Cohabitation, Marriage, and Union Dissolution in Norway: A Comparative Prospective Study

Longitudinal data, comprising repeated measurements of the same individuals over time, arise frequently in cardiology and the biomedical sciences in general. For example, Frison and Pocock1 used repeated measurements of the liver enzyme creatine kinase in serum of cardiac patients to study changes in liver function over a 12-month study period. The main goal, indeed the raison d’etre , of a longitudinal study is characterization of changes in the response of interest over time. Ordinarily, changes in the response are also related to selected covariates. For example, Frison and Pocock1 compared changes in creatine kinase between patients randomized to active drug and placebo. The past 25 years have witnessed remarkable developments in statistical methods for the analysis of longitudinal data. Despite these important advances, researchers in the biomedical sciences have been somewhat slow to adopt these methods and often rely on statistical techniques that fail to adequately account for longitudinal study designs. The goal of the present report is to provide an overview of some recently developed methods for longitudinal analyses that are more appropriate, with a focus on 2 methods for continuous responses: the analysis of response profiles and linear mixed-effects models. The analysis of response profiles is better suited to settings with a relatively small number of repeated measurements, obtained on a common set of occasions, whereas linear mixed-effects models are suitable in more general settings. Before describing these methods, we review some of the defining features of longitudinal studies and highlight the main aspects of longitudinal data that complicate their analysis. ### Covariance Structure A common feature of repeated measurements on an individual is correlation; that is, knowledge of the value of the response on one occasion provides information about the likely value of the response on a future occasion. Another common feature of longitudinal data is heterogeneous …

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

This report was derived from a workshop on cardiovascular risk assessment sponsored by the National Heart, Lung, and Blood Institute, which addressed whether risk equations developed in the Framingham Heart Study (FHS) for predicting new-onset coronary heart disease (CHD) apply to diverse population groups. Preparation for the workshop included a reanalysis and comparison of prospective studies in several different populations in which risk factors were related to cardiovascular outcomes. Some studies included fatal and nonfatal CHD end points, whereas others contained only CHD mortality. Extensive collaboration provided as much uniformity as possible with respect to both risk factors and CHD end points. The FHS has led in defining the quantitative impact of risk factors.1 Many potential risk factors were measured and related to cardiovascular outcomes. Several risk factors proved to be strong, largely independent predictors of cardiovascular disease (CVD). These factors—advancing age, cigarette smoking, blood pressure (particularly systolic), cholesterol in total serum and HDL, and diabetes—served as the basis for the development of risk prediction equations.1 If FHS risk estimates are to be widely used, they must apply widely in the US population. To document their transportability, they must be compared with prospective studies in other populations. Although the FHS is the longest running prospective study, there are other major studies. The cardiovascular end points of these other studies have varied. Some include cardiovascular morbidity and mortality; others have only cardiovascular mortality. Among the end points, CHD is the most extensively reported; for this reason, CHD was the primary focus of the workshop. ### Multivariate Relative Risk Comparisons In preparation for the workshop, multivariate regression coefficients for each risk factor were compared in different populations with those of the FHS. Adjusted relative risk estimates make it possible to determine whether each independent risk factor confers a similar or different relative risk among different …

Long-Term Safety of Dasatinib in Chinese Chronic Phase Chronic Myeloid Leukemia Patients with Imatinib-Resistance or -Intolerance: Results from a 6-Year Follow-up of a Multicenter Phase II Study

Dyspepsia and Irritable Bowel Syndrome After a Salmonella Gastroenteritis Outbreak: One-Year Follow-up Cohort Study

The objective of this paper is to investigate dynamic properties of age trajectories of physiological indices and their effects on mortality risk and longevity using longitudinal data on more than 5,000 individuals collected in biennial examinations of the Framingham Heart Study (FHS) original cohort during about 50 subsequent years of follow-up. We first performed empirical analyses of the FHS longitudinal data. We evaluated average age trajectories of indices describing physiological states for different groups of individuals and established their connections with mortality risk. These indices include body mass index, diastolic blood pressure, pulse pressure, pulse rate, level of blood glucose, hematocrit, and serum cholesterol. To be able to investigate dynamic mechanisms responsible for changes in the aging human organisms using available longitudinal data, we further developed a stochastic process model of human mortality and aging, by including in it the notions of “physiological norms,” “allostatic adaptation and allostatic load,” “stress resistance,” and other characteristics associated with the internal process of aging and the effects of external disturbances. In this model, the persistent deviation of physiological indices from their normal values contributes to an increase in morbidity and mortality risks. We used the stochastic process model in the statistical analyses of longitudinal FHS data. We found that different indices have different average age patterns and different dynamic properties. We also found that age trajectories of long-lived individuals differ from those of the shorter-lived members of the FHS original cohort for both sexes. Using methods of statistical modeling, we evaluated “normal” age trajectories of physiological indices and the dynamic effects of allostatic adaptation. The model allows for evaluating average patterns of aging-related decline in stress resistance. This effect is captured by the narrowing of the U-shaped mortality risk (considered a function of physiological state) with age. We showed that individual indices and their rates of change with age, as well as other measures of individual variability, manifested during the life course are important contributors to mortality risks. The advantages and limitations of the approach are discussed.

PC-FACS

The role of C-reactive protein (CRP) as a novel plasma marker of atherothrombotic disease is currently under investigation. Previous studies have mostly related CRP to coronary heart disease, were often restricted to a case-control design, and failed to include pertinent risk factors to evaluate the joint and net effect of CRP on the outcome. We related plasma CRP levels to incidence of first ischemic stroke or transient ischemic attack (TIA) in the Framingham Study original cohort. There were 591 men and 871 women free of stroke/TIA during their 1980 to 1982 clinic examinations, when their mean age was 69.7 years. CRP levels were measured by using an enzyme immunoassay on previously frozen serum samples. Analyses were based on sex-specific CRP quartiles. Risk ratios (RRs) were derived, and series of trend analyses were performed. During 12 to 14 years of follow-up, 196 ischemic strokes and TIAs occurred. Independent of age, men in the highest CRP quartile had 2 times the risk of ischemic stroke/TIA (RR=2.0, P=0.027), and women had almost 3 times the risk (RR=2.7, P=0.0003) compared with those in the lowest quartile. Assessment of the trend in risk across quartiles showed unadjusted risk increase for men (RR=1.347, P=0.0025) and women (RR=1.441, P=0.0001). After adjustment for smoking, total/HDL cholesterol, systolic blood pressure, and diabetes, the increase in risk across CRP quartiles remained statistically significant for both men (P=0.0365) and women (P=0.0084). Independent of other cardiovascular risk factors, elevated plasma CRP levels significantly predict the risk of future ischemic stroke and TIA in the elderly.

Analysis of longitudinal data with excessive zeros has gained increasing attention in recent years; however, current approaches to the analysis of longitudinal data with excessive zeros have primarily focused on balanced data. Dropouts are common in longitudinal studies; therefore, the analysis of the resulting unbalanced data is complicated by the missing mechanism. Our study is motivated by the analysis of longitudinal skin cancer count data presented by Greenberg, Baron, Stukel, Stevens, Mandel, Spencer, Elias, Lowe, Nierenberg, Bayrd, Vance, Freeman, Clendenning, Kwan, and the Skin Cancer Prevention Study Group[New England Journal of Medicine 323, 789-795]. The data consist of a large number of zero responses (83% of the observations) as well as a substantial amount of dropout (about 52% of the observations). To account for both excessive zeros and dropout patterns, we propose a pattern-mixture zero-inflated model with compound Poisson random effects for the unbalanced longitudinal skin cancer data. We also incorporate an autoregressive of order 1 correlation structure in the model to capture longitudinal correlation of the count responses. A quasi-likelihood approach has been developed in the estimation of our model. We illustrated the method with analysis of the longitudinal skin cancer data.

The Epidemiology of Hemoglobin Levels in Patients With Type 2 Diabetes

Analysis of binary longitudinal data with time-varying effects

Update This article was updated on February 6, 2019, because of a previous error. On page 105, in the subsection titled “Outcomes and Design” the sentence that had read “Furthermore, in a retrospective review, Houdek et al. 48 , at a mean follow-up of 8 years, demonstrated improved survivorship of 9,999 metal-backed compared with 1,645 all-polyethylene tibial components, over all age groups and most BMI categories” now reads “Furthermore, in a retrospective review, Houdek et al. 48 , at a mean follow-up of 8 years, demonstrated inferior survivorship of 9,999 metal-backed compared with 1,645 all-polyethylene tibial components, over all age groups and most BMI categories.” An erratum has been published: J Bone Joint Surg Am. 2019 Mar 20;101(6):e26.

Over the last 25 years, there has been an unprecedented increase in federal funding for large-scale longitudinal studies, many of which collect neuropsychological or neuroimaging outcome measures. These studies have collected data from thousands of study participants across multiple waves of data collection over many years. With the increased availability of longitudinal data, data sharing policies have become more liberal, thereby offering significant opportunities for interested researchers to carry out their own longitudinal research with these data. At the same time, these opportunities have stimulated new conceptualizations of longitudinal change and have led to the development of novel approaches toward analysis of longitudinal data. My aim is to review these new conceptualizations and novel data analytic approaches. In this article, I describe the state of the field a quarter century ago with respect to available longitudinal studies, and I trace the growth of federally funded longitudinal studies over the last 25 years by describing 18 of these projects, many of which are still collecting data. In the second part of this article, I describe changes in the methods used to analyze longitudinal data, transitioning from the paired t test and repeated measures ANOVA to latent change scores, linear mixed effects modeling, and latent growth curve models. Changes in the approach to management of missing data are also discussed. Future studies should abandon traditional longitudinal analytic methods in favor of contemporary approaches given their increased power, greater accuracy, and widespread availability. (PsycINFO Database Record

Objective To assess the efficacy and safety of interferon-beta (IFN-β) as monotherapy versus placebo for patients with relapsing-remitting multiple sclerosis (RRMS). Methods We searched Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, CINAHL, LILACS, PEDRO, China Biology Medicine Disc (CBMDisc), as well as clinical trial registries and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP, retrieval deadline: June 2014). Furthermore, we checked reference lists of published reviews and retrieved articles, and communicated personally with investigators and biotechnology companies participating in trials of IFN-β in an effort to identify further studies or unpublished data. Two review authors independently screened studies, extracted data and evaluated the risk of bias. Formal Meta-analysis were conducted by using Review Manager software (Version 5.3.3) and the impacts of limitations in study design or execution (risk of bias), inconsistency in results, imprecision of results, indirectness of evidence and publication bias on the quality of the body of evidence were assessed. Results A total of 576 articles were retrieved. After screening of titles and abstracts, 26 studies were provisionally selected. The full text of papers were obtained for further assessment of eligibility. Finally, 5 studies were included, involving 2129 patients with RRMS (high-dose IFN-β group: N = 1076; placebo group: N = 1053). All studies were randomized, double-blind, controlled, parallel-group clinical trials with a follow-up for at least one year, evaluating IFN-β versus placebo as monotherapy for patients with RRMS. Most studies had methodological limitations, mainly on a high risk of attrition bias. Moreover, the intention to treat (ITT) principle was not used in data analysis. Data from only 919 patients (43.17%) were available to calculate the primary outcomes at 2 years of follow-up. Meta-analysis indicated IFN-β slightly reduced the number of patients with at least one relapse [risk ratio (RR) = 0.810, 95%CI: 0.740-0.890; P = 0.000] and the number of patients with disability progression during the first 2 years of follow-up (RR = 0.700, 95%CI: 0.550-0.880; P = 0.002). However, the sensitivity analysis (worst-case scenario analysis) showed no treatment effect (RR = 1.110, 95%CI: 0.730-1.680, P = 0.620; RR = 1.310, 95%CI: 0.600-2.890, P = 0.500, respectively). Data from 1581 patients (74.26%) were available to analyze the number of patients with at least one relapse during the first year of follow-up (RR = 0.740, 95% CI: 0.590-0.930; P = 0.010). Absolute risk reduction (ARR) was 13.24% and number needed to treat (NNT) was 8, which meant 8 patients were needed to treat to prevent one patient against relapse. However, the pooled results showed no treatment effect on the annualized relapse rate. The adverse events frequently caused by IFN-β included injection-site reactions, chills, pyrexia, myalgia, influenza-like symptoms, headache, increased alanine aminotransferase (ALT) and increased aspartate aminotransferase (AST). However, the incidences of lymphocytopenia, leucopenia, depression and committed or attempted suicide were not significantly increased by IFN-β. Conclusions There is high-quality evidence to support that IFN-β slightly reduces the number of patients with RRMS having relapse during the first year of follow-up, but the clinical effect beyond one year is uncertain. There is insufficient evidence to determine the efficacy of IFN-β in reducing the number of patients with disability progression. New randomized controlled trials of high quality are needed to assess the long-term efficacy. doi: 10.3969/j.issn.1672-6731.2014.09.007

Oncological Outcomes After Radical Cystectomy for Bladder Cancer: Open Versus Minimally Invasive Approaches