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In this paper, we extend the work of Pimentel et al. (2015) and propose an adjusted estimator of Kendall's $\tau$ for bivariate zero-inflated count data. We provide achievable lower and upper bounds of our proposed estimator and show its relationship with current literature. In addition, we also suggest an estimator of the achievable bounds, thereby helping practitioners interpret the results while working with real data. The performance of the proposed estimator for Kendall's $\tau$ is unbiased with smaller mean squared errors compared to the unadjusted estimator of Pimentel et al. (2015). Our results also show that the bound estimator can be used when knowledge of the marginal distributions is lacking.

INTRODUCTION: Palpitations occurring in specific body positions are often reported by patients, but the effect of body position on arrhythmia has received little research attention. We hypothesize that resting body position can exert pro-arrhythmogenic effects in various ways. For example, lateral body position is known to increase change atrial and pulmonary vein dimensions.METHODS: This observational study capitalizes on overnight polysomnography (PSG) recordings from a tertiary sleep clinic. PSGs were retrieved based on any mention of cardiac arrhythmia in the clinical report, irrespective of primary sleep diagnosis or (cardiac) comorbidities. Every instance of atrial ectopy was annotated and subgroups with a homogenous rate of atrial ectopy were created based on the Dunn index. A generalized linear mixed-effects model using age, sex, gender, sleep stage and body position was used to analyse the total amount of atrial ectopy in each combination of sleep stage and body position. Backward elimination was then performed to select the best subset of variables for the model. Presence of a respiratory event was then added to the model for the subgroup with a high atrial ectopy rate.RESULTS: PSGs of 22 patients (14% female, mean age 61y) were clustered and analysed. Body position, sleep stage, age or sex did not have a significant effect on atrial ectopy in the subgroup with a low rate of atrial ectopy (N = 18). However, body position did significantly affect the rate of atrial ectopy in the subgroup with a high rate of atrial ectopy (N = 4; 18%). Respiratory events significantly altered the atrial ectopy rate in only three body positions across two patients.DISCUSSION: In each individual with a high rate of atrial ectopy, the rate of atrial ectopy was significantly higher in either left or right decubital or supine position. Increase in atrial wall stretch in lateral decubital position and obstructive respiratory events in positional sleep apnea are two possible pathophysiological mechanisms, while avoidance of a body position due to symptomatic atrial ectopy in that position is an important limitation.CONCLUSION: In a selected cohort of patients with a high rate of atrial ectopy during overnight polysomnography, the occurrence of atrial ectopy is related to resting body position.

Pearson’s correlation coefficient is considered a measure of linear association between bivariate random variables X and Y. It is recommended not to use it for other forms of associations. Indeed, for nonlinear monotonic associations alternative measures like Spearman’s rank and Kendall’s tau correlation coefficients are considered more appropriate. These views or opinions on the estimation of association are strongly rooted in the statistical and other empirical sciences. After defining linear and monotonic associations, we will demonstrate that these opinions are incorrect. Pearson’s correlation coefficient should not be ruled out a priori for measuring nonlinear monotonic associations. We will provide examples of practically relevant families of bivariate distribution functions with nonlinear monotonic associations for which Pearson’s correlation is preferred over Spearman’s rank and Kendall’s tau correlation in testing the dependency between X and Y. Alternatively, we will provide a family of bivariate distributions with a linear association between X and Y for which Spearman’s rank and Kendall’s tau are preferred over Pearson’s correlation. Our examples show that existing views on linear and monotonic associations are myths.

Age-specific reference values are important in medical science to evaluate the normal ranges of subjects and to help physicians signal potential disorders as early as possible. They are applied to ...

IntroductionCardiorespiratory monitoring is used in the neonatal intensive care unit (NICU) to assess the clinical status of newborn infants and detect critical deteriorations in cardiorespiratory function. Currently, heart rate (HR) is monitored by electrocardiography (ECG) and respiration by chest impedance (CI). Disadvantages of current monitoring techniques are usage of wired adhesive electrodes which may damage the skin and hinder care. The Bambi® belt is a wireless and non-adhesive alternative that enables cardiorespiratory monitoring by measuring electrical activity of the diaphragm via transcutaneous electromyography. A previous study showed feasibility of the Bambi® belt and this study compares the belt performance to ECG and CI.Methods and analysisThis multicentre non-inferiority paired study will be performed in the NICU of the Máxima Medical Center (MMC) in Veldhoven and the Emma Children’s Hospital, Amsterdam University Medical Centre (AmsterdamUMC) in Amsterdam, The Netherlands. 39 infants in different postmenstrual age groups (minimally 10 infants32 weeks) will be recruited. These infants will be monitored with the Bambi® belt in addition to standard ECG and CI for 24 hours. The primary outcome is the HR, studied with three criteria: (1) the limits of agreement of the HR measurements in terms of the second-to-second difference in the HR between the belt and standard ECG, (2) the detection of cardiac events consisting of bradycardia and tachycardia and (3) the quality of HR-monitoring. The secondary outcome is the respiratory rate (RR), studied with the criteria (1) agreement in RR-trend monitoring, (2) apnoea and tachypnoea detection and (3) reliable registrations.Ethics and disseminationThis protocol was approved by the Medical Ethical Committee of the MMC and the Central Committee for Human Research. The MMC started patient recruitment in July and the AmsterdamUMC in August 2021. The results will be presented at conferences and published in peer-reviewed journals.Trial registration numberNL9480.

AIM: To address alarm fatigue, a new alarm management system which ensures a quicker delivery of alarms together with waveform information on nurses' handheld devices was implemented and settings optimised. The effects of this clinical implementation on alarm rates and nurses' responsiveness were measured in an 18-bed single family rooms neonatal intensive care unit (NICU).METHODS: The technical implementation of the alarm management system was followed by clinical workflow optimisation. Alarms and vital parameters from October 2017 to December 2019 were analysed. Measures included monitoring alarms, nurses' response to alarms and time spent by patients in different saturation ranges. A survey among nurses was performed to evaluate changes in alarm rate and use of protocols.RESULTS: A significant reduction of monitoring alarms per patient days was detected after the optimisation phase (in particular for SpO2 ≤ 80%, P < .001). More time was spent by infants within the optimal peripheral oxygen saturation range (88% < SpO2 < 95%, P < .001). Results from the surveys showed that false alarms are less likely to cause an inappropriate response after the optimisation phase.CONCLUSION: The implementation of an alarm management solution and an optimisation programme can safely reduce the alarm burden inside of the NICU environment.

A bivariate zero-inflated Poisson control chart for rare events in an industrial process was presented in the paper of Fatahi et al. (2012). They used a specific copula to address the dependency between the bivariate counts. The copula that they provided did not satisfy the criteria for a copula when the correlation parameter is negative and they did not calculate the upper control limit for the sum of events correctly. We provide proper copula’s and upper control limits and also discuss their choice of parameter estimators. We illustrate our results with simulations.

Vital signs are used in Neonatal Intensive Care Units (NICUs) to monitor the state of multiple patients at once. Alarms are triggered if a vital sign is below/above a predefined threshold. Numerous alarms sound each hour which could translate into an overload for the medical team, known as alarm fatigue. Yet many of these alarms do not require immediate clinical action of the caregivers.In this paper we automatically detect moments that need an immediate response (i.e. interaction with the patient) of the medical team in NICUs by using caregiver response to the patient, which is based on the interpretation of vital signs and of nonverbal cues (e.g. movements) delivered by patients. The ultimate goal of such approach is to reduce the overload of alarms while maintaining the patient safety.We use features extracted from the electrocardiogram (ECG) and pulse oxymetry (SpO2) sensors of the patient, as most unplanned interactions between patient and caregivers are due to deteriorations. Since in our unit an alarm can only be paused or silenced manually at the bedside, we used this information as a prior for caregiver response. We also propose different labeling schemes for classification, each representative of a possible interaction scenario within the nature of our problem.We accomplished a general detection of caregiver response with a mean AUC of 0.82. We also show that when trained only with stable and truly deteriorating (critical state) samples, the classifiers can better learn the difference between alarms that need no immediate response and those that do. In addition, we present an analysis of the posterior probabilities over time for different labeling schemes, and use it to speculate about the reasons behind some failure cases.

Background To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. Methods We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. Results The change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). Conclusions There seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.

Additional file 1. Supplementary_Postetal_How did governmental interventions affect the spread of COVID-19 in European countries_BMC_vDec2020.pdf– Supplementary Appendix – A: Data sources governmental interventions, B: Discrete time Kolmogorov equations, C: Note on the effective reproduction number, D: Results sensitivity analysis, E: Note on the fraction infected tested and F: an overview of the Google mobility data.

Regression based methods for the detection of publication bias in meta-analysis have been extensively evaluated in literature. When dealing with continuous outcomes, specific hidden factors (e.g., heteroscedasticity) may interfere with the test statistics. In this paper we investigate the influence of residual heteroscedasticity on the performance of four tests for publication bias: the Egger test, the Begg-Mazumdar test and two tests based on weighted regression. In the presence of heteroscedasticity, the Egger test and the weighted regression tests highly inflate the Type I error rate, while the Begg-Mazumdar test deflates the Type I error rate. Although all three tests already have low statistical power, heteroscedasticity typically reduces it further. Our results in combination with earlier discussions on publication bias tests lead us to conclude that application of these tests on continuous treatment effects is not warranted.

Coronavirus disease 2019 (COVID-19) is an unprecedented and fast evolving pandemic, which has caused a large number of critically ill patients and deaths globally. It is an acute public health crisis leading to overloaded critical care capacity. Timely prediction of the clinical outcome (death/survival) of hospital-admitted COVID-19 patients can provide early warnings to clinicians, allowing improved allocation of medical resources. In a recently published paper, an interpretable machine learning model was presented to predict the mortality of COVID-19 patients with blood biomarkers, where the model was trained and tested on relatively small data sets. However, the model or performance stability was not explored and assessed. By re-analyzing the data, we reveal that the reported mortality prediction performance was likely over-optimistic and its uncertainty was underestimated or overlooked, with a large variability in predicting deaths.

This paper presents a simple yet novel method to estimate the heart frequency (HF) of neonates directly from the ECG signal, instead of using the RR-interval signals as generally done in clinical practices. From this, the heart rate (HR) can be derived. Thus, we avoid the use of peak detectors and the inherent errors that come with them.Our method leverages the highest Power Spectral Densities (PSD) of the ECG, for the bins around the frequencies related to heart rates for neonates, as they change in time (spectrograms).We tested our approach with the monitoring data of 6 days for 52 patients in a Neonate Intensive Care Unit (NICU) and compared against the HR from a commercial monitor, which produced a sample every second. The comparison showed that 92.4% of the samples have a difference lower than 5bpm. Moreover, we obtained a median MAE (Mean Absolute Error) between subjects equal to 2.28 bpm and a median RMSE (Root Mean Square Error) equal to 5.82 bpm. Although tested for neonates, we hypothesize that this method can also be customized for other populations.Finally, we analyze the failure cases of our method and found a direct co-allocation of errors due to moments with higher PSD in the lower frequencies with the presence of critical alarms related to other physiological systems (e.g. desaturation).

The authors have spotted a typographical error contained in our paper titled “Optimal unidirectional switch designs.”1 The asymptotic variance for the optimal augmented steppedwedge design provided in the bottomof page 3578 is incorrect. It should read: (Formula presented.) The relative efficiency of the optimal augmented stepped wedge design with respect to the optimal unidirectional switch design shown in Figure 6 was produced using this correct expression and no changes are further needed. No other results in the article were affected by this error.

Stepped wedge designs and delayed start designs can all be considered as special cases of the so-called unidirectional switch design. This paper provides optimal proportions of clusters that are allocated to switch patterns in a unidirectional switch design to minimize the asymptotic variance of the treatment effect estimator. This unique optimal design applies to certain cross-sectional and longitudinal variance component models. When the intraclass correlation coefficient is zero, the optimal unidirectional switch design coincides with the classic (cluster) parallel group design. The optimal unidirectional switch design is more efficient than the optimal stepped wedge design and delayed start designs. Compared with the uniform unidirectional switch design, the efficiency gain of the optimal unidirectional switch design can be substantial, but it depends on the intraclass correlation and the cluster size. We also showed that augmenting the optimal stepped wedge design with pure control pattern is more efficient than the optimal stepped wedge design. In addition, robust minimax design for unidirectional switch design, delayed start design, and stepped wedge design are provided.

Background: The aim of the present study was to investigate the predictive performance of serial tissue polypeptide antigen (TPA) testing after curative intent resection for detection of recurrence of colorectal malignancy. Methods: Serum samples were obtained in 572 patients from three different hospitals during follow-up after surgery. Test characteristics of serial TPA testing were assessed using a cut-off value of 75 U/L. The relation with American Joint Committee on Cancer stage and the potential additive value of tissue polypeptide antigen testing upon standard carcinoembryonic antigen (CEA) testing were investigated. Results: The area under the receiver operating characteristic curve of TPA for recurrent disease was 0.70, indicating marginal usefulness as a predictive test. Forty percent of cases that were detected by CEA testing would have been missed by TPA testing alone, whilst most cases missed by CEA were also not detected by TPA testing. In the subpopulation of patients with stage III disease predictive performance was good (area under the curve 0.92 within 30 days of diagnosing recurrent disease). In this group of patients, 86% of cases that were detected by CEA were also detected by TPA. Conclusions: Overall, TPA is a relatively poor predictor for recurrent disease during follow-up. When looking at the specific subpopulation of patients with stage III disease predictive performance of TPA was good. However, TPA testing was not found to be superior to CEA testing in this specific subpopulation.

The stepped wedge design is a unique clinical trial design that allows for a sequential introduction of an intervention. However, the statistical analysis is unclear when this design is applied in survival data. The time-dependent introduction of the intervention in combination with terminal endpoints and interval censoring makes the analysis more complicated. In this paper, a time-on-study scale discrete survival model was constructed. Simulations were conducted primarily to study the performance of our model for different settings of the stepped wedge design. Secondary, we compared our approach to continuous Cox proportional hazard model. The results show that the discrete survival model estimates the intervention effects unbiasedly. If the length of the censoring interval is increased, the precision of the estimates is decreased. Without left truncation and late entry, the number of steps improves the precision of the estimates, whereas in combination of left truncation and late entry, the number of steps decreases the precision. Given the same number of participants and clusters, a parallel group design has higher precision than a stepped wedge design. Copyright (c) 2016 John Wiley & Sons, Ltd.

Clinical trials may apply or use a sequential introduction of a new treatment to determine its efficacy or effectiveness with respect to a control treatment. The reasons for choosing a particular switch design have different origins. For instance, they may be implemented for ethical or logistic reasons or for studying disease-modifying effects. Large-scale pragmatic trials with complex interventions often use stepped wedge designs (SWDs), where all participants start at the control group, and during the trial, the control treatment is switched to the new intervention at different moments. They typically use cross-sectional data and cluster randomization. On the other hand, new drugs for inhibition of cognitive decline in Alzheimer’s or Parkinson’s disease typically use delayed start designs (DSDs). Here, participants start in a parallel group design and at a certain moment in the trial, (part of) the control group switches to the new treatment. The studies are longitudinal in nature, and individuals are being randomized. Statistical methods for these unidirectional switch designs (USD) are quite complex and incomparable, and they have been developed by various authors under different terminologies, model specifications, and assumptions. This imposes unnecessary barriers for researchers to compare results or choose the most appropriate method for their own needs. This paper provides an overview of past and current statistical developments for the USDs (SWD and DSD). All designs are formulated in a unified framework of treatment patterns to make comparisons between switch designs easier. The focus is primarily on statistical models, methods of estimation, sample size calculation, and optimal designs for estimation of the treatment effect. Other relevant open issues are being discussed as well to provide suggestions for future research in USDs.

Background: Increased breast cancer incidence between 45 and 50 years of age requires reconsideration of the current starting age of routine mammography. Our aim was to evaluate the quantitative benefits, harms, and cost-effectiveness of lowering the starting age of breast cancer screening in the Dutch general population. Methods: Economic modelling with a lifelong perspective compared biennial screening 48-74 and 46-74 to the current Dutch screening program (50-74). Tumour deaths prevented, years of life saved (YOLS), false positives, radiation-induced tumours, costs and incremental costseffectiveness ratios (ICERs) were evaluated. Results: Starting the screening at 48 vs. 50 led to an increase in: the number of small screen detected tumours (4.0%), tumour deaths prevented (5.6%), false positives (9.2%), YOLS (5.6%), radiation-induced tumours (14.7%), and costs (4.1%). Starting the screening at 46 vs. 48 increased the number of small screen detected tumours (3.3%), tumour deaths prevented (4.2%), false positives (8.8%), YOLS (3.7%), radiation-induced tumours (15.2%), and costs (4.0%). The ICER was €5,600/YOLS for 48-74 scenario and €5,600/YOLS for 46-74. Conclusions: Women could benefit from lowering the starting age of screening as more breast cancer deaths will be averted. Starting regular breast cancer screening earlier is also cost-effective. As the number of additional expected harms is relatively small in both alternative scenarios and the difference in ICERs is not large, introducing two additional screening rounds is justifiable.

BackgroundBecause the incidence of breast cancer increases between 45 and 50 years of age, a reconsideration is required of the current starting age (typically 50 years) for routine mammography. Our aim was to evaluate the quantitative benefits, harms, and cost-effectiveness of lowering the starting age of breast cancer screening in the Dutch general population.MethodsEconomic modelling with a lifelong perspective compared biennial screening for women aged 48–74 years and for women aged 46–74 years with the current Dutch screening programme, which screen women between the ages of 50 and 74 years. Tumour deaths prevented, years of life saved (YOLS), false-positive rates, radiation-induced tumours, costs and incremental cost-effectiveness ratios (ICERs) were evaluated.ResultsStarting the screening at 48 instead of 50 years of age led to increases in: the number of small tumours detected (4.0%), tumour deaths prevented (5.6%), false positives (9.2%), YOLS (5.6%), radiation-induced tumours (14.7%), and costs (4.1%). Starting the screening at 46 instead of 48 years of age increased the number of small tumours detected (3.3%), tumour deaths prevented (4.2%), false positives (8.8%), YOLS (3.7%), radiation-induced tumours (15.2%), and costs (4.0%). The ICER was €5,600/YOLS for the 48–74 scenario and €5,600/YOLS for the 46–74 scenario.ConclusionsWomen could benefit from lowering the starting age of screening as more breast cancer deaths would be averted. Starting regular breast cancer screening earlier is also cost-effective. As the number of additional expected harms is relatively small in both the scenarios examined, and the difference in ICERs is not large, introducing two additional screening rounds is justifiable.

Aim: The value of frequent Carcino-Embryonic Antigen (CEA) measurements and CEA-triggered imaging for detecting recurrent disease in colorectal cancer (CRC) patients was investigated in search for an evidence-based follow-up protocol.Methods: This is a randomized-controlled multicenter prospective study using a stepped-wedge cluster design. From October 2010 to October 2012, surgically treated non-metastasized CRC patients in follow-up were followed in eleven hospitals. Clusters of hospitals sequentially changed their usual follow-up care into an intensified follow-up schedule consisting of CEA measurements every two months, with imaging in case of two CEA rises. The primary outcome measures were the proportion of recurrences that could be treated with curative intent, recurrences with definitive curative treatment outcome, and the time to detection of recurrent disease.Results: 3223 patients were included; 243 recurrences were detected (7.5%). A higher proportion of recurrences was detected in the intervention protocol compared to the control protocol (OR = 1.80; 95%-CI: 1.33-2.50; p = 0.0004). The proportion of recurrences that could be treated with curative intent was higher in the intervention protocol (OR = 2.84; 95%-CI: 1.38-5.86; p = 0.0048) and the proportion of recurrences with definitive curative treatment outcome was also higher (OR = 3.12, 95%-CI: 1.25-6.02, p-value: 0.0145). The time to detection of recurrent disease was significantly shorter in the intensified follow-up protocol (HR = 1.45; 95%-CI: 1.08-1.95; p = 0.013).Conclusion: The CEA watch protocol detects recurrent disease after colorectal cancer earlier, in a phase that a significantly higher proportion of recurrences can be treated with curative intent. (C) 2015 Elsevier Ltd. All rights reserved.

Background The aim of the study was to evaluate psychological effects of the state-of-art intensified follow-up protocol for colorectal cancer patients in the CEAwatch trial. Method At two time points during the CEAwatch trial questionnaires regarding patients’ attitude towards follow-up, patients’ psychological functioning and patients’ experiences and expectations were sent to participants by post. Linear mixed models were fitted to assess the influences and secular trends of the intensified follow-up on patients’ attitude towards follow-up and psychological functioning. As secondary outcome, odds ratios were calculated using ordinal logistic mixed model to compare patients’ experiences to their expectations, as well as their experiences at two different time points. Results No statistical significant effects of the intensified follow-up were found on patients’ attitude towards the follow-up and psychological functioning variables. Patients had high expectations of the intensified follow-up and their experiences at the second time point were more positive compared to the scores at the first time point. Conclusion The intensified follow-up protocol posed no adverse effects on patients’ attitude towards follow-up and psychological functioning. In general, patients were more nervous and anxious at the start of the new follow-up protocol, had high expectations of the new follow-up protocol and were troubled by the nuisances of the blood sample testing. As they spent more time in the follow-up and became more adapted to it, the nervousness and anxiety decreased and the preference for the frequent blood test became high in replacement of conversations with the doctors.

Background The CEAwatch randomized trial showed that follow-up with intensive carcinoembryonic antigen (CEA) monitoring (CEAwatch protocol) was better than care as usual (CAU) for early postoperative detection of colorectal cancer recurrence. The aim of this study was to calculate overall survival (OS) and disease-specific survival (DSS). Methods For all patients with recurrence, OS and DSS were compared between patients detected by the CEAwatch protocol versus CAU, and by the method of detection of recurrence, using Cox regression models. Results Some 238 patients with recurrence were analysed (7·5 per cent); a total of 108 recurrences were detected by CEA blood test, 64 (55·2 per cent) within the CEAwatch protocol and 44 (41·9 per cent) in the CAU group (P = 0·007). Only 16 recurrences (13·8 per cent) were detected by patient self-report in the CEAwatch group, compared with 33 (31·4 per cent) in the CAU group. There was no significant improvement in either OS or DSS with the CEAwatch protocol compared with CAU : hazard ratio 0·73 (95 per cent 0·46 to 1·17) and 0·78 (0·48 to 1·28) respectively. There were no differences in survival when recurrence was detected by CT versus CEA measurement, but both of these methods yielded better survival outcomes than detection by patient self-report. Conclusion There was no direct survival benefit in favour of the intensive programme, but the CEAwatch protocol led to a higher proportion of recurrences being detected by CEA -based blood test and reduced the number detected by patient self-report. This is important because detection of recurrence by blood test was associated with significantly better survival than patient self-report, indirectly supporting use of the CEAwatch protocol.

Stepped wedge designs and delayed start designs can all be considered as special cases of the so-called unidirectional switch design. This paper provides optimal proportions of clusters that are allocated to switch patterns in a unidirectional switch design to minimize the asymptotic variance of the treatment effect estimator. This unique optimal design applies to certain cross-sectional and longitudinal variance component models. When the intraclass correlation coefficient is zero, the optimal unidirectional switch design coincides with the classic (cluster) parallel group design. The optimal unidirectional switch design is more efficient than the optimal stepped wedge design and delayed start designs. Compared with the uniform unidirectional switch design, the efficiency gain of the optimal unidirectional switch design can be substantial, but it depends on the intraclass correlation and the cluster size. We also showed that augmenting the optimal stepped wedge design with pure control pattern is more efficient than the optimal stepped wedge design. In addition, robust minimax design for unidirectional switch design, delayed start design, and stepped wedge design are provided.

Objectives: To determine the advantages and disadvantages of a stepped wedge design for a specific clinical application.Study Design and Setting: The clinical application was a pragmatic cluster randomized surgical trial intending to find an increased percentage of curable recurrences in patients in follow-up after colorectal cancer. Advantages and disadvantages of the stepped wedge design were evaluated, and for this application, new advantages and disadvantages were presented.Results: A main advantage of the stepped wedge design was that the intervention rolls out to all participants, motivating patients and doctors, and a large number of patients who were included in this study. The stepped wedge design increased the complexity of the data analysis, and there were concerns regarding the informed consent procedure. The repeated measurements may bring burden to patients in terms of quality of life, satisfaction, and costs.conclusion:. The stepped wedge design is a strong alternative for pragmatic cluster randomized trials. The known advantages hold, whereas most of the disadvantages were not applicable to this application. The main advantage Was that we Were able to include a large number of patients. Main disadvantages were that the informed consent procedure can be problematic and that the analysis of the data can be complex. (C) 2014 Elsevier Inc. All rights reserved.

The authors regret that the name Alicja M. Daszczuck, has been misspelled and should be Alicja M. Daszczuk. The authors would like to apologise for any inconvenience caused.

Breast cancer screening is a topic of hot debate, and currently no general consensus has been reached on starting and ending ages and screening intervals, in part because of a lack of precise estimations of the benefit&ndash, harm ratio. Simulation models are often applied to account for the expected benefits and harms of regular screening, however, the degree to which the model outcomes are reliable is not clear. In a recent systematic review, we therefore aimed to assess the quality of published simulation models for breast cancer screening of the general population. The models were scored according to a framework for qualitative assessment. We distinguished seven original models that utilized a common model type, modelling approach, and input parameters. The models predicted the benefit of regular screening in terms of mortality reduction, and overall, their estimates compared well to estimates of mortality reduction from randomized controlled trials. However, the models did not report on the expected harms associated with regular screening. We found that current simulation models for population breast cancer screening are prone to many pitfalls, their outcomes bear a high overall risk of bias, mainly because of a lack of systematic evaluation of evidence to calibrate the input parameters and a lack of external validation. Our recommendations concerning future modelling are therefore to use systematically evaluated data for the calibration of input parameters, to perform external validation of model outcomes, and to account for both the expected benefits and the expected harms so as to provide a clear balance and cost-effectiveness estimation and to adequately inform decision-makers.

The aim of this review was to critically evaluate published simulation models for breast cancer screening of the general population and provide a direction for future modeling. A systematic literature search was performed to identify simulation models with more than one application. A framework for qualitative assessment which incorporated model type; input parameters; modeling approach, transparency of input data sources/assumptions, sensitivity analyses and risk of bias; validation, and outcomes was developed. Predicted mortality reduction (MR) and cost-effectiveness (CE) were compared to estimates from meta-analyses of randomized control trials (RCTs) and acceptability thresholds. Seven original simulation models were distinguished, all sharing common input parameters. The modeling approach was based on tumor progression (except one model) with internal and cross validation of the resulting models, but without any external validation. Differences in lead times for invasive or non-invasive tumors, and the option for cancers not to progress were not explicitly modeled. The models tended to overestimate the MR (11-24%) due to screening as compared to optimal RCTs 10% (95% CI - 2-21%) MR. Only recently, potential harms due to regular breast cancer screening were reported. Most scenarios resulted in acceptable cost-effectiveness estimates given current thresholds. The selected models have been repeatedly applied in various settings to inform decision making and the critical analysis revealed high risk of bias in their outcomes. Given the importance of the models, there is a need for externally validated models which use systematical evidence for input data to allow for more critical evaluation of breast cancer screening. (C) 2015 Elsevier Ltd. All rights reserved.

Breast cancer screening is a topic of hot debate, and currently no general consensus has been reached on starting and ending ages and screening intervals, in part because of a lack of precise estimations of the benefit–harm ratio. Simulation models are often applied to account for the expected benefits and harms of regular screening; however, the degree to which the model outcomes are reliable is not clear. In a recent systematic review, we therefore aimed to assess the quality of published simulation models for breast cancer screening of the general population. The models were scored according to a framework for qualitative assessment. We distinguished seven original models that utilized a common model type, modelling approach, and input parameters. The models predicted the benefit of regular screening in terms of mortality reduction; and overall, their estimates compared well to estimates of mortality reduction from randomized controlled trials. However, the models did not report on the expected harms associated with regular screening. We found that current simulation models for population breast cancer screening are prone to many pitfalls; their outcomes bear a high overall risk of bias, mainly because of a lack of systematic evaluation of evidence to calibrate the input parameters and a lack of external validation. Our recommendations concerning future modelling are therefore to use systematically evaluated data for the calibration of input parameters, to perform external validation of model outcomes, and to account for both the expected benefits and the expected harms so as to provide a clear balance and cost-effectiveness estimation and to adequately inform decision-makers.