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PubMed2006

Relative risk of progressive glaucomatous visual field loss in patients enrolled and not enrolled in a prospective longitudinal study.

OBJECTIVE: To establish the relative risk of progressive visual field loss in a sample of glaucomatous eyes enrolled in a prospective longitudinal study vs a matched sample of eyes not enrolled in a study. METHODS: The first visual field records of 66 glaucomatous eyes enrolled in a prospective longitudinal study (mean follow-up time, 3.4 years; mean number of visual field tests, 8.3) were matched to 66 eyes from patients not enrolled in a study (mean follow-up time, 3 years; mean number of visual field tests, 3.7). Eyes were matched on the basis of (1) time of enrollment, (2) length of follow-up, and (3) the extent and spatial pattern of visual field loss. Linear regression of global visual field indexes was used to measure change and the relative risk of progression was calculated for a series of progression criteria sample. RESULTS: The relative risk of progressive visual field loss was on average 368% (range, 209%-673%) higher in the eyes not enrolled in a prospective longitudinal study. CONCLUSION: Selection bias may reduce the risk of progressive visual field loss in patients enrolled in longitudinal studies.

Disease Progression↗
PubMed2006

Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood.

OBJECTIVE: To use ethnically diverse, national data to examine longitudinal trends in race/ethnic disparities in 20 leading health indicators from Healthy People 2010 across multiple domains from adolescence to young adulthood. Much of what is known about health disparities is based on cross-sectional measures collected at a single time point. DESIGN, SETTING, AND PARTICIPANTS: Nationally representative data for more than 14 000 adolescents enrolled in wave I (1994-1995) or wave II (1996) of the National Longitudinal Study of Adolescent Health (Add Health) and followed up into adulthood (wave III; 2001-2002). We fit longitudinal regression models to assess and contrast the trend in health indicators among racial/ethnic groups of adolescents as they transition into adulthood. MAIN OUTCOME MEASURES: Diet, inactivity, obesity, tobacco use, substance use, binge drinking, violence, sexually transmitted diseases, mental health, and health care access. RESULTS: Diet, inactivity, obesity, health care access, substance use, and reproductive health worsened with age. Perceived health, mental health, and exposure to violence improved with age. On most health indicators, white and Asian subjects were at lowest and Native American subjects at highest risk. Although white subjects had more favorable health in adolescence, they experienced greatest declines by young adulthood. No single race/ethnic group consistently leads or falters in health across all indicators. CONCLUSIONS: Longitudinal data indicate that, for 15 of 20 indicators, health risk increased and access to health care decreased from the teen and adult years for most US race/ethnic groups. Relative rankings on a diverse range of health indicators (and patterns of change over time) vary by sex and race/ethnicity, causing disparities to fluctuate over time.

Adolescent↗
PubMed1993

Some conceptual and statistical issues in analysis of longitudinal psychiatric data. Application to the NIMH treatment of Depression Collaborative Research Program dataset.

Longitudinal studies have a prominent role in psychiatric research; however, statistical methods for analyzing these data are rarely commensurate with the effort involved in their acquisition. Frequently the majority of data are discarded and a simple end-point analysis is performed. In other cases, so called repeated-measures analysis of variance procedures are used with little regard to their restrictive and often unrealistic assumptions and the effect of missing data on the statistical properties of their estimates. We explored the unique features of longitudinal psychiatric data from both statistical and conceptual perspectives. We used a family of statistical models termed random regression models that provide a more realistic approach to analysis of longitudinal psychiatric data. Random regression models provide solutions to commonly observed problems of missing data, serial correlation, time-varying covariates, and irregular measurement occasions, and they accommodate systematic person-specific deviations from the average time trend. Properties of these models were compared with traditional approaches at a conceptual level. The approach was then illustrated in a new analysis of the National Institute of Mental Health Treatment of Depression Collaborative Research Program dataset, which investigated two forms of psychotherapy, pharmacotherapy with clinical management, and a placebo with clinical management control. Results indicated that both person-specific effects and serial correlation play major roles in the longitudinal psychiatric response process. Ignoring either of these effects produces misleading estimates of uncertainty that form the basis of statistical tests of hypotheses.

Analysis of Variance↗
PubMedDate not supplied

Model-based approaches to analysing incomplete longitudinal and failure time data.

Since Wu and Carroll (Biometrics 44, 175-188) proposed a model for longitudinal progression in the presence of informative dropout, several researchers have developed and studied models for situations where both a vector of repeated outcomes and an event time is available for each subject. These models have been developed for either longitudinal studies with dropout or for survival studies in which a random, time-varying covariate is measured repeatedly across time. When inference about the longitudinal variable is of interest, event times are treated as covariates and are often incomplete due to censoring. If survival or event time is the primary endpoint, repeated outcomes observed prior to the event are viewed as covariates; this covariate process is often incomplete, measured with error, or observed at unscheduled times during the study. We review several models which are used to handle incomplete response and covariate data in both survival and longitudinal studies.

Algorithms↗
PubMedDate not supplied

Longitudinal studies of cognitive, functional and behavioural change in patients with Alzheimer's disease.

This paper reviews data on the natural history of symptoms in patients with Alzheimer's disease (AD) and describes some of the problems encountered in analysing longitudinal data in this population. Data on cognition, functional ability and psychiatric or behavioural symptoms have all been obtained from AD patients. Because of attrition, the length of follow-up is not uniform for all patients and neither is the frequency of evaluation. Furthermore, patients enter longitudinal studies with a wide range of symptom severity and longitudinal decline in cognition and function is distinctly non-linear. Behavioural symptoms do not progress regularly in AD but are episodic phenomena not closely related to cognition or function. Strengths and limitations of various analytic techniques used for hypothesis testing with these longitudinal data are described.

Aged↗
PubMed1999

Familial aggregation of body mass index and subcutaneous fat measures in the longitudinal Québec family study.

Family resemblance for several measures of body fat and fat distribution was explored in the longitudinal Québec Family Study (QFS), including an overall measure of adiposity (body mass index, BMI), total subcutaneous fat (the sum of 6 skinfolds, SF6), and subcutaneous fat distribution (the trunk to extremity ratio, TER). Repeated measures were taken twice approximately 12 years apart. A longitudinal familial correlation model was used to assess familial resemblance at each of times 1, 2, and cross-time, and a univariate model was used for the change score. The change score was assumed to index the degree to which different familial factors impacted on the longitudinal resemblance, while the cross-time comparisons indexed similar familial factors across time. For BMI, the maximal heritability was 44 and 36% at times 1 and 2, respectively, 37% for the change score, and 33-43% for the cross-time comparison. While the etiology of the BMI familial effect at times 1, 2, and cross-time was assumed to be primarily polygenic, that for the change score was a function of cohort effects (environmental). For SF6, the maximal heritability (primarily genetic) was low at time 1 and for the change score (16%), but was nonsignificant at time 2 and cross-time. For TER, the maximal heritabilities were significant for each of times 1 (42%), 2 (40%), change score (59%), and cross-time comparisons (35-36%). In summary, simple univariate familial correlation analysis of the change scores and bivariate analysis of the longitudinal measures are useful in delineating the underlying factors leading to both change and stability across time.

Adipose Tissue↗
PubMed2006

Association of early radiographic damage with impaired physical function in rheumatoid arthritis: a ten-year, longitudinal observational study in 238 patients.

OBJECTIVE: To investigate the longitudinal relationship between physical disability, disease activity, and radiographic damage over 10 years in patients with rheumatoid arthritis (RA). METHODS: A cohort of 238 consecutively enrolled RA patients from the European Research on Incapacitating Disease and Social Support study (age range 20-70 years, mean disease duration 2.3 years, 68% rheumatoid factor [RF] positive) was followed up longitudinally for 10 years. Assessments at baseline and at years 1, 2, 5, and 10 comprised review of demographic variables, measurement of disease activity with the erythrocyte sedimentation rate (ESR), functional evaluations using the Health Assessment Questionnaire (HAQ) and grip strength (average value of the right and left hand), and radiographs of the hands scored by the van der Heijde modification of the Sharp method. Marginal modeling by generalized estimating equations was used to study the longitudinal relationship between the ESR and modified Sharp radiographic damage score with respect to explaining the HAQ score and grip strength. Age, sex, RF status, and disease duration were entered as covariates. RESULTS: The HAQ score and grip strength were longitudinally associated with the momentary modified Sharp/van der Heijde score as well as with progression in this score, independent of the ESR. Therefore, an increase of 10 units in the modified Sharp/van der Heijde score will result in a 0.03-unit increase in HAQ score and a 0.95-kg decrease in grip strength, after adjustment for the ESR. CONCLUSION: This 10-year observational study provides evidence that both radiographic damage and disease activity are independent contributors to impaired physical function in RA, both early and late in the disease process.

Adult↗
PubMed2006

A simple imputation method for longitudinal studies with non-ignorable non-responses.

Missing data are a common problem in longitudinal studies in the health sciences. Motivated by data from the Muscatine Coronary Risk Factor (MCRF) study, a longitudinal study of obesity, we propose a simple imputation method for handling non-ignorable non-responses (i.e., when non-response is related to the specific values that should have been obtained) in longitudinal studies with either discrete or continuous outcomes. In the proposed approach, two regression models are specified; one for the marginal mean of the response, the other for the conditional mean of the response given non-response patterns. Statistical inference for the model parameters is based on the generalized estimating equations (GEE) approach. An appealing feature of the proposed method is that it can be readily implemented using existing, widely-available statistical software. The method is illustrated using longitudinal data on obesity from the MCRF study.

Algorithms↗
PubMed2002

Extension of variance components approach to incorporate temporal trends and longitudinal pedigree data analysis.

Here we present a method that permits one to evaluate genetic effects and to detect genetic linkages by using serial observations of quantitative traits in pedigrees. We developed a statistical method that incorporates longitudinal family data and genetic marker information into an estimating equations framework. With this approach, we can study changes in components over time that measure polygenic and major genetic variances as well as shared and individual-specific environmental effects. Our method provides a measure of heritability from analysis of longitudinal data. Results using longitudinal family data from the Center for Preventive Medicine (Nancy, France) are presented. The results of our analysis show that the apolipoprotein E locus has no effect on interindividual variability in systolic blood pressure. We found that the longitudinal measure of heritability of systolic blood pressure is 0.32.

Apolipoproteins E↗
PubMed1991

Longitudinal precision of dual-energy x-ray absorptiometry in a multicenter study. The Nafarelin/Bone Study Group.

Reproducibility is a key issue in both clinical and research applications of bone mineral density (BMD) measurements. To examine the longitudinal precision of dual-energy x-ray absorptiometry (DEXA) for the measurement of mineral density in vivo and in vitro, the performance of a group of instruments in the course of a multicenter longitudinal clinical trial was monitored. Measures were performed on eight identical machines (Hologic QDR1000) and analyzed using the same automated software program. Short-term precision was good in vitro, [anthropomorphic spine phantoms; mean intrasite coefficient of variation (CV) 0.42 +/- 0.1% (SD)] and in vivo (lumbar spine; CV 1.1 +/- 0.5%). Intersite measures of a single spine phantom (specified mineral content -57.8 g) revealed a range of 57.3-58.4 g (CV 0.7%). In two subjects intersite CV in vivo were 3.7 and 2.1% (spine) and 1.8 and 3.2% (femoral neck). At five sites frequent phantom measures were performed over a 1 year period (mean number of measures 196) and revealed a mean all-point CV of 0.43% (range 0.35-0.53%). Longitudinal precision in vivo was somewhat less (mean CV of spinal measures 1.1%, femoral neck 1.2%, trochanter 1.3%, and Ward's area 2.4%). At one additional site large variations in phantom measures heralded repeated mechanical failures that eventually required machine replacement. In summary, DEXA demonstrates good in vitro and in vivo longitudinal precision, providing the basis for expanded clinical and research usefulness. Nevertheless, stringent quality assurance measures are required to detect and respond to system malfunctions.

Absorptiometry, Photon↗
PubMed1992

Radial bone mineral changes in pre- and postmenopausal healthy Japanese women: cross-sectional and longitudinal studies.

Healthy premenopausal and postmenopausal Japanese women were evaluated by longitudinal and cross-sectional studies to assess changes in radial bone mineral density. A total of 300 healthy Japanese women were enrolled in the cross-sectional study, and 126 of them were chosen for the longitudinal study. In the cross-sectional study significantly lower bone mineral density was found in the women over 50 years old. In the longitudinal study, premenopausal women aged 45-54 years showed rate of change of -0.61%/year at the distal radius and 0.49%/year at the radial shaft. The perimenopausal changes were -1.95 and -0.93%/year and the postmenopausal changes were -1.88 and -1.37%/year at the distal radius and at the radial shaft, respectively, in the women aged 45-54 years. These rates of premenopausal and postmenopausal change were similar to those reported for whites in the United States. The strength of the relationship between the rate of change in radial bone mineral density and the anthropometric values, biochemical data, and menopausal status was evaluated by multiple linear regression analysis. Menopausal status had the greatest influence on the rate of change in radial bone mineral density. It is concluded that menopausal status was the most important variable related to bone loss in our longitudinal study and the rate of bone loss was greatest during the early postmenopausal period in healthy Japanese women.

Adult↗
PubMed2003

Is patient dropout from a longitudinal study of lung function predictable and reversible?

Predictive factors for individuals dropping out from a longitudinal study of lung function have not been described, nor have methods for re-recruiting dropouts been developed. Our aims were to identify factors predictive of dropout and methods of re-recruiting dropouts from a longitudinal study of lung function. Details at enrollment into a longitudinal birth cohort study were compared between those who dropped out and those who were assessed 6 and 11 years later. Strategies for identifying individuals successfully contacted at 11 but not 6 years were described. Of 253 infants enrolled prenatally, 123 (49%) were followed up at age 6 and 194 (77%) at age 11 years. At 6 years, independent risk factors for a child dropping out were: no other sibling enrolled (OR, 4.0; 95% CI, 1.6, 10.1; P = 0.003); being first-born (OR, 2.8; 95% CI, 1.5, 5.2; P = 0.001); mother completing education at age 15 years (OR, 2.5; 95% CI, 1.3, 4.9; P = 0.007); and mother born overseas (OR, 2.3; 95% CI; 2.8; P = 0.01). The following were predictive of a child dropping out at 11 years: mother born overseas (OR, 4.8; 95% CI, 2.2, 10.2; P < 0.001); paternal smoker (OR, 3.1; 95% CI, 1.5, 6.5; P = 0.003); no other sibling enrolled (OR, 4.1; 95% CI, 1.1, 15.7; P = 0.04); and being first-born (OR, 2.1; 95% CI, 1.0, 4.3; P < 0.05). Of the 74 families successfully followed up at 11 but not 6 years, 35 (47%) were contacted by using data already held in study records, and 32 (43%) were contacted by databases available to the public. In conclusion, factors present at enrollment and predictive of dropout from a longitudinal cohort study can be identified. Using a variety of methods, re-recruitment is possible for many individuals who previously dropped out.

Australia↗
PubMed2001

Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.

Many cohort studies and clinical trials are designed to compare rates of change over time in one or more disease markers in several groups. One major problem in such longitudinal studies is missing data due to patient drop-out. The bias and efficiency of six different methods to estimate rates of changes in longitudinal studies with incomplete observations were compared: generalized estimating equation estimates (GEE) proposed by Liang and Zeger (1986); unweighted average of ordinary least squares (OLSE) of individual rates of change (UWLS); weighted average of OLSE (WLS); conditional linear model estimates (CLE), a covariate type estimates proposed by Wu and Bailey (1989); random effect (RE), and joint multivariate RE (JMRE) estimates. The latter method combines a linear RE model for the underlying pattern of the marker with a log-normal survival model for informative drop-out process. The performance of these methods in the presence of missing data completely at random (MCAR), at random (MAR) and non-ignorable (NIM) were compared in simulation studies. Data for the disease marker were generated under the linear random effects model with parameter values derived from realistic examples in HIV infection. Rates of drop-out, assumed to increase over time, were allowed to be independent of marker values or to depend either only on previous marker values or on both previous and current marker values. Under MACR all six methods yielded unbiased estimates of both group mean rates and between-group difference. However, the cross-sectional view of the data in the GEE method resulted in seriously biased estimates under MAR and NIM drop-out process. The bias in the estimates ranged from 30 per cent to 50 per cent. The degree of bias in the GEE estimates increases with the severity of non-randomness and with the proportion of MAR data. Under MCAR and MAR all the other five methods performed relatively well. RE and JMRE estimates were more efficient(that is, had smaller variance) than UWLS, WLS and CL estimates. Under NIM, WLS and particularly RE estimates tended to underestimate the average rate of marker change (bias approximately 10 per cent). Under NIM, UWLS, CL and JMRE performed better in terms of bias (3-5 per cent) with the JMRE giving the most efficient estimates. Given that markers are key variables related to disease progression, missing marker data are likely to be at least MAR. Thus, the GEE method may not be appropriate for analysing such longitudinal marker data. The potential biases due to incomplete data require greater recognition in reports of longitudinal studies. Sensitivity analyses to assess the effect of drop-outs on inferences about the target parameters are important.

Adolescent↗
PubMed2005

Regression models for the analysis of longitudinal Gaussian data from multiple sources.

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry.

Adolescent↗
PubMed2006

Shared parameter models for the joint analysis of longitudinal data and event times.

Longitudinal studies often gather joint information on time to some event (survival analysis, time to dropout) and serial outcome measures (repeated measures, growth curves). Depending on the purpose of the study, one may wish to estimate and compare serial trends over time while accounting for possibly non-ignorable dropout or one may wish to investigate any associations that may exist between the event time of interest and various longitudinal trends. In this paper, we consider a class of random-effects models known as shared parameter models that are particularly useful for jointly analysing such data; namely repeated measurements and event time data. Specific attention will be given to the longitudinal setting where the primary goal is to estimate and compare serial trends over time while adjusting for possible informative censoring due to patient dropout. Parametric and semi-parametric survival models for event times together with generalized linear or non-linear mixed-effects models for repeated measurements are proposed for jointly modelling serial outcome measures and event times. Methods of estimation are based on a generalized non-linear mixed-effects model that may be easily implemented using existing software. This approach allows for flexible modelling of both the distribution of event times and of the relationship of the longitudinal response variable to the event time of interest. The model and methods are illustrated using data from a multi-centre study of the effects of diet and blood pressure control on progression of renal disease, the modification of diet in renal disease study.

Blood Pressure↗
PubMed2007

A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome.

We consider a joint model for exploring association between several correlated longitudinal markers and a clinical event. A nonlinear growth mixture model exhibits the different latent classes of evolution of the latent quantity underlying the correlated longitudinal markers and a logistic regression models the probability of occurence of the clinical event according to the latent classes. By introducing a flexible nonlinear transformation including parameters to be estimated between each marker and the latent process, the model also deals with non-Gaussian continuous markers. Through an application on cognitive ageing, the two advantages of the model are underlined: (1) the latent profiles of evolution associated with the clinical event are described including covariate effects in the longitudinal model but also in the probability of class membership and in the probability of occurence of the event, and (2) a diagnostic and a prognostic tools are derived from the model for early detection of the clinical event using any available information about the longitudinal markers.

Data Interpretation, Statistical↗
PubMed1988

Longitudinal versus cross-sectional estimation of lung function decline--further insights.

This paper explores the extent to which differences in longitudinal versus cross-sectional inference may be influenced by the choice of statistical models. Using lung function data on 524 working men, we first compare the goodness-of-fit and implication for longitudinal decline of a variety of cross-sectional models. We then compare the predicted longitudinal patterns from these models with those observed over a period of four years. In general, both approaches provide qualitatively, if not quantitatively, similar messages concerning the relative effects of smoking and age on lung function decline. Nonetheless, we acknowledge the existence of real selection and cohort effects. Although we recognize the utility of cross-sectional designs, we discourage quantitative comparisons between studies, especially longitudinal versus cross-sectional.

Adult↗
PubMedDate not supplied

Intentionally incomplete longitudinal designs: I. Methodology and comparison of some full span designs.

Longitudinal designs are important in medical research and in many other disciplines. Complete longitudinal studies, in which each subject is evaluated at each measurement occasion, are often very expensive and motivate a search for more efficient designs. Recently developed statistical methods foster the use of intentionally incomplete longitudinal designs that have the potential to be more efficient than complete designs. Mixed models provide appropriate data analysis tools. Fixed effect hypotheses can be tested via a recently developed test statistic, FH. An accurate approximation of the statistic's small sample non-central distribution makes power computations feasible. After reviewing some longitudinal design terminology and mixed model notation, this paper summarizes the computation of FH and approximate power from its non-central distribution. These methods are applied to obtain a large number of intentionally incomplete full-span designs that are more powerful and/or less costly alternatives to a complete design. The source of the greater efficiency of incomplete designs and potential fragility of incomplete designs to randomly missing data are discussed.

Longitudinal Studies↗