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PubMedDate not supplied

Component analysis in multivariate aging research.

A method of components analysis, related to principal components analysis, is described for applications in multivariate cross-sectional or longitudinal data. The method may be useful to researchers seeking to reduce the number of observed variables through scale construction. The method creates component variables as weighted sums of the observed variables using weights that are identical across groups and occasions. The statistical and conceptual properties of these components are discussed. The method is contrasted with traditional principal components analysis and factor analysis. An application of the method is presented using longitudinal WAIS and WAIS-R data.

Cross-Sectional Studies↗

PubMed1991

Measurement of oral status and treatment need among subjects with dental prostheses: are the measures less reliable than the prostheses? Part I: Oral status in removable prosthodontics.

Indices used to measure the oral status of removable dental prostheses generally do not conform to criteria essential to high-quality measures. Existing indices fail to meet accepted standards of reliability and validity, and most lack specified training protocols, use ambiguous terminology, are limited to nominal, ordinal, or dichotomous level data, and require high levels of observer inference. As a result it is virtually impossible to compare one study with another, to combine data from more than one study to broaden the results obtained, and even to collect longitudinal data without being able to rule out examiner drift. More successful measures, in particular for vertical dimension of occlusion, maxillary denture retention, and arch size and shape, hold promise for future developments in research data collection and quality assessment. These improvements, combined with a growing awareness of the importance of measuring nondental factors, should lead to more reliable, valid measures of need for treatment.

Denture, Complete↗

PubMed1999

The Society of Thoracic Surgeons National Database: current status and future directions.

The Society of Thoracic Surgeons National Database, established seven years ago by thoracic surgeons for self improvement and quality assurance, now has 1,181,464 patients registered, including 897,914 coronary artery bypass operations. Risk-adjusted 30 day mortality for coronary bypass procedures, unadjusted mortality for other cardiothoracic procedures, unadjusted morbidity and length of stay as well as several processes of care are measured. There has been a progressive decrease in operative mortality and length of stay over the past seven years. Deaths, complications, and lengths of stay are stratified according to estimated risk of death. Definitions have been refined in conjunction with the American College of Cardiology. The database is being increasingly utilized for state analyses and is in demand by other organizations and third party carriers. Logistic regression analysis is now utilized for development of the risk models. The database has been useful for health care policy decisions and can be useful for our Professional Affairs Committee in their dealings with government. Other uses include measuring access to care and cost. Data quality improvement measures have been put in place, as well as data manager education. The General Thoracic and Congenital data acquisition packages are being modified and improved, and a goal is to begin collecting longitudinal data to demonstrate the long term efficacy of thoracic procedures. The data elements have been decreased from 500 to 200+ core variables for simplification. With the changing healthcare environment and emphasis on cost cutting, collecting valid data by a national specialty group enhances the monitoring of quality of care, thus protecting our patients from overzealous cutbacks. Data is essential to document the efficacy, quality and cost-effectiveness of the procedures we perform and is a necessary tool for each of us to have to assure the quality and continued success of our practices.

Coronary Artery Bypass↗

PubMed1996

Longitudinal studies in organizational stress research: a review of the literature with reference to methodological issues.

Demonstrating causal relationships has been of particular importance in organizational stress research. Longitudinal studies are typically suggested to overcome problems of reversed causation and third variables (e.g., social desirability and negative affectivity). This article reviews the empirical longitudinal literature and discusses designs and statistical methods used in these studies. Forty-three longitudinal field reports on organizational stress were identified. Most of the investigations used a 2-wave panel design and a hierarchical multiple regression approach. Six studies with 3 and more waves were found. About 50% of the studies analyzed potential strain-stressor (reversed causation) relationships. In about 33% of the studies there was some evidence of reverse causation. The power of longitudinal studies to rule out third variable explanations was not realized in many studies. Procedures of how to analyze longitudinal data are suggested.

Humans↗

PubMed2000

Survival analysis in clinical trials: past developments and future directions.

The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.

Biometry↗

PubMed1997

Linking large administrative databases: a method for conducting emergency medical services cohort studies using existing data.

OBJECTIVE: To evaluate probabilistic matching for linking a cohort of cardiac arrest (CA) patients identified in the Metro Toronto Ambulance (MTA) database in Toronto, Ontario, Canada, to their appropriate record in either the Vital Statistics Information System (VSIS) or the Canadian Institute of Health Information (CIHI) databases and thus establish their clinical outcomes. METHODS: A linkage of a large administrative database was performed. A cohort of patients who suffered an out-of-hospital CA during the calendar years 1988-1993 was identified. To determine the patients' outcomes, the cohort was probabilistically linked to patient records in the VSIS and CIHI databases. Identifying variables used during the process of linking records included: names (first and last); New York State Identification and Intelligence System (NYSIIS) code; date of event; date of death; city; admitting hospital number; mode of admission to hospital; age; and sex. RESULTS: A cohort of 7,079 CA patients was identified from the MTA database; 6,448 (91%) patients were accurately linked to records in 1 of the 2 outcome databases (CIHI, VSIS). Missing data for > or = 1 of the linking variables were responsible for unlinked records. Using these longitudinal data, it was possible to determine the number of patients surviving their out-of-hospital CAs to be admitted to hospital (n = 833) (16%). No differences in survival rates (p = 0.06) or median lengths of hospital stay among the survivors (p = 0.15) were observed between admitting hospitals. CONCLUSIONS: Probabilistic matching is an effective method by which researchers can use existing administrative data to determine outcomes of population cohorts. This is especially valuable in situations where controlled intervention studies are not feasible or may be inappropriate. In this analysis, in-hospital management of admitted CA patients, as determined by hospital-specific survival rates and length of stay, suggests no measurable differences in the care provided to these patients by hospitals in Toronto.

Algorithms↗

PubMed1984

An illustration of the experimental range of variation of blood pressure.

The nominal uniformity of long-term mean pressure (approximately 100 mmHg at heart level) for all mammalian species belies the broad nature of its variance, which is found at many time scales. Such variations should be a useful index of physiologically normal and pathological processes. However, it is dubious whether a scientifically meaningful determination of central blood pressure at its many process scales can be achieved from a few isolated measurements: to cite a common and highly significant instance, measurement made by a physician on a patient during one or two office visits. The data presented here are far from exhaustive (a few reports in the literature and detailed longitudinal data on 1 subj), but they serve to illustrate a protocol and a sample of the physiological results that might be expected if data were gathered for larger populations. The basic scales of interest lie in the lower-frequency domain, involving periods of fluctuations longer than tens of minutes.

Adult↗

PubMed2005

Factors to weigh when considering electronic data collection.

Researchers are increasingly considering the adoption of electronic data-collection methods--which entail the use of the Web, e-mail, and desktop and handheld computers--for surveys, diaries, research instruments, and focus groups. Based on prior research findings on electronic data collection, the authors delineate the key factors, which incorporate population, data, and resource characteristics, that guide researchers in selecting the data-collection method most appropriate for their research question. Population factors to consider in data-collection decisions are age, gender, socio-economic status, and access to technology. Key data factors influencing the selection of collection method include sensitivity of the topic, time sensitivity, longitudinal data, and contextual data. Data-collection decisions are also based on financial, time, and technological resources. Technology and demographics will continue to change and affect data-collection methods and possibilities. By examining these key factors, however, researchers will be able to reach data-collection decisions that are appropriate for each project.

Computer Simulation↗

PubMed1991

Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data.

A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on six occasions.

Analysis of Variance↗

PubMed1993

PC program for estimating polynomial growth, velocity and acceleration curves when subjects may have missing data.

A stand-alone, menu-driven PC program, written in GAUSS386i, for estimating polynomial growth, velocity, and acceleration curves from longitudinal data is described, illustrated and made available to interested readers. Missing data are accommodated: we assume that the study is planned so that individuals will have common times of measurement, but allow some of the sequences to be incomplete. The degrees, Di, adequate to fit the growth profiles of the N individuals are determined and the corresponding polynomial regression coefficients are calculated and can be saved in ASCII files which may then be imported into a statistical computing package for further analysis. Examples of the use of the program are provided.

Achondroplasia↗

PubMed2005

Longitudinal modeling of adiposity in periadolescent Greek schoolchildren.

PURPOSE: Obesity has an etiology that is multidimensional in nature. Given the dearth of longitudinal data, we examined changes in adipose tissue (Ad) in relation to physical activity levels (PA), aerobic fitness (AF), and energy intake (EI) in Greek schoolchildren, as they progressed from age 12 to 14 yr. METHODS: This was a 2-yr and three-time-point (TP) study. Participants (N=210 (TP1); =204 (TP2); =198 (TP3)) were assessed for anthropometry, maturity status, Ad, PA, AF, and EI. Mean values were used for exploratory analyses, whereas two generalized estimating equations (GEE) models examined for longitudinal associations between the studied parameters. The first (GEE1) aimed to extract inherent associations between the dependent (Ad) and independent (PA, AF, EI) variables for the entire study period. For further evidence of association, the second analysis (GEE2) used the independent variables at TP1 and TP2 to predict the dependent variables at TP3. RESULTS: Levels of Ad in boys decreased significantly (P<0.05) from TP1 to TP3, whereas the same variable demonstrated a nonsignificant increase (P>0.05) in girls. GEE1 revealed that longitudinal changes in Ad were significantly associated only with PA (beta=-0.16; P<0.001) and AF (beta=-0.09; P<0.05) for all schoolchildren. Similarly, GEE2 revealed that the main factors (at TP1 and TP2) predicting the development of Ad (at TP3) were PA (beta=-0.14; P<0.001) followed by AF (beta=-0.10; P<0.05). CONCLUSION: With respect to data presented, we established that longitudinal changes in Ad are mainly accompanied by changes in PA and, to a lesser extent, AF levels.

Adiposity↗

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↗

PubMed2000

Structured latent growth curves for twin data.

We describe methods to fit structured latent growth curves to data from MZ and DZ twins. The well-known Gompertz, logistic and exponential curves may be written as a function of three components - asymptote, initial value, and rate of change. These components are allowed to vary and covary within individuals in a structured latent growth model. Such models are highly economical, requiring a small number of parameters to describe covariation across many occasions of measurement. We extend these methods to analyse longitudinal data from MZ and DZ twins and focus on the estimation of genetic and environmental variation and covariation in each of the asymptote, initial and rate of growth factors. For illustration, the models are fitted to longitudinal Bayley Infant Mental Development Scale data published by McArdle (1986). In these data, all three components of growth appear strongly familial with the majority of variance associated with the shared environment; differences between the models were not great. Occasion-specific residual factors not associated with the curve components account for approximately 40% of variance of which a significant proportion is additive genetic. Though the growth curve model fit less well than some others, they make restrictive, falsifiable predictions about the mean, variance and twin covariance of other (not yet measured) occasions of measurement.

Algorithms↗

PubMed1993

An assessment of survival rates and within-patient clustering of failures for endosseous oral implants.

This study examined endosseous cylinder implant survival, defined as the unqualified presence of the implant in the mouth at the end of the observation period, in 598 consecutive VA patients, with a total of 2098 implants. Data were taken from the Department of Veterans Affairs (VA) Dental Implant Registry, which has maintained longitudinal data on the survival of individual dental implants in VA patients since 1987. The maximum time of observation in any one patient was 2040 days (5.6 yr). Survival analysis by use of life-table methods was carried out on both an implant- and a patient-specific basis. Implant cases were accrued randomly, and therefore a random censoring model was used. A correlated binomial model was used for assessment of the degree of within-patient clustering of implant removals. Results showed that the implant-specific survival rate during the longest time interval (5.6 yr) was 89.9%; the patient-specific implant survival rate during the same time was 78.2%. Among implants which were removed, the mean time to removal was 292 days. The hazard function, which describes the probability of implant loss as a function of time, decreased steadily throughout the observation period. The correlated binomial model suggested a clustering of removals within patients with multiple implants (rho = 0.11, p = 0.0001). The odds of having a second implant removed were 1.3 times greater if the patient had already had one implant removed. This study suggests that when implants fail, they do so soon after placement, and the likelihood of failure decreases steadily from implantation through the first five years post-surgery.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

PubMed2003

Longitudinal effect of intimate partner abuse on high-risk behavior among adolescents.

OBJECTIVE: To determine the longitudinal effect of abuse by an intimate partner on risk behavior among adolescents. DESIGN AND PARTICIPANTS: Linear regression analyses of longitudinal data from 4443 adolescents surveyed in 1995 (Wave I) and 1996 (Wave II) from the National Longitudinal Study of Adolescent Health Public Use Dataset. MAIN OUTCOME MEASURES: Abuse was assessed using a 5-point scale measuring if the subject had been insulted in public, sworn at, threatened with violence, pushed or shoved, or had something thrown at them by an intimate partner. The primary outcome measures were changes between Waves I and II in each of the following 5-risk behaviors: illicit substance use, antisocial behavior, violent behavior, suicidal behavior, and depression. RESULTS: Abuse between Waves I and II was associated with higher rates of all 5 risk behaviors at both Waves I and II among both sexes. After adjusting for sociodemographic factors, number of intimate partners, time between interviews, baseline risk behavior scores, and the most abusive relationship experienced prior to Wave I, more severe abuse having occurred between Waves I and II was significantly associated with increased levels of depression in both sexes and increased involvement in illicit substance use, antisocial behavior, and suicidal behavior among female adolescents. CONCLUSIONS: Abuse by an intimate partner is associated with higher levels of risk behavior in both sexes and incident abuse is associated with increased depression in both sexes and increased illicit substance use, antisocial behavior, and suicidal behavior among females. Intimate partner violence interventions should address the negative behaviors associated with abuse, particularly among female adolescents.

Adolescent↗

PubMed1996

Statistical approaches to human brain mapping by functional magnetic resonance imaging.

Proper use of functional neuro-imaging through effective experimental design and modern statistical analysis provides new insights in current brain research. This tutorial has two aims: to describe aspects of this technology to applied statisticians and to provide some statistical ideas to neuroscientists unfamiliar with quantitative analytic methods that accommodate randomness. Introductory background material and ample references to current literature on the physics of magnetic resonance imaging, Fourier methods for image reconstruction and measures of image quality are included. Two of the statistical approaches mentioned here are extensions of established methods for longitudinal data analysis to the frequency domain. A recent case study provides real-world instances of approaches, problems and open questions encountered in current functional neuro-imaging research and an introduction to the analysis of spatial time series in this context.

Algorithms↗

PubMed1993

A comparison of the generalized estimating equation approach with the maximum likelihood approach for repeated measurements.

Liang and Zeger proposed an extension of generalized linear models to the analysis of longitudinal data. Their approach is closely related to quasi-likelihood methods and can handle both normal and non-normal outcome variables such as Poisson or binary outcomes. Their approach, however, has been applied mainly to non-normal outcome variables. This is probably due to the fact that there is a large class of multivariate linear models available for normal outcomes such as growth models and random-effects models. Furthermore, there are many iterative algorithms that yield maximum likelihood estimators (MLEs) of the model parameters. The multivariate linear model approach, based on maximum likelihood (ML) estimation, specifies the joint multivariate normal distribution of outcome variables while the approach of Liang and Zeger, based on the quasi-likelihood, specifies only the marginal distributions. In this paper, I compare the approach of Liang and Zeger and the ML approach for the multivariate normal outcomes. I show that the generalized estimating equation (GEE) reduces to the score equation only when the data do not have missing observations and the correlation is unstructured. In more general cases, however, the GEE estimation yields consistent estimators that may differ from the MLEs. That is, the GEE does not always reduce to the score equation even when the outcome variables are multivariate normal. I compare the small sample properties of the GEE estimators and the MLEs by means of a Monte Carlo simulation study.

Clinical Trials as Topic↗

PubMed2006

Longitudinal assessment of cardiovascular exercise performance after pediatric heart transplantation.

BACKGROUND: No existing longitudinal data document exercise performance after pediatric heart transplantation. We report the exercise performance findings from the longitudinal study of pediatric heart transplantation patients and the association of aerobic capacity with echocardiographic measures of graft function. METHODS: We performed a retrospective analysis of 28 children after heart transplantation who underwent 87 exercise tests and echocardiograms. Subjects exercised using graded cycle or treadmill protocols. Maximal oxygen consumption (VO2), physical working capacity, peak heart rate, and anaerobic threshold were evaluated. To measure systolic and diastolic function, shortening fraction and mitral valve pressure half-time (PHT) respectively, were obtained by echocardiography. RESULTS: The average age at transplantation was 10.9 +/- 5.6 years, at initial exercise test was 13.8 +/- 5.0 years, and at final exercise test was 15.8 +/- 5.2 years. Percent-predicted values at the initial exercise test were VO2, 59.3%; physical working capacity, 60.2%; and peak heart rate, 75.8%; these remained similarly decreased at the final exercise test. Shortening fraction and PHT were within normal limits, but PHT was significantly greater at final test (p < 0.05). The relationship of VO2% with time was statistically significant, described by a quadratic equation that included initial VO2% and time from heart transplantation. This relationship remained significant when the shortening fraction (p < .05) but not PHT was added as a covariate in the equation. CONCLUSIONS: Exercise performance after pediatric heart transplantation is impaired and, despite an initial improvement, declines over time. This can be explained by increasing diastolic dysfunction independent of donor graft age. If confirmed, these findings point the direction to further research aimed at limiting this aerobic capacity decline after heart transplantation.

Adult↗