Circulating Cardiac Troponin T Exhibits a Diurnal Rhythm

ObjectivesThe goal of this study was to test the unverified assumption that chronically elevated cardiac troponin T (cTnT) levels fluctuate randomly around a homeostatic set point.BackgroundThe introduction of high-sensitivity cardiac troponin (cTn) assays has improved sensitivity for acute myocardial infarction (AMI). However, many patients with a single positive cTn test result do not have AMI. Therefore, the diagnosis of AMI relies strongly on serial testing and interpretation of cTn kinetics. Essential in this regard is a profound understanding of the biological variation of cTn.MethodsTwo studies were conducted to assess biological cTnT variation and to investigate the presence of a diurnal rhythm of cTnT. Study 1 comprised 23 male subjects with type 2 diabetes, with no acute cardiovascular disease. Serial venous blood samples were drawn over an 11-h period (8:30 am to 7:30 pm). In study 2, the presence of a diurnal cTnT rhythm was investigated by hourly sampling of 7 subjects from study 1 over 25 h.ResultsIn study 1, we observed a gradual decrease in cTnT concentrations during the day (24 ± 2%). This decrease was present in all participants and was most prominent in subjects with the highest baseline cTnT values (Pearson’s R 0.93). Diurnal variation of cTnT, as assessed in study 2, was characterized by peak concentrations during morning hours (8:30 am, 17.1 ± 2.9 ng/l), gradually decreasing values during daytime (8:30 pm, 11.9 ± 1.6 ng/l), and rising concentrations during nighttime (8:30 am the next day, 16.9 ± 2.8 ng/l).ConclusionsA diurnal cTnT rhythm substantiates the recommendation that all dynamic changes in cTnT should be interpreted in relation to the clinical presentation. Epidemiological studies and risk-stratification protocols with the use of cTnT may benefit from standardized sampling times. (Exercise and Glycemic Control in Type 2 Diabetes; NCT00945165

Maximin D-optimal designs for binary longitudinal responses

Optimal design problems for logistic mixed effects models for binary longitudinal responses are considered. A function of the approximate information matrix under the framework of the Penalized Quasi Likelihood (PQL) and a generalized linear mixed model with autocorrelation is optimized. Locally D-optimal designs are computed. Maximin D-optimal designs are considered to overcome the problem of parameter value dependency of the D-optimal designs. The results show that the optimal number of repeated measurements depends on the number of regression parameters in the model. The performance of the maximin D-optimal designs in terms of the maximin efficiency (MME) is high for a range of parameter values that is common in practice. The design locations for mixed-effects logistic models generally shift to the left as compared to the design locations for general linear mixed-effects models known in the literature.

Cost-efficiency considerations in the choice of a microarray platform for time course experimental designs

Customarily, the choice between one- or two-colour microarray platforms is based on their respective practical and technical merits, contingent on objectives and constraints of the study at stake. Statistical efficiency, if accounted for, plays a secondary role. A cost-efficiency comparison of the one- and two-colour designs for a 2x4 time course experiment was conducted. It is shown that differences in costs between the platforms' designs, once adjusted for statistical efficiency, are not always negligible. The extent of these differences is largely influenced by subjects and array prices as well as by biological and error variances in their relative magnitude. Circumstances are described, in which cost-efficiency considerations will have an added value in motivating the selection of a microarray platform at the design stage.One- and two-colour microarray designs Microarray cost-efficiency Time course experiment Random intercept model Platform comparison

Imputation of missing covariate in randomized controlled trials with a continuous outcome: Scoping review and new results

In this article, we first review the literature on dealing with missing values on a covariate in randomized studies and summarize what has been done and what is lacking to date. We then investigate the situation with a continuous outcome and a missing binary covariate in more details through simulations, comparing the performance of multiple imputation (MI) with various simple alternative methods. This is finally extended to the case of time-to-event outcome. The simulations consider five different missingness scenarios: missing completely at random (MCAR), at random (MAR) with missingness depending only on the treatment, and missing not at random (MNAR) with missingness depending on the covariate itself (MNAR1), missingness depending on both the treatment and covariate (MNAR2), and missingness depending on the treatment, covariate and their interaction (MNAR3). Here, we distinguish two different cases: (1) when the covariate is measured before randomization (best practice), where only MCAR and MNAR1 are plausible, and (2) when it is measured after randomization but before treatment (which sometimes occurs in nonpharmaceutical research), where the other three missingness mechanisms can also occur. The proposed methods are compared based on the treatment effect estimate and its standard error. The simulation results suggest that the patterns of results are very similar for all missingness scenarios in case (1) and also in case (2) except for MNAR3. Furthermore, in each scenario for continuous outcome, there is at least one simple method that performs at least as well as MI, while for time-to-event outcome MI is best

Imputation of missing covariate in randomized controlled trials with a continuous outcome:Scoping review and new results

In this article, we first review the literature on dealing with missing values on a covariate in randomized studies and summarize what has been done and what is lacking to date. We then investigate the situation with a continuous outcome and a missing binary covariate in more details through simulations, comparing the performance of multiple imputation (MI) with various simple alternative methods. This is finally extended to the case of time-to-event outcome. The simulations consider five different missingness scenarios: missing completely at random (MCAR), at random (MAR) with missingness depending only on the treatment, and missing not at random (MNAR) with missingness depending on the covariate itself (MNAR1), missingness depending on both the treatment and covariate (MNAR2), and missingness depending on the treatment, covariate and their interaction (MNAR3). Here, we distinguish two different cases: (1) when the covariate is measured before randomization (best practice), where only MCAR and MNAR1 are plausible, and (2) when it is measured after randomization but before treatment (which sometimes occurs in nonpharmaceutical research), where the other three missingness mechanisms can also occur. The proposed methods are compared based on the treatment effect estimate and its standard error. The simulation results suggest that the patterns of results are very similar for all missingness scenarios in case (1) and also in case (2) except for MNAR3. Furthermore, in each scenario for continuous outcome, there is at least one simple method that performs at least as well as MI, while for time-to-event outcome MI is best

Quality of care and quality of life of people with dementia living at green care farms: a cross-sectional study

Abstract Background Many countries are introducing smaller, more home-like care facilities that represent a radically new approach to nursing home care for people with dementia. The green care farm is a new type of nursing home developed in the Netherlands. The goal of this study was to compare quality of care, quality of life and related outcomes in green care farms, regular small-scale living facilities and traditional nursing homes for people with dementia. Methods A cross-sectional design was used. Three types of nursing homes were included: (1) green care farms; (2) regular small-scale living facilities; (3) traditional nursing homes. All participating nursing homes were non-profit, collectively funded nursing homes in the south of the Netherlands. One hundred and fifteen residents with a formal diagnosis of dementia were included in the study. Data on quality of care was gathered and consisted of outcome indicators (e.g. falling incidents, pressure ulcers), structure indicators (e.g. hours per resident per day), and process indicators (e.g. presence, accessibility and content of protocols on care delivery). Furthermore, questionnaires on cognition, dependence in activities of daily living, quality of life, social engagement, neuropsychiatric symptoms, agitation, and depression were used. Results Data showed that quality of care was comparable across settings. No large differences were found on clinical outcome measures, hours per resident per day, or process indicators. Higher quality of life scores were reported for residents of green care farms in comparison with residents of traditional nursing homes. They scored significantly higher on the Quality of Life – Alzheimer’s disease Scale (p  0.7). No differences with regular small-scale living facilities were found. Conclusions Green care farms seem to be a valuable alternative to existing nursing homes. This is important as people with dementia are a heterogeneous group with varying needs. In order to provide tailored care there also is a need for a variety of living environments

Does the workplace-smoking ban eliminate differences in risk for environmental tobacco smoke exposure at work?

Objectives A workplace-smoking ban in the Netherlands was introduced on January 1, 2004. Before the ban male and low educated employees were at higher risk for exposure to environmental tobacco smoke (ETS). Effective implementation of the ban should result not only in an overall decline of exposure, but also in the disappearance of systematic differences in exposure between subgroups of employees.Methods Data from a Dutch continuous Internet survey were used. From July 2003 through June 2005, 200 respondents were randomly selected each week. The sample consisted of 11,291 non-smoking, working respondents, aged 16-65 years.Results ETS exposure decreased among all employees and among subgroups at higher risk before the ban. However, also after the ban, males and low educated employees were still most likely to be exposed to ETS.Conclusions The workplace-smoking ban was effective in reducing ETS exposure among employees. However, after the ban still 52.2% of non-smoking workers reported to be exposed. We did not find the expected stronger effect among employees who were at higher risk. Both before and after implementation of the ban, males and lower educated employees were about two times more likely to be exposed to ETS.Environmental tobacco smoke pollution Legislation Employees Occupational groups