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Aim: To determine the duration and the extension of the pro‐inflammatory response to hypoglycaemia both in people with type 1 diabetes and healthy controls. Materials and Methods: Adults with type 1 diabetes (n = 47) and matched controls (n = 16) underwent a hyperinsulinaemic‐euglycaemic hypoglycaemic (2.8 ± 0.1 mmoL/L [49.9 ± 2.3 mg/dL]) glucose clamp. During euglycaemia, hypoglycaemia, and 1, 3 and 7 days later, blood was drawn to determine immune cell phenotype, monocyte function and circulating inflammatory markers. Results: Hypoglycaemia increased lymphocyte and monocyte counts, which remained elevated for 1 week. The proportion of CD16+ monocytes increased and the proportion of CD14+ monocytes decreased. During hypoglycaemia, monocytes released more tumour necrosis factor‐α and interleukin‐1β, and less interleukin‐10, after ex vivo stimulation. Hypoglycaemia increased the levels of 19 circulating inflammatory proteins, including high sensitive C‐reactive protein, most of which remained elevated for 1 week. The epinephrine peak in response to hypoglycaemia was positively correlated with immune cell number and phenotype, but not with the proteomic response. Conclusions: Overall, despite differences in prior exposure to hypoglycaemia, the pattern of the inflammatory responses to hypoglycaemia did not differ between people with type 1 diabetes and healthy controls. In conclusion, hypoglycaemia induces a range of pro‐inflammatory responses that are sustained for at least 1 week in people with type 1 diabetes and healthy controls. [ABSTRACT FROM AUTHOR]

In a randomized controlled trial, outcomes of different subjects may be independent at baseline, but correlated at a follow‐up measurement due to treatment. This treatment‐related clustering at follow‐up can arise for instance because the treatment is given in a group or because subjects are treated individually but by the same therapist (therapist effect). There is substantial literature on the design and analysis of such trials when estimation of the intervention effect is based on a follow‐up measurement (eg, directly after treatment or at a later time point). However, often the baseline measurement of the outcome is highly correlated with the follow‐up measurement, and this information can be used in the analysis. For a randomized design with a baseline and a follow‐up measurement, we compare sample size requirements for analyses with and without adjustment for this baseline measure. We show that adjusting for baseline reduces required sample size. This reduction depends on the variance of the difference between arms at baseline, the variance of this difference at follow‐up, and the correlation between the two. From this, we derive sample size formulas for partially or fully nested designs, and cluster randomized trials with treatment as a partially or fully cross‐classified factor. Also, we discuss situations where clusters are already present at baseline or where treatment by cluster interaction is present. For the partially nested design, we work out practical design considerations (eg, use of content‐matter input, design factors and optimal allocation ratio) and investigate small sample properties of the sample size formula. [ABSTRACT FROM AUTHOR]

Background: Disease severity in spinocerebellar ataxia type 3 (SCA3) is commonly defined by the Scale for the Assessment and Rating of Ataxia (SARA) sum score, but little is known about the contributions and progression patterns of individual items.Objectives: To investigate the temporal dynamics of SARA item scores in SCA3 patients and evaluate if clinical and demographic factors are differentially associated with evolution of axial and appendicular ataxia.Methods: In a prospective, multinational cohort study involving 11 European and 2 US sites, SARA scores were determined longitudinally in 223 SCA3 patients with a follow-up assessment after 1 year.Results: An increase in SARA score from 10 to 20 points was mainly driven by axial and speech items, with a markedly smaller contribution of appendicular items. Finger chase and nose-finger test scores not only showed the lowest variability at baseline, but also the least deterioration at follow-up. Compared with the full set of SARA items, omission of both tests would result in lower sample size requirements for therapeutic trials. Sex was associated with change in SARA sum score and appendicular, but not axial, subscore, with a significantly faster progression in men. Despite considerable interindividual variability, the average annual progression rate of SARA score was approximately three times higher in subjects with a disease duration over 10 years than in those within 10 years from onset.Conclusion: Our findings provide evidence for a difference in temporal dynamics between axial and appendicular ataxia in SCA3 patients, which will help inform the design of clinical trials and development of new (etiology-specific) outcome measures. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We re‐analysed six clinical trials evaluating a new anti‐depression treatment. We selected the following analysis methods—ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed‐models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump‐to‐reference imputation; and pattern‐mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population‐level summaries between the estimands were 4–8 points. Not all six estimands had a clinically meaningful interpretation. Only a few analyses would target the same estimand, hence only few could be used as sensitivity analyses. The fact that an analysis could estimate different estimands emphasises the importance of prospectively defining the estimands targeting the primary objective of a trial. The fact that an estimand can be targeted by different analyses emphasises the importance of prespecifying precisely the estimator for the targeted estimand. [ABSTRACT FROM AUTHOR]

A study published in the journal Diabetes, Obesity & Metabolism examined the impact of sedentary behavior on glucose concentration in individuals with type 1 diabetes. The study found that increased sitting time and long sedentary bouts were positively associated with higher same-day mean glucose levels. However, sedentary breaks did not show a significant association with glucose levels. The study suggests that reducing sitting time may be an effective strategy for improving glucose control in individuals with type 1 diabetes. The findings highlight the importance of physical activity in managing diabetes, although further research is needed to confirm these results. [Extracted from the article]

There is a key problem in randomised clinical trials as outcomes can be distorted due to informative post‐randomisation events. This is inadequately addressed by the use of traditional intention‐to‐treat or per protocol analysis sets and often either ignored or wrongly labelled as missing data. As a consequence, the treatment effects of interest in a clinical trial are not well defined and their estimates might be misinterpreted. The estimand framework should help all those planning, conducting and analysing clinical trials as well as those interpreting the results to better define, estimate and understand the treatment effects of interest. This framework is described in the addendum to ICH E9 and addresses precisely this problem. It is relevant for regulatory drug trials and academic‐run trials, as well as for trials of nonpharmacological interventions. [ABSTRACT FROM AUTHOR]

Dynamic risk predictions based on all available information are useful in timely identification of high‐risk patients. However, in contrast with time to event outcomes, there is still a lack of studies that clearly demonstrate how to obtain and update predictions for a future binary outcome using a repeatedly measured biomarker. The aim of this study is to give an illustrative overview of four approaches to obtain such predictions: likelihood based two‐stage method (2SMLE), likelihood based joint model (JMMLE), Bayesian two‐stage method (2SB), and Bayesian joint model (JMB). We applied the approaches to provide weekly updated predictions of post–molar gestational trophoblastic neoplasia (GTN) based on age and repeated measurements of human chorionic gonadotropin (hCG). Discrimination and calibration measures were used to compare the accuracy of the weekly predictions. Internal validation of the models was conducted using bootstrapping. The four approaches resulted in the same predictive and discriminative performance in predicting GTN. A simulation study showed that the joint models outperform the two‐stage methods when we increase the within‐ and the between‐patients variability of the biomarker. The applicability of these models to produce dynamic predictions has been illustrated through a comprehensive explanation and accompanying syntax (R and SAS®). [ABSTRACT FROM AUTHOR]

Subgroup analyses are an essential part of fully understanding the complete results from confirmatory clinical trials. However, they come with substantial methodological challenges. In case no statistically significant overall treatment effect is found in a clinical trial, this does not necessarily indicate that no patients will benefit from treatment. Subgroup analyses could be conducted to investigate whether a treatment might still be beneficial for particular subgroups of patients. Assessment of the level of evidence associated with such subgroup findings is primordial as it may form the basis for performing a new clinical trial or even drawing the conclusion that a specific patient group could benefit from a new therapy. Previous research addressed the overall type I error and the power associated with a single subgroup finding for continuous outcomes and suitable replication strategies. The current study aims at investigating two scenarios as part of a nonconfirmatory strategy in a trial with dichotomous outcomes: (a) when a covariate of interest is represented by ordered subgroups, eg, in case of biomarkers, and thus, a trend can be studied that may reflect an underlying mechanism, and (b) when multiple covariates, and thus multiple subgroups, are investigated at the same time. Based on simulation studies, this paper assesses the credibility of subgroup findings in overall nonsignificant trials and provides practical recommendations for evaluating the strength of evidence of subgroup findings in these settings. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effects of non‐mandatory transitioning from the originator biologic drug etanercept (ETN) to its biosimilar, SB4, on drug survival and effectiveness in a controlled cohort study of patients with an inflammatory rheumatic disease. Methods: In 2016, 642 patients were asked to transition their treatment from originator ETN to biosimilar SB4 by a structured communication strategy with opt‐out option. Patients who consented to switch to SB4 were considered eligible for inclusion in the transition cohort, while patients being treated with originator ETN in 2014 were recruited as the historical cohort. Drug survival was compared between the 2 cohorts using Cox regression analyses, which were adjusted for age, sex, diagnosis, ETN treatment duration, ETN dose interval, conventional synthetic disease‐modifying antirheumatic drug usage, and C‐reactive protein (CRP) level, with a robust variance estimator applied to account for repeated subjects (i.e., patients who were included in both the transition cohort and the historical cohort). Adjusted differences in the 6‐month change in CRP level, Disease Activity Score in 28 joints using CRP level (DAS28‐CRP), and Bath Ankylosing Spondylitis Disease Activity Index were also assessed. Results: Of the 642 ETN‐treated patients, 635 (99%) agreed to transition from originator ETN to biosimilar SB4, of whom 625 patients (433 with rheumatoid arthritis, 128 with psoriatic arthritis, and 64 with ankylosing spondylitis) were included in the transition cohort, and 600 ETN‐treated patients from 2014 were included in the historical cohort. The crude treatment persistence rate for biosimilar SB4 over 6 months was 90% (95% confidence interval [95% CI] 88–93%), compared to a 6‐month treatment persistence rate of 92% (95% CI 90–94%) for originator ETN. Patients in the transition cohort, compared to the historical cohort, had a statistically significantly higher relative risk of treatment discontinuation (adjusted hazard ratio 1.57, 95% CI 1.05–2.36) and showed smaller decreases in the CRP level (adjusted difference 1.8, 95% CI 0.3–3.2) and DAS28‐CRP (adjusted difference 0.15, 95% CI 0.05–0.25) over 6 months. Conclusion: Non‐mandatory transitioning from originator ETN to biosimilar SB4 using a specifically designed communication strategy resulted in a slightly lower 6‐month treatment persistence rate and smaller decreases in disease activity in the transition cohort compared to the historical cohort, but these differences were not considered clinically relevant. [ABSTRACT FROM AUTHOR]

In drug development, it sometimes occurs that a new drug does not demonstrate effectiveness for the full study population but appears to be beneficial in a relevant subgroup. In case the subgroup of interest was not part of a confirmatory testing strategy, the inflation of the overall type I error is substantial and therefore such a subgroup analysis finding can only be seen as exploratory at best. To support such exploratory findings, an appropriate replication of the subgroup finding should be undertaken in a new trial. We should, however, be reasonably confident in the observed treatment effect size to be able to use this estimate in a replication trial in the subpopulation of interest. We were therefore interested in evaluating the bias of the estimate of the subgroup treatment effect, after selection based on significance for the subgroup in an overall 'failed' trial. Different scenarios, involving continuous as well as dichotomous outcomes, were investigated via simulation studies. It is shown that the bias associated with subgroup findings in overall nonsignificant clinical trials is on average large and varies substantially across plausible scenarios. This renders the subgroup treatment estimate from the original trial of limited value to design the replication trial. An empirical Bayesian shrinkage method is suggested to minimize this overestimation. The proposed estimator appears to offer either a good or a conservative correction to the observed subgroup treatment effect hence provides a more reliable subgroup treatment effect estimate for adequate planning of future studies. [ABSTRACT FROM AUTHOR]

Background People with intellectual disabilities are vulnerable to develop psychopathology (in particular anxiety) and related challenging behaviour. A diagnostic guideline was developed to support professionals to better diagnose and thus treat psychopathology and related CB. This study examined preliminary outcomes from the application of this guideline. Method A comparative multiple case study with an experimental and a control condition. Results The application of the guideline showed a trend of decreases of internalizing problems ( P = 0.07) and anxiety/depressed problems ( P = 0.09). We found no statistically significant decreases of externalizing problems and no increases in perceived quality of life as compared with care as usual. Clients were not more satisfied with the support they received for coping with their emotional and behaviour problems. Conclusion The application of the Diagnostic Guideline for Anxiety and Challenging Behaviour did not show statistically significant changes in externalizing problems and Quality of Life. Despite the small sample size of n = 59, we did find a trend in decreasing internalizing problems and anxiety/depressed problems. Further research into either or not confirming these trends is recommended. [ABSTRACT FROM AUTHOR]

The sample size required for a cluster randomised trial is inflated compared with an individually randomised trial because outcomes of participants from the same cluster are correlated. Sample size calculations for longitudinal cluster randomised trials (including stepped wedge trials) need to take account of at least two levels of clustering: the clusters themselves and times within clusters. We derive formulae for sample size for repeated cross-section and closed cohort cluster randomised trials with normally distributed outcome measures, under a multilevel model allowing for variation between clusters and between times within clusters. Our formulae agree with those previously described for special cases such as crossover and analysis of covariance designs, although simulation suggests that the formulae could underestimate required sample size when the number of clusters is small. Whether using a formula or simulation, a sample size calculation requires estimates of nuisance parameters, which in our model include the intracluster correlation, cluster autocorrelation, and individual autocorrelation. A cluster autocorrelation less than 1 reflects a situation where individuals sampled from the same cluster at different times have less correlated outcomes than individuals sampled from the same cluster at the same time. Nuisance parameters could be estimated from time series obtained in similarly clustered settings with the same outcome measure, using analysis of variance to estimate variance components. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

AIMS The aim of the present study was to investigate whether differences in total and peak drug exposure upon generic substitution are due to differences between formulations or to intrasubject pharmacokinetic variability of the active substance. Methods The study was designed as a retrospective reanalysis of existing studies. Nine replicate design bioequivalence studies representing six drug classes - i.e. for alendronate, atorvastatin, cyclosporin, ebastine, exemestane, mycophenolate mofetil, and ropinirole - were retrieved from the Dutch Medicines Regulatory Authority. Results In most studies, the intrasubject variability in total and peak drug exposure was comparable for the brand-name [in the range 0.01-0.24 for area under the concentration-time curve (AUCt) and 0.02-0.29 for peak plasma concentration (Cmax) on a log scale] and generic (0.01-0.23 for AUCt and 0.08-0.33 for Cmax) drugs, and was comparable with the intrasubject variability upon switching between those drugs (0.01-0.23 for AUCt and 0.06-0.33 for Cmax). The variance related to subject-by-formulation interaction could be considered negligible (-0.069 to 0.047 for AUCt and -0.091 to 0.02 for Cmax). Conclusion In the investigated studies, the variation in total and peak exposure seen when a patient is switched from a brand-name to a generic drug is comparable with that seen following repeated administration of the brand-name drug in the patient. Only the intrasubject variability seems to play a crucial and decisive role in the variation in drug exposure seen; no additional formulation-dependent variation in exposure is observed upon switching. Thus, our data support that, for the medicines that were included in the present investigation, from a clinical pharmacological perspective, the benefit-risk balance of a generic drug is comparable with that of the brand-name drug. [ABSTRACT FROM AUTHOR]

SUMMARY In statistical and biometric sciences, one often uses predictive linear models. The initial form of such models is usually obtained by fitting the coefficients of the model to a set of observed data according to the classical least squares method. Newborn models that are obtained in this way will be referred to as raw models. Such raw models are often subject of efforts to improve them as to their predictive performance on external datasets. Several methods can be followed to fine-tune raw models, thus leading to a variety of model building strategies. In this paper, the idea of so-called victory rates is introduced to compare the performance of building strategies mutually.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Next to severely decreased pump flow, hypovolemia in extracorporeal life support ( ELS) can result in subatmospheric venous line pressure. Such pressure may lead to degassing and resultant gaseous microemboli ( GME), with potential changes in neurological clinical outcome. CME activity resulting from degassing was investigated in relation to subatmospheric venous line pressure, partial oxygen pressure ( pO2), and hematocrit in a model of a centrifugal pump-based circuit for long-term ELS. Additionally, a device that provides instantaneous volume buffer capacity during hypovolemia was evaluated in relation to GME appearance. An exponential relationship was found between decreasing venous line pressure and GME downstream of the centrifugal pump ( P = 0.001). Arterial bubble activity appeared at subatmospheric venous line pressures of −200 mm Hg and less. A rising ( pO2) increased formation of GME ( P = 0.05). A rise in hematocrit, in contrast, did not affect embolic activity ( P = 0.22). With simulated hypovolemia, volume buffer capacity added to the venous line dampened fluctuations of venous line pressure by approximately 40%, but a significant reduction in GME formation could not be found ( P = 0.22). Moreover, the device enabled a 14% higher support flow. With ELS flow being related to patient volume status, hypovolemia can diminish support. A coherent decrease of venous line pressure triggers degassing of blood-dissolved gases and causes arterial GME, which can become massive during persistent conditions of limited venous return. Incorporation of a volume buffer capacity device into the extracorporeal support circuit enables a higher and more stable support flow in critically low patient filling. [ABSTRACT FROM AUTHOR]

Objective This study aims to test the accuracy of the Nijmegen Observer-Rated Depression (NORD) scale, a new short scale for screening of depression in nursing home (NH) residents with and without dementia. Methods This cross-sectional study with 103 residents with dementia ( N = 19 depressed) and 72 residents without dementia ( N = 10 depressed) was undertaken in 13 Dutch NH units. An elderly care physician and a psychologist of each unit assessed residents for the presence of clinical depression. Primary professional caregivers administered the NORD scale. Results Five of the six proposed items showed acceptable performance in screening for depression. Receiver operating characteristic analyses revealed significant areas under the empirical curve (AUC) for the 5-item NORD scale in the total sample ( AUC = 0.83, p < 0.001), as well as in residents with dementia ( AUC = 0.84, p < 0.001) and without dementia ( AUC = 0.84, p < 0.001). The cutoff score of >1 showed the highest sum of sensitivity (100) and specificity (69) in non-dementia and >2 the highest sum of sensitivity (79) and specificity (77) in dementia. The cutoff score of >1 showed the lowest negative likelihood ratio of 0.0 in non-dementia and of 0.2 in dementia. The highest positive likelihood ratios were found for the cutoff of >2 in non-dementia (3.4) and for >4 in dementia (26.5). Conclusion The 5-item NORD scale showed acceptable accuracy comparable with those of more extensive scales in other studies. It is easy and quick to administer and can be used for screening of depression in NH residents with or without dementia. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An analysis of covariance (ANCOVA) using both the baseline and follow-up score of the outcome will then have more power. We calculate the efficiency of an ANCOVA analysis using the baseline scores compared with an analysis on follow-up scores only. The sample size for such an ANCOVA analysis is a factor r2 smaller, where r is the correlation of the cluster means between baseline and follow-up. This correlation can be expressed in clinically interpretable parameters: the correlation between baseline and follow-up of subjects (subject autocorrelation) and that of clusters (cluster autocorrelation). Because of this, subject matter knowledge can be used to provide (range of) plausible values for these correlations, when estimates from previous studies are lacking. Depending on how large the subject and cluster autocorrelations are, analysis of covariance can substantially reduce the number of clusters needed. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

van der Vliet JA, Warlé MC, Cheung CLS, Teerenstra S, Hoitsma AJ. Influence of prolonged cold ischemia in renal transplantation. Clin Transplant 2011: 25: E612-E616. © 2011 John Wiley & Sons A/S. Abstract: Aim: To determine to what extent current cold ischemia times (CITs) affect the results of renal transplantation in the Netherlands. Methods: Retrospective survey of the Dutch Organ Transplant Registry concerning transplants from deceased donors between 1990 and 2007. Results: A total of 6322 recipients were identified, of whom 5306 received a kidney from deceased heartbeating (HBD) and 1016 from donors after cardiac death (DCD). Mean CIT was 24.0 ± 7.9 h in HBD and 21.6 ± 6.7 h in DCD. The percentage delayed graft function (DGF) was 12.3 and 50.4, respectively (p < 0.001). Primary non-function (PNF) occurred in, respectively, 1.7% and 5.0% (p < 0.001). Serum creatinine after three months was 166 μM in HBD and 213 μM in DCD (p < 0.001). Five-yr graft survival was 79.5% and 78.3%, respectively (p = ns). In multivariate analysis, CIT proved to be an independent risk factor for DGF and PNF. Shorter CIT was associated with better graft survival in both groups with a hazard ratio of 1.024 (1.011-1.037, 95% CI)/h. CIT <20 h was associated with a graft survival benefit of 3% after five yr in HBD and CIT of <16 h with a benefit of 10% in DCD. Conclusions: Longer CITs are associated with the occurrence of DGF, PNF and decreased graft survival in the Netherlands. [ABSTRACT FROM AUTHOR]

In some clinical trials, treatment allocation on a patient level is not feasible, and whole groups or clusters of patients are allocated to the same treatment. If, for example, a clinical trial is investigating the efficacy of various patient coaching methods and randomization is done on a patient level, then patients who are receiving different methods may come into contact with each other and influence each other. This would create contamination of the treatment effects. Such bias might be prevented by randomization on the coaches level. The patients of a coach constitute a cluster and all the subjects in that cluster receive the same treatment. Disadvantages of this approach may be reduced statistical efficiency and recruitment bias, as the treatment that a subject will receive is known in advance. Pseudo cluster randomization avoids this, because in pseudo cluster randomization, not everybody in a certain cluster receives the same treatment, just the majority. There are two groups of clusters: in one group the majority of subjects receive treatment A, while a limited number receive treatment B. In the other group of clusters the proportions are reversed. The statistical properties of this method are described. When contamination is present, the method appears to be more efficient than randomization on a patient level or on a cluster level. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: To assess the diagnostic accuracy of a single-lead portable ECG device for measuring QTc-intervals in comparison with a standard 12-lead ECG.Methods: Adult patients visiting the cardiology outpatient clinic for a 12-lead recording were also measured with a portable single-lead ECG recorder (HeartcheckTM). QTc-intervals were determined by two cardiologists. Perfect agreement was defined as a limit of ≤10 ms between the two measurement methods.Results: Hundred one ECGs were recorded. QTc-interval mean differences between the two measurement methods was substantially outside our definition of perfect agreement (-31.9 [SD±41.3] ms).Conclusion: In conclusion, the Heartcheck single-lead ECG device is not accurate for measuring QTc-intervals. [ABSTRACT FROM AUTHOR]