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Results for “longitudinal data”

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PubMed2001

A mixed effects model for the analysis of ordinal longitudinal pain data subject to informative drop-out.

We extend the model of Pulkstenis et al. that models binary longitudinal data, subject to informative drop-out through remedication, to the ordinal response case. We present a selection model shared-parameter approach that specifies mixed models for both ordinal response and discrete survival time to remedication. In this fashion, the random parameter present in both models completely characterizes the relationship between response and time to remedication inducing their conditional independence. With a log-log link function for both response and study 'survival', as well as specification of a log-gamma distribution for the random effect, we obtain a closed-form expression for the marginal log-likelihood of response and time to remedication that does not require approximation or numerical integration techniques. A data analysis is performed and simulation results presented which support the consistency of parameter and standard error estimates.

Computer Simulation↗

PubMed1995

Predictors of hand function in older persons: a two-year longitudinal analysis.

OBJECTIVE: To validate a hierarchical model of hand function in older persons, using longitudinal data. DESIGN: Longitudinal observational study (2-year data from an ongoing longitudinal study). SUBJECTS: 689 persons older than age 60, including Continuing Care Retirement Community (n = 230), homebound (n = 204), and ambulatory (n = 255) respondents. Mean age at baseline 76.6 (SD = 8.8). MEASUREMENT: Independent variables included sociodemographics, physician measures of upper joint impairment, self-reported comorbidity, arthritis pain, depression, and anxiety. The dependent variables included grip strength and a timed manual performance test. MAIN RESULTS: Using generalized estimated equations (GEE) to test our hierarchial model, we found that gender and upper extremity joint impairment were the strongest predictors of a longitudinal measure of grip strength. Grip strength, in turn, along with demographics, comorbidity, and a measure of psychological status, was significantly related to timed manual performance. CONCLUSIONS: The longitudinal analyses confirmed a previous cross-sectional finding that upper extremity joint impairment contributes significantly to reduced grip strength, which, in turn, contributes to reduced hand performance on a timed test.

Activities of Daily Living↗

PubMed2004

Modeling nonlinear effects in longitudinal survival data: implications for the physiological dynamics of biological systems.

Despite the wealth of longitudinal data on the health dynamics of human populations, information on covariates (risk factors) changes in those studies has not been systematically and fully exploited. In this work we use the 46-year follow-up of the Framingham Heart Study to analyze dynamics of these risk factors in survival models that go far beyond the standard linear dynamic formulation. We focus on improving the inferences about the physiology of human aging processes and its plasticity and on modeling state trajectories for individuals considering the effect of nonlinear interactions among covariates. We find that using standard statistical methods to construct models describing the age dependence of health status might give rise to surprising results with highly "diluted" dynamics, but with significantly improved statistical criteria. It is found that problems with the dynamics are a consequence of the intrinsic nonlinear nature of these models. We show that evolution of the risk factors measured in the Framingham study is more complicated for females than for males (i.e., female health status is more sensitive to nonlinear interactions among risk factors). We suggest that this is due to the rapid rate of decline of estrogen production after menopause.

Aging↗

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↗

PubMed2000

Estimating transition probabilities from aggregate samples plus partial transition data.

Longitudinal studies often collect only aggregate data, which allows only inefficient transition probability estimates. Barring enormous aggregate samples, improving the efficiency of transition probability estimates seems to be impossible without additional partial-transition data. This paper discusses several sampling plans that collect data of both types, as well as a methodology that combines them into efficient estimates of transition probabilities. The method handles both fixed and time-dependent categorical covariates and requires no assumptions (e.g., time homogeneity, Markov) about the population evolution.

Acquired Immunodeficiency Syndrome↗

PubMed1993

Short-term forecasting of internal migration.

A new methodological approach to the forecasting of short-term trends in internal migration in the United States is introduced. "Panel-data (or longitudinal-data) models are used to represent the relationship between destination-specific out-migration and several explanatory variables. The introduction of this methodology into the migration literature is possible because of some new and improved databases developed by the U.S. Bureau of the Census.... Data from the Bureau of Economic Analysis are used to investigate the incorporation of exogenous factors as variables in the model." The exogenous factors considered include employment and unemployment, income, population size of state, and distance between states. The author concludes that "when one...includes additional parameters that are estimable in longitudinal-data models, it turns out that there is little additional information in the exogenous factors that is useful for forecasting."

Americas↗

PubMed2001

Gender differences in the longitudinal predictors of adolescent dating violence.

BACKGROUND: Adolescent dating violence is a public health problem. The public health approach to prevention is to identify predictors of problem behaviors and develop interventions to eliminate or reduce those predictors with the intention of altering the chain of causation. Longitudinal data are preferred for identifying predictors of behavior but all dating violence studies have used cross-sectional data. We use longitudinal data to examine predictors of adolescent dating violence from several domains guided by an ecological perspective. METHODS: Eighty percent (N = 1,965) of the 8th- and 9th-graders in one county completed baseline questionnaires in school and 90% (N = 1,759) of those adolescents completed questionnaires again 1&1/2 years later. Proportional odds models were used to identify cross-sectional correlates and longitudinal predictors of dating violence perpetration that occurred between baseline and follow-up. RESULTS: Most of the study variables were correlated with dating violence in cross-sectional analyses. Having friends who are victims of dating violence, using alcohol, and being of a race other than white predicted dating violence perpetration by females. Holding attitudes that are accepting of dating violence predicted dating violence perpetration by males. CONCLUSION: The findings suggest that intervention strategies should vary for males and females and that when basing interventions on cross-sectional findings, scarce resources may be stretched to address persons who may not truly be at risk of beginning to perpetrate dating violence.

Adolescent↗

PubMed2003

Joint regression and association modeling of longitudinal ordinal data.

We propose models for longitudinal, or otherwise clustered, ordinal data. The association between subunit responses is characterized by dependence ratios (Ekholm, Smith, and McDonald, 1995, Biometrika 82, 847-854), which are extended from the binary to the multicategory case. The joint probabilities of the subunit responses are expressed as explicit functions of the marginal means and the dependence ratios of all orders, obtaining a computational advantage for likelihood-based inference. Equal emphasis is put on finding regression models for the univariate cumulative probabilities, and on deriving the dependence ratios from meaningful association-generating mechanisms. A data set on the effects of treatment with Fluvoxamine, which has been analyzed in parts before (Molenberghs, Kenward, and Lesaffre, 1997, Biometrika 84, 33-44), is analyzed in its entirety. Selection models are used for studying the sensitivity of the results to drop-out.

Analysis of Variance↗

PubMed2006

A local influence sensitivity analysis for incomplete longitudinal depression data.

In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple ad hoc methods that are valid only if the data are missing completely at random (MCAR), to more principled (likelihood-based or Bayesian) ignorable analyses, which are valid under the less restrictive missing at random (MAR) assumption. The availability of the necessary standard statistical software allows for such analyses in practice. Although the possibility of data missing not at random (MNAR) cannot be ruled out, it is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials. Therefore, rather than either forgetting about or blindly shifting to an MNAR framework, the optimal place for MNAR analyses is within a sensitivity analysis context. Such analyses can be used, for example, to assess how sensitive results from an ignorable analysis are to possible departures from MAR and how much results are affected by influential observations. In this article, we apply the local influence sensitivity tool (Verbeke et al., 2001) to a longitudinal depression trial, thereby applying it to continuous outcomes from clinical trials.

Antidepressive Agents↗

PubMed2006

Latent curve analyses of longitudinal twin data using a mixed-effects biometric approach.

In a recent article McArdle and Prescott (2005) showed how simultaneous estimation of the biometric parameters can be easily programmed using current mixed-effects modeling programs (e.g., SAS PROC MIXED). This article extends these concepts to deal with mixed-effect modeling of longitudinal twin data. The biometric basis of a polynomial growth curve model was used by Vandenberg and Falkner (1965) and this general class of longitudinal models was represented in structural equation form as a latent curve model by McArdle (1986). The new mixed-effects modeling approach presented here makes it easy to analyze longitudinal growth-decline models with biometric components based on standard maximum likelihood estimation and standard indices of goodness-of-fit (i.e., chi(2), df, epsilon(a)). The validity of this approach is first checked by the creation of simulated longitudinal twin data followed by numerical analysis using different computer programs (i.e., Mplus, Mx, MIXED, NLMIXED). The practical utility of this approach is examined through the application of these techniques to real longitudinal data from the Swedish Adoption/Twin Study of Aging (Pedersen et al., 2002). This approach generally allows researchers to explore the genetic and nongenetic basis of the latent status and latent changes in longitudinal scores in the absence of measurement error. These results show the mixed-effects approach easily accounts for complex patterns of incomplete longitudinal or twin pair data. The results also show this approach easily allows a variety of complex latent basis curves, such as the use of age-at-testing instead of wave-of-testing. Natural extensions of this mixed-effects longitudinal approach include more intensive studies of the available data, the analysis of categorical longitudinal data, and mixtures of latent growth-survival/frailty models.

Biometry↗

PubMed1989

Inter-regional migration and social change: a study of South East England based upon data from the Longitudinal Study.

"Data from the [Office of Population Censuses and Surveys] Longitudinal Study are used to trace the social class effects of migration between the South East region and the rest of England and Wales in the period 1971-81. The analysis begins with the migration streams into and out of the South East of those people who were in the labour market (including the unemployed) at both census dates. The paper then proceeds to an analysis of the migration streams of those who entered or left the labour market between 1971 and 1981. Finally, the effects of the migration streams on the social class composition of both the South East and the rest of England and Wales are summarised. The results are interpreted in the light of debates about change in the British urban and regional system."

Demography↗

PubMedDate not supplied

Longitudinal dispersivity data and implications for scaling behavior.

Longitudinal dispersivity (alpha) data were compiled from 109 different authors for different types of geological media. The data were subdivided into different subsets. Dispersivity values for consolidated media were subdivided as basalts, granites, sandstones, and carbonate rocks, while unconsolidated sediments were subdivided into three reliability classes. The data sets provided here may provide ground water practitioners a preliminary guide to estimate dispersivity values at various scales and to guide and verify theories on scaling behavior. Based on the data set presented here, the relationship that empirically best described the dispersivity data in regard to scale of measurement was in the form of a power law. The scaling exponent for consolidated and unconsolidated geological media varied between 0.40 and 0.92, and 0.44 and 0.94, respectively. Higher reliability subsets of data for the unconsolidated sediments and more frequently tested rock formations indicate that the scaling exponent is at the lower end of the observed range, close to 0.5. No significant difference in scaling exponent was found among different media, and no clear evidence exists for the presence of an upper bound or asymptotic behavior on the relationship for any of the analyzed media.

Geological Phenomena↗

PubMed1997

A stochastic model for the analysis of bivariate longitudinal AIDS data.

We present a model for multivariate repeated measures that incorporates random effects, correlated stochastic processes, and measurement errors. The model is a multivariate generalization of the model for univariate longitudinal data given by Taylor, Cumberland, and Sy (1994, Journal of the American Statistical Association 89, 727-736). The stochastic process used in this paper is the multivariate integrated Ornstein-Uhlenbeck (OU) process, which includes Brownian motion and a random effects model as special limiting cases. This process is an underlying continuous-time autoregressive order [AR(1)] process for the derivatives of the multivariate observations. The model allows unequally spaced observations and missing values for some of the variables. We analyze CD4 T-cell and beta-2-microglobulin measurements of the seroconverters at multiple time points from the Los Angeles section of the Multicenter AIDS Cohort Study. The model allows us to investigate the relationship between CD4 and beta-2-microglobulin through the correlations between their random effects and their serial correlation. The data suggest that CD4 and beta-2-microglobulin follow a bivariate Brownian motion process. The fit of the model implies that an increase in beta-2-microglobulin is associated with a decrease in future CD4 but not vice versa, agreeing with immunologic postulates about the relationship between these two variables.

Acquired Immunodeficiency Syndrome↗

PubMed2003

Toward a definition and method of assessment of treatment failure and treatment effectiveness: the case of leflunomide versus methotrexate.

OBJECTIVE: Time to treatment discontinuation (TTD) is an accepted method of assessing treatment effectiveness in the community, but is susceptible to channeling bias and secular and cohort effects. In addition, TTD does not consider the addition of new disease modifying antirheumatic drugs (DMARD) to insufficiently effective therapies. We expand the definition of treatment failure to include discontinuation or addition of a second DMARD (1) to examine leflunomide (LEF) versus methotrexate (MTX) effectiveness in clinical practice; (2) to obtain an estimate of overall clinical effectiveness; and (3) to identify factors associated with treatment successes and failure. In addition, (4) we test the feasibility of performing a clinical trial using a longitudinal data bank. METHODS: Using the National Data Bank for Rheumatic Diseases longitudinal data bank, 1431 patients with rheumatoid arthritis (RA) who began taking LEF or MTX as part of their routine medical care were followed from 1998 through 2001. None of the 1431 patients had received either treatment previously. Patients were assessed at 6 month intervals for periods up to 36 months by mailed questionnaires concerning DMARD therapy and demographic and RA severity factors. Kaplan-Meier survivor functions and Cox regression analyses were used to assess treatment failure, defined as time to discontinuation or to the addition of a second DMARD. RESULTS: For 756 patients taking LEF, the failure rate was 55.5 per 100 patient-years, and the median time to failure was 15 (95% CI 13, 17) months. For 675 patients taking MTX the failure rate was 57.3 per 100 patient-years, and the median failure time was 14 (95% CI 12, 18) months. These differences were not statistically significant. The overall rate of discontinuation was 68.7% of the failure rate. Discontinuation was predicted by adverse effects [hazard ratio 1.76 (95% CI 1.51, 2.04)] and by clinical status prior to starting DMARD, and these results were not affected by specific DMARD treatment. Discontinuation was more common with LEF, and addition of a second DMARD was more common with MTX. More than 77% of treatment failures, defined by use of additional therapy, resulted in starting anti-tumor necrosis factor treatment rather than a conventional DMARD. CONCLUSION: In an observational clinical trial using a contemporary longitudinal data bank, with time to treatment failure as the outcome, LEF and MTX had equal effectiveness as measured by time to treatment failure. Treatment failure rates were substantially greater than noted historically. Given the availability of many efficacious additional treatment options, this increase in failure rate appears to reflect a greater propensity to discontinue and/or add therapy.

Adult↗

PubMed1984

Self-generated identification codes for anonymous collection of longitudinal questionnaire data.

The success of a self-generated identification code for linking longitudinal questionnaire data was examined. The matching procedure developed for linking questionnaires, including a simple technique to compensate for nonidentical codes, yielded a high success rate (92% linkage of cases over a one-month interval and 78% over a one-year interval) and very few incorrectly linked cases. The procedure worked equally well with elementary and high school students, and the resulting samples were representative of the student population on a wide range of measures. Some suggestions are offered regarding the elements comprising self-generated codes.

Analysis of Variance↗

PubMed2003

The General Practice Research Network: the capabilities of an electronic patient management system for longitudinal patient data.

PURPOSE: To evaluate the potential of a general practice research database derived directly from de-identified electronic medical records to provide national prescribing data in Australia. To observe the utilisation of a computer-based patient management system over time. To evaluate the impact of the research network participation on data quality in participants' electronic records. METHOD: A random sample of 297 general practitioners (GPs) from 128 practices provided longitudinal patient data from electronic medical records (using Medical Director software) retrospectively from 1 January 1999 to April 2002. The General Practice Research Network (GPRN) database contains approximately 600,000 patients, representing over 4 million prescriptions from 4 million encounters. The quality and representativeness of data for prescribing, morbidity and software usage were evaluated by comparison with National data. RESULTS: Older GPs (> 55) were under-represented, perhaps due to lower computer usage rates, but patient visits were similar to the national distribution. Over time, there were increases in: the quality of prescribing data (with reason for visit/prescription being compulsorily recorded); recording of non-prescribing visits; and the use of other features of the electronic patient management software. CONCLUSIONS: Data derived from electronic general practice records is of sufficient quality to be used to provide national prescribing estimates and has potential value for pharmacoepidemiology and population health monitoring. Such longitudinal data has previously been unavailable in Australia. Monitoring of software usage demonstrates the evolution of the Australian GP user and will be increasingly useful in assessing and improving the quality of electronic medical records.

Australia↗

PubMed2003

Estimates of incidence rates with longitudinal claims data.

OBJECTIVE: To estimate incidence rates of the 3 major chronic eye diseases--diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (ARMD)--by using longitudinal claims data from Medicare. METHODS: Longitudinal cases were ascertained by using a national probability sample of Medicare beneficiaries aged 65 years and older in 1991 who initially had none of the eye diseases documented. After adjusting for death and enrollment in a health maintenance organization, claims filed by optometrists or ophthalmologists with an International Classification of Diseases, Ninth Revision, Clinical Modification code for all forms of DR, glaucoma, and ARMD were used to indicate diagnosis. RESULTS: Annual incidence rates for the 3 conditions after the first year of observation ranged from 14.3% to 17.7% (higher earlier) across an 8-year longitudinal follow-up. Incidence rates among those with diabetes mellitus for any form of DR varied between 3.8% and 6.5%, while those for glaucoma varied between 4.6% and 7.8% and those for ARMD varied between 7.5% and 9.3%. CONCLUSIONS: Longitudinal claims data after the first year provide relatively stable estimates of incidence rates on an annual basis. These estimates are comparable with those of the few population-based studies available.

Centers for Medicare and Medicaid Services, U.S.↗

PubMed1997

Analytic approaches to longitudinal caries data in adults.

The objective of this paper is to consider current methods for analyzing longitudinal caries data in adults. To illustrate these methods, we used data from the Piedmont dental study, a prospective investigation of the oral health of older adults. Longitudinal dental data sets comprise repeated observations of an outcome (often clustered within randomly selected primary sampling units), and a set of covariates for each of many subjects, in whom clustering can occur as a result of measuring teeth, or surfaces, within people. One objective of statistical analysis is to predict the outcome variable as a function of the covariates, while accounting for the correlation among the repeated observations for a given subject and the effect of clustering within subjects, as well as between subjects within primary sampling units, such as communities, schools, hospitals, or other such units. We considered two statistical approaches: generalized estimating equations and survey regression models. We also examined the impact of varying diagnostic criteria for caries estimation between epidemiologists and clinicians. One approach is to perform the usual time(x) exam score minus time0 score analysis for the baseline and final examinations, while an alternative is to analyze trends among interim examinations. Finally, because caries studies in which the onset of the disease is the endpoint face the problem of censoring due to subject attrition and/or tooth loss, we recommend the incidence density (time-to-event) analytic strategy to address this problem. This approach was found to be most suitable for longitudinal studies of older adults since it accounts for the time each surface remains at risk for the event of interest, making use of interim exam data until the moment the subject and/or the tooth are no longer available for examination. We also included a discussion on biases that occur upon application of the usual methods of estimating caries experience in missing teeth and crowns, which often ignore the classification error in the estimation. We propose a method to adjust for misclassification of the M-component of the DMFS index. In the case where one can observe true reversals or remineralization of caries lesions, we recommend an adjustment formula to account for reversals that are most likely due to examiner misclassification. We provide examples to demonstrate the applicability of the methods for covariates subject to outcome misclassification.

Adult↗