Unlinked vital events in census-based longitudinal studies can bias subsequent analysis

Objective: To examine the potential biases arising from the nonlinkage of census records and vital events in longitudinal studies.
Study Design and Setting: A total of 56,396 deaths of residents of Northern Ireland in the 4 years after the 2001 Census were linked to the 2001 Census records. The characteristics of matched and nonmatched death records were compared using multivariate logistic regression. Subject attributes were as recorded on the death certificate.
Results: In total, 3,392 (6.0%) deaths could not be linked to a census record. Linkage rates were lowest in young adults, males, the unmarried, people living in communal establishments, or living in areas that were more deprived or had recorded low census enumeration. For those aged less than 65 years at census, this linkage would exclude from analysis 20.2% of suicides and 19.7% of deaths by external causes.
Conclusion: The nonlinkage of census and death records is a combination of nonenumeration at census and deficient information about the deceased recorded at the time of death. Unmatched individuals may have been more disadvantaged or socially isolated, and analysis based on the linked data set may therefore show some bias and perhaps understate true social gradients.

Systematic review of the survival rate and the incidence of biological, technical, and aesthetic complications of single crowns on implants reported in longitudinal studies with a mean follow-up of 5 years

To assess the 5-year survival of implant-supported single crowns (SCs) and to describe the incidence of biological, technical, and aesthetic complications. The focused question was: What is the survival rate of implants supporting single crowns and implant-supported crowns with a mean follow-up of 5 years and to which extent do biological, technical, and aesthetic complications occur?

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Disentangling the effects of low self-esteem and stressful events on depression: Findings from three longitudinal studies

Diathesis-stress models of depression suggest that low self-esteem and stressful events jointly influence the development of depressive affect. More specifically, the self-esteem buffering hypothesis states that, in the face of challenging life circumstances, individuals with low self-esteem are prone to depression because they lack sufficient coping resources, whereas those with high self-esteem are able to cope effectively and consequently avoid spiraling downward into depression. The authors used data from 3 longitudinal studies of adolescents and young adults, who were assessed 4 times over a 3-year period (Study 1; N = 359), 3 times over a 6-week period (Study 2; N = 249), and 4 times over a 6-year period (Study 3; N = 2,403). In all 3 studies, low self-esteem and stressful events independently predicted subsequent depression but did not interact in the prediction. Thus, the results did not support the self-esteem buffering hypothesis but suggest that low self-esteem and stressful events operate as independent risk factors for depression. In addition, the authors found evidence in all 3 studies that depression, but not low self-esteem, is reciprocally related to stressful events, suggesting that individuals high in depression are more inclined to subsequently experience stressful events.

Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies

Low self-esteem and depression are strongly related, but there is not yet consistent evidence on the nature of the relation. Whereas the vulnerability model states that low self-esteem contributes to depression, the scar model states that depression erodes self-esteem. Furthermore, it is unknown whether the models are specific for depression or whether they are also valid for anxiety. We evaluated the vulnerability and scar models of low self-esteem and depression, and low self-esteem and anxiety, by meta-analyzing the available longitudinal data (covering 77 studies on depression and 18 studies on anxiety). The mean age of the samples ranged from childhood to old age. In the analyses, we used a random-effects model and examined prospective effects between the variables, controlling for prior levels of the predicted variables. For depression, the findings supported the vulnerability model: The effect of self-esteem on depression (β = -.16) was significantly stronger than the effect of depression on self-esteem (β = -.08). In contrast, the effects between low self-esteem and anxiety were relatively balanced: Self-esteem predicted anxiety with β = -.10, and anxiety predicted self-esteem with β = -.08. Moderator analyses were conducted for the effect of low self-esteem on depression; these suggested that the effect is not significantly influenced by gender, age, measures of self-esteem and depression, or time lag between assessments. If future research supports the hypothesized causality of the vulnerability effect of low self-esteem on depression, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression.

Development of self-esteem and relationship satisfaction in couples: Two longitudinal studies

We examined the effects of self-esteem development on the development of relationship satisfaction in 2 samples of couples. Study 1 used data from both partners of 885 couples assessed 5 times over 12 years, and Study 2 used data from both partners of 6,116 couples assessed 3 times over 15 years. The pattern of results was similar across the 2 studies. First, development of relationship satisfaction could be modeled as a couple-level process. Second, initial level of self-esteem of each partner predicted the initial level of the partners’ common relationship satisfaction, and change in self-esteem of each partner predicted change in the partners’ common relationship satisfaction. Third, these effects did not differ by gender and held when controlling for participants’ age, length of relationship, health, and employment status. Fourth, self-esteem similarity among partners did not influence the development of their relationship satisfaction. The findings suggest that the development of self-esteem in both partners of a couple contributes in a meaningful way to the development of the partners’ common satisfaction with their relationship.

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

In vivo longitudinal studies of spinal cord injury and vascular endothelial growth factor treatment

Approximately 12,000 new cases of spinal cord injury (SCI) are added each year to the estimated 259,000 Americans living with SCI. The majority of these patients return to society, their lives forever changed by permanent loss of sensory and motor function. While there are no FDA approved drugs for the treatment of SCI or a universally accepted standard therapy, the current though controversial treatment includes the delivery of high dosages of the corticosteroid methyliprednisolone sodium succinate, surgical interventions to stabilize the spinal column, and physical rehabilitation. It is therefore critically important to fully understand the pathology of injury and determine novel courses and rationally-based therapies for SCI. ^ Vascular endothelial growth factor (VEGF) is an attractive target for treating central nervous system (CNS) injury and disease because it has been shown to influence angiogenesis and neuroprotection. Preliminary studies have indicated that increased vasculature may be associated with functional recovery; therefore exogenous delivery of a pro-angiogenic growth factor such as VEGF may improve neurobehavioral outcome. In addition, VEGF may provide protection from secondary injury and result in increased survival and axonal sprouting. ^ In these studies, SCI rats received acute intraspinal injections of VEGF, the antibody to VEGF, or vehicle control. The effect of these various agents was investigated using longitudinalmulti-modal magnetic resonance imaging (MRI), neuro- and sensory behavioral assays, and end point immunohistochemistry. We found that rats that received VEGF after SCI had increased tissue sparing and improved white matter integrity at the earlier time points as shown by advanced magnetic resonance imaging (MRI) techniques. However, these favorable effects of VEGF were not maintained, suggesting that additional treatments with VEGF at multiple time points may be more beneficial, Histological examinations revealed that VEGF treatment may result in increased oligodendrogenesis and therefore may eventually lead to remyelination and improved functional outcome. ^ On the neurobehavioral studies, treatments with VEGF and Anti-VEGF did not significantly affect performance on tests of open-field locomotion, grid walk, inclined plane, or rearing. However, VEGF treatment resulted in significantly increased incidence of chronic neuropathic pain. This phenomenon could possibly be attributed to the fact that VEGF treatment may promote axonal sprouting and also results in tissue sparing, thereby providing a substrate for the growth of new axons. New connections made by these sprouting axons may involve components of pathways involved in the transmission of pain and therefore result in increased pain in those animals. ^

Design of mixed-longitudinal studies

Cross-sectional designs, longitudinal designs in which a single cohort is followed over time, and mixed-longitudinal designs in which several cohorts are followed for a shorter period are compared by their precision, potential for bias due to age, time and cohort effects, and feasibility. Mixed longitudinal studies have two advantages over longitudinal studies: isolation of time and age effects and shorter completion time. Though the advantages of mixed-longitudinal studies are clear, choosing an optimal design is difficult, especially given the number of possible combinations of the number of cohorts and number of overlapping intervals between cohorts. The purpose of this paper is to determine the optimal design for detecting differences in group growth rates.^ The type of mixed-longitudinal study appropriate for modeling both individual and group growth rates is called a "multiple-longitudinal" design. A multiple-longitudinal study typically requires uniform or simultaneous entry of subjects, who are each observed till the end of the study.^ While recommendations for designing pure-longitudinal studies have been made by Schlesselman (1973b), Lefant (1990) and Helms (1991), design recommendations for multiple-longitudinal studies have never been published. It is shown that by using power analyses to determine the minimum number of occasions per cohort and minimum number of overlapping occasions between cohorts, in conjunction with a cost model, an optimal multiple-longitudinal design can be determined. An example of systolic blood pressure values for cohorts of males and cohorts of females, ages 8 to 18 years, is given. ^

Derived variables for longitudinal studies

Suppose that for each individual a vector of features is measured at a number of time points. We look for a transformation of the features, the same at all time points, that will induce a simple dependency structure. In the simplest situation this requires that a certain asymmetric matrix has real nonzero eigenvalues. Extensions are considered.