficients of IOP against age, systolic blood pressure, diastolic blood pressure, and BMI were calculated using the mixed effect model. RESULTS: The mean (+/-standard deviation) IOP values for men and women were 11.9+/-2.5 and 11.5+/-2.4 mmHg, respectively. In cross-sectional analysis, IOP decreased significantly with age (P < 0.001). However, longitudinal analysis showed that IOP increased significantly with age in both men and women (P < 0.001). Systolic blood pressure and BMI were positively correlated to IOP in both the cross-sectional and longitudinal studies. CONCLUSION: The authors found an inconsistency in the change in IOP against age between cross-sectional and longitudinal analysis. It is suspected that birth cohort differences in ocular characteristics influence IOP in the Japanese population.

Adult↗
PubMed2006

Longitudinal genetic analysis of plasma lipids.

The consensus from published studies is that plasma lipids are each influenced by genetic factors, and that this contributes to genetic variation in risk of cardiovascular disease. Heritability estimates for lipids and lipoproteins are in the range .48 to .87, when measured once per study participant. However, this ignores the confounding effects of biological variation measurement error and ageing, and a truer assessment of genetic effects on cardiovascular risk may be obtained from analysis of longitudinal twin or family data. We have analyzed information on plasma high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, and triglycerides, from 415 adult twins who provided blood on two to five occasions over 10 to 17 years. Multivariate modeling of genetic and environmental contributions to variation within and across occasions was used to assess the extent to which genetic and environmental factors have long-term effects on plasma lipids. Results indicated that more than one genetic factor influenced HDL and LDL components of cholesterol, and triglycerides over time in all studies. Nonshared environmental factors did not have significant long-term effects except for HDL. We conclude that when heritability of lipid risk factors is estimated on only one occasion, the existence of biological variation and measurement errors leads to underestimation of the importance of genetic factors as a cause of variation in long-term risk within the population. In addition our data suggest that different genes may affect the risk profile at different ages.

Adolescent↗
PubMed2003

Longitudinal data analysis in pedigree studies.

Longitudinal family studies provide a valuable resource for investigating genetic and environmental factors that influence long-term averages and changes over time in a complex trait. This paper summarizes 13 contributions to Genetic Analysis Workshop 13, which include a wide range of methods for genetic analysis of longitudinal data in families. The methods can be grouped into two basic approaches: 1) two-step modeling, in which repeated observations are first reduced to one summary statistic per subject (e.g., a mean or slope), after which this statistic is used in a standard genetic analysis, or 2) joint modeling, in which genetic and longitudinal model parameters are estimated simultaneously in a single analysis. In applications to Framingham Heart Study data, contributors collectively reported evidence for genes that affected trait mean on chromosomes 1, 2, 3, 5, 8, 9, 10, 13, and 17, but most did not find genes affecting slope. Applications to simulated data suggested that even for a gene that only affected slope, use of a mean-type statistic could provide greater power than a slope-type statistic for detecting that gene. We report on the results of a small experiment that sheds some light on this apparently paradoxical finding, and indicate how one might form a more powerful test for finding a slope-affecting gene. Several areas for future research are discussed.

Cardiovascular Diseases↗
PubMed1988

Stability and change in adult intelligence: 2. Simultaneous analysis of longitudinal means and covariance structures.

We analyzed data on psychometric intelligence from the Seattle Longitudinal Study, simultaneously estimating longitudinal factors, their covariance structure, and their mean levels. Data on five Thurstone Primary Mental Abilities subtests were available for 412 adults, ages 22-70 at first test, who were tested three times at 7-year intervals. A previous longitudinal factor analysis had shown high stability of individual differences (covariance stability) in general intelligence for three adult age groups. We extended that model to estimate factor means. All three age groups showed high levels of covariance stability, but differed sharply in their mean profiles. The young group showed increasing levels of general intelligence, the middle-aged group had stable levels of intelligence, and the old group showed salient, approximately linear, decline. The patterns of stability in middle-age, followed by mean decline and high covariance stability in old age, suggest a normative developmental transition from a stability pattern to a decline pattern of general intelligence, with the inflection point occurring somewhere around age 60.

Adult↗
PubMedDate not supplied

Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data.

Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.

Animals↗
PubMed1998

Models for the longitudinal genetic analysis of same-age twins: application to HDL cholesterol.

Models are presented for the analysis of longitudinal data from same-age twins which permit the exploration of a remarkably diverse array of alternative explanations for continuity and change during development. Data of this type permit the detection of new sources of genetic or environmental covariation during development that are not expressed at earlier ages and, because they include the effects of age-specific genes, the resulting heritability estimates are more reliable than those obtained from relatives who differ in age. The proposed models were applied to measurements of HDL cholesterol obtained on 81 pairs of monozygotic (MZ) twins and 69 dizygotic (DZ) pairs at 11, 12.5 and 14 years of age. All three MZ co-twin correlations were substantially higher than the self correlations across occasions, suggesting that new sources of genetic or environmental covariation must be expressed during early adolescence. This interpretation was confirmed by analysis of the full covariance matrices which showed that only models which assumed the expression of new or age-specific genes could explain the observed pattern of covariation. Because they include the effects of age-specific genes, the resulting heritabilities (0.80-0.83) were substantially higher than many previous estimates.

Adolescent↗
PubMed1995

Longitudinal data analysis for linear Gaussian models with random disturbed-highest-derivative-polynomial subject effects.

For linear regression analysis of longitudinal data with Gaussian response, I propose a new model to generalize the traditional class of random effects models in which the random effects are deterministic polynomials with coefficients randomly distributed over subjects with mean zero. The generalization is accomplished by adding zero mean Gaussian 'disturbances' to the highest derivative of each random coefficient subject polynomial, independently at each observation time. The resulting random effects, which have mean zero at each observation time, are called disturbed highest derivative polynomials (DHDPs). The disturbances induce serial correlation and also allow the subject-specific DHDP time trends to be non-linear. I do not estimate the subject-specific DHDP time trends. Analysis is based on the marginal model, that is, the fixed effects or population model obtained by integrating the random polynomial coefficients and all disturbances out of the joint distribution of themselves and the response vector. This allows a 'population averaged' interpretation. One can select the DHDP order by an information criterion. When the population time trend is not correctly modelled, the optimal DHDP order will be larger than when it is correctly modelled. One can make the covariance matrix of the regression coefficients robust to errors in modelling the within-subject dependence. I describe the relationship of a DHDP to a smoothing polynomial spline, and show how to replace the DHDP model with a smoothing polynomial spline model for the within-subject dependence in the marginal model.

Bias↗
PubMed2001

Longitudinal data analysis: an application to construction of a natural history profile of Duchenne muscular dystrophy.

A 30-month prospective study of 27 Scandinavian boys with confirmed diagnosis of Duchenne muscular dystrophy was carried out to construct profiles of the natural history of the disease. Assessments which included measures of voluntary muscle strength and function were done at 3 monthly intervals except for the first and second which were separated by 1 month. Recently developed statistical methods for analysis of longitudinal data with repeated observations on the same individual were used avoiding the problem of induced serial correlations. This allowed for the construction of both reference and prediction profiles for the variables %MRC, motor ability, walking time for 10 m and the sum of myometry of seven muscle groups.

Child↗
PubMed2002

Pathways through adolescent smoking: a 7-year longitudinal grouping analysis.

This study examined longitudinal patterns of smoking among students (N = 852) followed from 6th through 12th grades using longitudinal grouping analysis. Six patterns (clusters) were identified: nonsmokers, quitters, experimenters, early escalators, late escalators, and continuous smokers. Baseline (6th-grade) differences in associated risk factors were examined. Growth curve modeling revealed meaningful intercluster differences in risk factor trends over the study period. In general, nonsmokers had the fewest baseline risk factors and slowest increase in risk factors, whereas continuous smokers had higher baseline and more rapidly increasing trends in risk factors. Results suggest that some clusters may respond to population-based antismoking interventions, whereas others (early escalators and continuous smokers) will probably require more focused interventions.

Adolescent↗
PubMed2006

Analysis of longitudinal multinomial outcome data.

Analysis of categorical outcomes in a longitudinal study has been an important statistical issue. Continuous outcome in a similar study design is commonly handled by the mixed effects model. The longitudinal binary or Poisson-like outcome analysis is often handled by the generalized estimation equation (GEE) method. Neither method is appropriate for analyzing a multinomial outcome in a longitudinal study, although the cross-sectional multinomial outcome is often analyzed by generalized linear models. One reason that these methods are not used is that the correlation structure of two multinomial variables can not be easily specified. In addition, methods that rely upon GEE or mixed effects models are unsuitable in instances when the focus of a longitudinal study is on the rate of moving from one category to another. In this research, a longitudinal model that has three categories in the outcome variable will be examined. A continuous-time Markov chain model will be used to examine the transition from one category to another. This model permits an unbalanced number of measurements collected on individuals and an uneven duration between pairs of consecutive measurements. In this study, the explicit expression for the transition probability is derived that provides an algebraic form of the likelihood function and hence allows the implementation of the maximum likelihood method. Using this approach, the instantaneous transition rate that is assumed to be a function of the linear combination of independent variables can be estimated. For a comparison between two groups, the odds ratios of occurrence at a particular category and their confidence intervals can be calculated. Empirical studies will be performed to compare the goodness of fit of the proposed method with other available methods. An example will also be used to demonstrate the application of this method.

Biometry↗
PubMed1994

Longitudinal changes in IQ among fragile X females: a preliminary multicenter analysis.

Longitudinal declines in IQ among fragile X [fra(X)] males have been reported previously by several investigators. Remarkably little is known about longitudinal changes in IQ scores among fra(X) females. Previously, one cross-sectional study showed a significant negative correlation between age and IQ scores. However, a recent investigation of girls with fra(X) syndrome noted longitudinal increases in IQ scores in 8 of 11 individuals. Therefore, the purpose of this preliminary multicenter study was to determine: (1) the characteristics of longitudinal changes in IQ among fra(X) females; and (2) whether these changes were comparable to those which have been observed among fra(X) males. IQ test and retest scores for 11 fra(X) females were obtained from 3 centers: Greenwood, South Carolina; Ibaraki, Japan; and Leuven, Belgium. To ensure high reliability, only test-retest scores from the Wechsler and Stanford-Binet tests were used. Age of subjects at initial testing ranged from 5 to 35 years. Mean intertest interval was 4.5 years. In contrast to a report of longitudinal increases, we found 9/11 (82%) subjects demonstrated decreases in IQ scores. Mean decline was 9 points. Females over 18 years of age showed little or no change in IQ scores. Decreases in scores appeared to be related to initial IQ score. Females in the earlier longitudinal report were higher functioning than those in our study, which may account for the observed difference in direction of change; or, change in IQ score may be related to size of the fra(X) mutation.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2003

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches.

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene x gene and gene x environment interactions. There was evidence to suggest the existence of gene x environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene x gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene x gene or gene x environment interactions.

Adult Children↗
PubMed1999

Longitudinal data analysis (repeated measures) in clinical trials.

Longitudinal data is often collected in clinical trials to examine the effect of treatment on the disease process over time. This paper reviews and summarizes much of the methodological research on longitudinal data analysis from the perspective of clinical trials. We discuss methodology for analysing Gaussian and discrete longitudinal data and show how these methods can be applied to clinical trials data. We illustrate these methods with five examples of clinical trials with longitudinal outcomes. We also discuss issues of particular concern in clinical trials including sequential monitoring and adjustments for missing data. A review of current software for analysing longitudinal data is also provided. Published in 1999 by John Wiley & Sons, Ltd. This article is a US Government work and is the public domain in the United States.

Clinical Trials as Topic↗
PubMed2004

Longitudinal data analysis. A comparison between generalized estimating equations and random coefficient analysis.

The analysis of data from longitudinal studies requires special techniques, which take into account the fact that the repeated measurements within one individual are correlated. In this paper, the two most commonly used techniques to analyze longitudinal data are compared: generalized estimating equations (GEE) and random coefficient analysis. Both techniques were used to analyze a longitudinal dataset with six measurements on 147 subjects. The purpose of the example was to analyze the relationship between serum cholesterol and four predictor variables, i.e., physical fitness at baseline, body fatness (measured by sum of the thickness of four skinfolds), smoking and gender. The results showed that for a continuous outcome variable, GEE and random coefficient analysis gave comparable results, i.e., GEE-analysis with an exchangeable correlation structure and random coefficient analysis with only a random intercept were the same. There was also no difference between both techniques in the analysis of a dataset with missing data, even when the missing data was highly selective on earlier observed data. For a dichotomous outcome variable, the magnitude of the regression coefficients and standard errors was higher when calculated with random coefficient analysis then when calculated with GEE-analysis. Analysis of a dataset with missing data with a dichotomous outcome variable showed unpredictable results for both GEE and random coefficient analysis. It can be concluded that for a continuous outcome variable, GEE and random coefficient analysis are comparable. Longitudinal data-analysis with dichotomous outcome variables should, however, be interpreted with caution, especially when there are missing data.

Adipose Tissue↗
PubMed2007

An approach to joint analysis of longitudinal measurements and competing risks failure time data.

Joint analysis of longitudinal measurements and survival data has received much attention in recent years. However, previous work has primarily focused on a single failure type for the event time. In this paper we consider joint modelling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint which occurs frequently in clinical trials. Our model uses latent random variables and common covariates to link together the sub-models for the longitudinal measurements and competing risks failure time data, respectively. An EM-based algorithm is derived to obtain the parameter estimates, and a profile likelihood method is proposed to estimate their standard errors. Our method enables one to make joint inference on multiple outcomes which is often necessary in analyses of clinical trials. Furthermore, joint analysis has several advantages compared with separate analysis of either the longitudinal data or competing risks survival data. By modelling the event time, the analysis of longitudinal measurements is adjusted to allow for non-ignorable missing data due to informative dropout, which cannot be appropriately handled by the standard linear mixed effects models alone. In addition, the joint model utilizes information from both outcomes, and could be substantially more efficient than the separate analysis of the competing risk survival data as shown in our simulation study. The performance of our method is evaluated and compared with separate analyses using both simulated data and a clinical trial for the scleroderma lung disease.

Clinical Trials as Topic↗
PubMedDate not supplied

Guttman scale analysis of longitudinal data: a methodology and drug use applications.

Traditional Guttman scalogram analysis is limited to evaluating item order cross-sectionally. This paper describes a new methodology, Longitudinal Scalogram Analysis (LSA), that is a direct extension of cross-sectional scalogram analysis to longitudinal data. Example applications of the LSA method to drug use data are provided. The benefits of LSA relative to cross-sectional methods for drug use analysis are discussed.

Adolescent↗
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↗