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In this study we propose a sequential test for hypothesis testing on the $$C_{pmk}$$ process capability index. Furthermore, we propose a sequential sampling plan for lot acceptance based on $$C_{pmk}$$ . We compare the statistical properties of the sequential procedures with the performance of the corresponding non-sequential methodologies by carrying out an extensive simulation study. The results show that the proposed sequential methods make it possible to reach decisions much more quickly, on average, than the fixed sample size procedures with the same discriminating power.

In this study we propose a sequential test for hypothesis testing on the Cpmk process capability index. Furthermore, we propose a sequential sampling plan for lot acceptance based on Cpmk .We compare the statistical properties of the sequential procedures with the performance of the corresponding non-sequential methodologies by carrying out an extensive simulation study. The results show that the proposed sequential methods make it possible to reach decisions much more quickly, on average, than the fixed sample size procedures with the same discriminating power.

Background Patients with prior colon cancer have increased risk of metachronous colorectal neoplasms; therefore, endoscopic surveillance is indicated. Current recommendations are not risk-stratified. We investigated predictive factors for colorectal neoplasms to build a model to spare colonoscopies for low-risk patients. Methods This was a multicenter, retrospective study including patients who underwent surgery for colon cancer in 2001 – 2008 (derivation cohort) and 2009 – 2013 (validation cohort). A predictive model for neoplasm occurrence at second surveillance colonoscopy was developed and validated. Results 421 and 203 patients were included in derivation and validation cohort, respectively. At second surveillance colonoscopy, 112 (26.6 %) and 55 (27.1 %) patients had metachronous neoplasms in derivation and validation groups; three cancers were detected in the latter. History of left-sided colon cancer (OR 1.64, 95 %CI 1.02 – 2.64), ≥ 1 advanced adenoma at index colonoscopy (OR 1.90, 95 %CI 1.05 – 3.43), and ≥ 1 adenoma at first surveillance colonoscopy (OR 2.06, 95 %CI 1.29 – 3.27) were independently predictive of metachronous colorectal neoplasms at second surveillance colonoscopy. For patients without such risk factors, diagnostic accuracy parameters were: 89.3 % (95 %CI 82.0 %-94.3 %) and 78.2 % (95 %CI 65.0 %-88.2 %) sensitivity, and 28.5 % (95 %CI 23.5 %-33.9 %) and 33.8 % (95 %CI 26.2 %-42.0 %) specificity in derivation and validation group, respectively. No cancer would be missed. Conclusions Patients with prior left-sided colon cancer or ≥ 1 advanced adenoma at index colonoscopy or ≥ 1 adenoma at first surveillance colonoscopy had a significantly higher risk of neoplasms at second surveillance colonoscopy; patients without such factors had much lower risk and could safely skip the second surveillance colonoscopy. A prospective, multicenter validation study is needed.

Statistical surveillance is a noteworthy endeavor in many health-care areas such as epidemiology, hospital quality, infection control, and patient safety. Formonitoring hospital adverse events, the Shewhart u-control chart is the most used methodology. One possible issue of the u-chart is that in health-care applications the lower control limit (LCL) is often conventionally set to zero as the adverse events are rare and the sample sizes are not sufficiently large to obtain LCL greater than zero. Consequently, the control chart loses any ability to signal improvements. Furthermore, as the area of opportunity (sample size) is not constant over time, the in-control and out-of-control run length performances of themonitoring scheme are unknown. In this article, on the basis of a real case and through an intensive simulation study,we first investigate the in-control statistical properties of the u-chart. Then we set up several alternative monitoring schemes with the same in-control performances and their out-of-control properties are studied and compared. The aim is to identify themost suitable control chart considering jointly: the ability to detect unexpected changes (usually worsening), the ability to test the impact of interventions (usually improvements), and the ease of use and clarity of interpretation. The results indicate that the exponentially weighted moving average control chart derived under the framework of weighted likelihood ratio test has the best overall performance.

Patient safety is a great concern of healthcare institutions and the correct reporting and management of adverse events is a key element for supporting safety improvement efforts. Patient falls are the most frequent adverse event in hospitals and often cause serious patient outcomes. In this work, we describe the experience of the Local Healthcare Authority of Romagna, Italy that, within the framework of a quality and safety improvement programme, designed, developed and implemented a desktop application for monitoring inpatient falls. A multidisciplinary team created a software tool based on R, an open source software for statistical computing that, appropriately combined with the existing hospital information system, is used to obtain Shewhart u-control charts for monitoring the monthly fall rates. The tool had been implemented in twenty nine hospital units. The results indicate that the proposed application gave a valuable contribution in the safety improvement activities. Its usefulness extended beyond the “safety problem” as it also enabled hospital managers to identify a number of critical issues in data collection. As a result, where necessary, improvement actions had been implemented. Furthermore, the use of open source software led to a considerable cost reduction and facilitated customization of the software tool.

We propose a sequential hypothesis test for the process capability index Cpmk. We compare the performance of the proposed test with the properties of the most used non-sequential test by performing a simulation study. The results indicate that the sequential test has best results in terms of power function and makes it possible to save a not negligible amount of sample size.

In this work we propose a sequential test for the process capability index Cpmk. We study the statistical properties of the sequential test by performing an extensive simulation study with regard the average of the sample sizes for correctly deciding, for H0 and H1, the maximum allowable sample size required to achieve a pre-set power level and for ensuring that the empirical type I error probability does not exceed the nominal alfa-level of the test. We compared the performance of the sequential procedure with the properties of the most used non-sequential test. The results show that the proposed test allows on average smaller stopping sample sizes as compared with the fixed sample size test while maintaining the desired alfa-level and power.

Background: Patient safety is a priority for all healthcare institutions, mainly for ethical reasons but also for the related economic issues. For implementing effective safety improvement programs, it is necessary to monitor processes and assess performances. Statistical process control is a useful methodology for monitoring performance and processes and healthcare literature suggests the use of control charts for monitoring patient safety metrics. Challenges may occur when applying statistical quality improvement methods often due to the difficulty of combining commercial software with the hospitals information systems. Furthermore, commercial software for control charts can be expensive for hospital units that often struggle with budget constraints. Objectives: In this work, we describe the experience of the AUSL of Romagna that designed, developed and implemented desktop application to obtain control charts for monitoring inpatient falls. Methodology: A multidisciplinary team created a software tool based on R, an open source software for statistical computing, that properly combined with the existing hospital information system, allows, upon request, to generate a Shewhart u-control chart for monitoring the monthly falls rates. The tool had been implemented in twenty nine hospital units. Results: The main results indicated that control charts, not only allowed to increase process understanding by assessing the process’ steady-state behavior and identifying changes that indicate either improvement or deterioration safety performance, but also enabled hospital safety managers to identify some critical issues in data collection. Consequently, where necessary, improvement actions had been implemented. It is worth noting that the use of open source software led to a considerable cost reduction and made the customization of the software tool easy. Adverse events cannot be completely eliminated. However, the experience gained by the AUSL of Romagna developing this project has shown that the use of an automatic real-time monitoring system is very effective in reducing the time dedicated to the bureaucratic aspects of patient safety. This allows healthcare professionals to spend more time on the substance of patient safety.

Background Patients with prior colorectal cancer (CRC) are at slightly increased risk of metachronous colorectal neoplasms, therefore endoscopic surveillance is indicated. Current recommendations of repeating examinations at 1, 3 and 5 years after surgery, are not tailored according to risk stratification. Our aim was to find predictive factors of colorectal neoplasms to build a predictive model, to spare colonoscopies for low-risk patients. Methods Multicenter retrospective study including patients with colon carcinoma surgically resected from 2001 to 2008 (training cohort) and from 2009 to 2013 (validation cohort). A predictive model for neoplasms occurrence at second surveillance colonoscopy was developed and externally validated. Results 396 and 131 patients were included in training and validation cohort respectively. Patients with ≥1 adenoma at the 2nd surveillance colonoscopy were 113/396 (28.5%) and 21/131 (16.5%) in the two groups. In the validation cohort, 3 cancers were found. Four variables were associated with higher risk of metachronous colorectal adenomas at 2nd surveillance colonoscopy on multivariate analysis: age >65 years old, left colectomy, ≥1 advanced adenoma at basal colonoscopy and ≥1 adenoma at first surveillance colonoscopy (table 1). The predictive model showed fair discrimination, with an area under the ROC curve of 0.69 and 0.64, in training and validation cohort respectively. In validation group, If patients with a low-risk profile (i.e. none of the risk factors) skip the 2nd surveillance colonoscopy, 25/131 (19.1%) exams would be saved while missing 2/21 (9.5%) patients with ≥1 adenoma; no cancer would be missed. Conclusions We provided a risk-stratification tool for adenoma occurrence after colon surgery, which could prove cost-effective to select patients who could skip the second surveillance colonoscopy.

Aims. Patients with prior colorectal cancer (CRC) are at slightly increased risk of metachronous colorectal neoplasms, therefore endoscopic surveillance is indicated. Current recommendations of repeating examinations at 1, 3 and 5 years after surgery, are not tailored according to risk stratification. Our aim was to find predictive factors of colorectal neoplasms to build a predictive model, to spare colonoscopies for low-risk patients. Methods. Multicenter retrospective study including patients with colon carcinoma surgically resected from 2001 to 2008 (training cohort) and from 2009 to 2013 (validation cohort). A predictive model for neoplasms occurrence at second surveillance colonoscopy was developed and externally validated. Results 396 and 131 patients were included in training and validation cohort, respectively. Patients with ≥1 adenoma at 2nd surveillance colonoscopy were 113/396 (28.5%) and 21/131 (16.5%) in the two groups. In validation cohort, 3 cancers were found. Four variables identified the low-risk patient's profile of developing metachronous colorectal adenomas: age ≤65 years old, right colectomy, no advanced adenoma at basal colonoscopy and no adenoma at first surveillance colonoscopy. The predictive model showed fair discrimination, with an area under the ROC curve of 0.69 and 0.64, in training and validation cohort. In validation group, If patients with a low-risk profile skip the 2nd surveillance colonoscopy, 25/131 (19.1%) exams would be saved while missing 2/21 (9.5%) patients with ≥1 adenoma; no advanced adenoma nor cancer would be missed. Conclusions We provided a risk-stratification tool for adenoma occurrence after colon surgery, which could prove cost-effective to select patients who could skip the second surveillance colonoscopy.

BACKGROUND AND AIMS: Self-expandable metal stent (SEMS) positioning is the recommended method for palliation of dysphagia from esophageal cancer, although it is not adverse event-free. The present study was aimed at identifying predictors for adverse events and at proposing a statistical model to predict them. METHODS: We performed a retrospective analysis of a prospectively collected database. All patients who underwent SEMS placement for stricture due to esophageal cancer between 2002 and 2011 in a tertiary-care center were identified. Multivariable regression analysis in the presence of competing risk events was used to identify factors associated with SEMS-related adverse events and to build a prediction model. RESULTS: A total of 267 patients were included. According to the competing risk regression analysis, only 2 variables were significantly associated with the risk of SEMS-related adverse events: prior chemoradiotherapy (CRT), yielding a hazard ratio (HR) of 1.687 (95% confidence interval [CI], 1.076-2.644), and the SEMS length (HR 0.884; 95% CI, 0.798-0.980) for every 10-mm length increase. Based on the estimated probability curves, after 4 months from SEMS placement, the probability of an adverse event in patients who did receive prior CRT was 50.9% compared with 34.4% in those who did not receive prior therapy, which was reduced to 9.2% and 15.1%, respectively, if a 180 mm-length stent was used. The ability of the predictive model to differentiate between patients who did and did not experience the adverse event was moderate (c-index: 0.617). CONCLUSION: The rate of SEMS-related adverse events was higher in patients with previous CRT and lower in patients receiving longer stents. Both factors were used to build an accurate predictive model. Background and Aims Self-expandable metal stent (SEMS) positioning is the recommended method for palliation of dysphagia from esophageal cancer, although it is not adverse event-free. The present study was aimed at identifying predictors for adverse events and at proposing a statistical model to predict them. Methods We performed a retrospective analysis of a prospectively collected database. All patients who underwent SEMS placement for stricture due to esophageal cancer between 2002 and 2011 in a tertiary-care center were identified. Multivariable regression analysis in the presence of competing risk events was used to identify factors associated with SEMS-related adverse events and to build a prediction model. Results A total of 267 patients were included. According to the competing risk regression analysis, only 2 variables were significantly associated with the risk of SEMS-related adverse events: prior chemoradiotherapy (CRT), yielding a hazard ratio (HR) of 1.687 (95% confidence interval [CI], 1.076-2.644), and the SEMS length (HR 0.884; 95% CI, 0.798-0.980) for every 10-mm length increase. Based on the estimated probability curves, after 4 months from SEMS placement, the probability of an adverse event in patients who did receive prior CRT was 50.9% compared with 34.4% in those who did not receive prior therapy, which was reduced to 9.2% and 15.1%, respectively, if a 180 mm-length stent was used. The ability of the predictive model to differentiate between patients who did and did not experience the adverse event was moderate (c-index: 0.617). Conclusion The rate of SEMS-related adverse events was higher in patients with previous CRT and lower in patients receiving longer stents. Both factors were used to build an accurate predictive model.

In this study, we propose a sequential method for hypothesis testing on the Cpk process capability index. We compare the properties of the sequential test with the performances of nonsequential tests by performing an extensive simulation study. The results show that the proposed sequential test makes it possible to save a large amount of sample size as compared with the fixed sample size tests while maintaining the desired α‐level and power.

Important features of multivariate process capability indices are comparability, interpretability and ease of implementation. When poor process capability is indicated by an index, the user should determine why the process is incapable (e.g. excessive variability or off-target process mean). One of the most used multivariate process capability indices is MCpm because it provides assessments of process precision and accuracy. In this work, we study and discuss a peculiarity of MCpm: processes that are equivalent in terms of precision, accuracy and MCpm index, after the occurrence of the same increase in the process variability, can have different values of the index. Because MCpm is often used for comparing processes, this behaviour may cause comparability difficulties. Therefore, we suggest how to take into account this specific behaviour for avoiding erroneous conclusions. Copyright © 2013 John Wiley & Sons, Ltd.

The multivariate Measurement Systems Analysis (MSA) approval criteria proposed in literature are specifically designed for assessing measurement systems made up of a single instrument. Therefore, they may encounter difficulties in assessing multisite measurement systems where there are multiple instruments in parallel. In this work, we propose a method for assessing such complex measurement systems. Since a key assumption in multisite measurement systems is that all instruments are expected to have the same level of precision, we base our proposal on the comparison of the precisions of multivariate instruments by means of a statistical test. A simulation study is performed in order to evaluate the performances of the proposed method. The results show that the illustrated approach is effective for assessing complex measurement systems and can be useful for reducing the costs for performing multivariate MSA. Copyright © 2013 John Wiley & Sons Ltd

Purpose Operating room turnaround time is a key process indicator for hospital business management: delays lead to a reduced surgical interventions per day with a consequent increase in costs and decrease in efficiency. This study aims to increase understanding by assessing the process’ steady-state behaviour and identifying changes that indicate either improvement or deterioration in quality. Design/methodology/approach With this purpose, we retrospectively applied Shewhart control charts and exponentially weighted moving average control charts to data extracted from an hospital information system. Findings The results showed that statistical process control is able to identify steady-state behaviour process and to detect positive or negative changes in process performance. In particular we detected a deterioration in the process performance coinciding with the change in the operanting room patient transfer staff. Practical implications This study showed that statistical quality control is a valuable tool for monitoring performance indicators. Currently, hospital managers are designing an operating room dashboard which also includes the control charts. Originality/value The article highlights the control chart application to organizational indicators allowing an objective operating room system performance assessment

OBJECTIVES: We aimed to evaluate the prevalence of irritable bowel syndrome (IBS) in patients with typical refl ux symptoms as distinguished into gastroesophageal refl ux disease (GERD), hypersensitive esophagus (HE), and functional heartburn (FH) by means of endoscopy and multichannel intraluminal impedance (MII)-pH monitoring. The secondary aim was to detect pathophysiological and clinical differences between different sub-groups of patients with heartburn. METHODS: Patients underwent a structured interview based on questionnaires for GERD, IBS, anxiety, and depression. Off-therapy upper-gastrointestinal (GI) endoscopy and 24 h MII-pH monitoring were performed in all cases. In patients with IBS, fecal calprotectin was measured and colonoscopy was scheduled for values >100 mg/kg to exclude organic disease. Multivariate logistic regression analysis was performed to identify independent risk factors for FH. RESULTS: Of the 697 consecutive heartburn patients who entered the study, 454 (65%) had refl ux-related heartburn (GERD+HE), whereas 243 (35%) had FH. IBS was found in 147/454 (33%) GERD/ HE but in 187/243 (77%) FH patients ( P

Faecal calprotectin (FC) is the most relevant noninvasive biomarker for monitoring inflammatory status, response to treatment and for predicting clinical relapse in ulcerative colitis (UC). The aim of this study was to evaluate the role of FC in predicting both clinical/endoscopic activity and clinical relapse in a large UC patient cohort. Objective Faecal calprotectin (FC) is the most relevant noninvasive biomarker for monitoring inflammatory status, response to treatment and for predicting clinical relapse in ulcerative colitis (UC). The aim of this study was to evaluate the role of FC in predicting both clinical/endoscopic activity and clinical relapse in a large UC patient cohort. Patients and methods A two-phase prospective study was carried out. In the first phase, the relationship between FC and clinical/endoscopic activity was evaluated. In the second phase, a cohort of asymptomatic patients with endoscopic mucosal healing was followed up using clinical and FC level determinations. Results One hundred and twenty-one UC patients were enrolled. The FC concentrations were directly correlated with both clinical and endoscopic activity (r= 0.76 and 0.87, respectively, P>0.05) and were capable of differentiating between different degrees of endoscopic severity (P> 0.01). An FC cut-off value of 110 ìg/g was highly predictive (95%) of endoscopic activity. Seventy-four patients in clinical remission with mucosal healing were followed up for a year or until relapse and 27% developed a clinical relapse. The FC concentration of nonrelapsed patients (48 μg/g) versus relapsed patients (218 μg/g) was significantly different (P> 0.01). An FC cut-off value of 193 μg/g had an accuracy of 89% in predicting clinical relapse. High FC levels were associated with clinical relapse using survival analysis and multivariate analysis. Conclusion Our data strongly support the use of FC for staging the activity of disease, predicting relapse and leading decisionmaking in a UC setting.

Salinity is a tracer of water and nutrient flows and is employed in studies on coastal environment to establish the coastal or remote origins of pollutants and nutrients. The present article analyses the effects of salinity on the multivariate distribution of the Trophic Index TRIX. The approach taken is based on a multivariate nested model with salinity as an explicative variable. We believe that our results are of use in discerning those areas of the North-West Adriatic Sea influenced by the Po River.

The majority of actions designed to improve processes and quality include the assessment of the capability of a measurement system. The statistical model relating the measured value to the true, but not observable, value of a product characteristic is usually Gaussian and additive. In this paper we propose to extend the said model to a more general formulation by introducing the structure of the two-component error model. An approximated method for evaluating the misclassification rates under the two-component error model is proposed and assessed.

The properties of the estimator of C p for autocorrelated data in presence of measurement errors are discussed. This work is motivated by the fact that while some efforts have been dedicated in the literature to the statistical properties of the capability index estimator when the data are autocorrelated, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this paper, for a first order stationary autoregressive process, the performances of the estimator of C p in the case of measurement errors are derived and compared with those obtained in the error free case.

Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement-error effects on the performance of PCIs. The analysis is illustrated with reference to and , i.e. the two most common measures suggested to evaluate process capability. Copyright © 2002 John Wiley & Sons, Ltd.

Process capability indices (PCIs) have been widely used in manufacturing industries to previde a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement errors effects on the performance of PCIs. The analysis is illustrated with reference toC p , i.e. the simplest and most common measure suggested to evaluate process capability.

The quality of data collected by air pollution monitoring networks is often affected by inaccuracies and missing data problems, mainly due to breakdowns and/or biases of the measurement instruments. In this paper we propose a statistical method to detect, as soon as possible, biases in the measurement devices, in order to improve the quality of collected data on line. The technique is based on the joint use of stochastic modelling and statistical process control algorithms. This methodology is applied to the mean hourly ozone concentrations recorded from one monitoring site of the Bologna urban area network. We set up the monitoring algorithm through Monte Carlo simulations in such a way to detect anomalies in the data within a reasonable delay. The results show several out of control signals that may be caused by problems in the measurement device.

Multivariate measurement systems analysis is usually performed by designing suitable gauge R&R experiments ignoring available data generated by the measurement system while used for inspection or process control. This article proposes an approach that, using the data that are routinely available from the regular activity of the instrument, offers the possibility of assessing multivariate measurement systems without the necessity of performing a multivariate gauge study. It can be carried out more frequently than a multivariate gauge R&R experiment, since can be implemented at almost no additional cost. Therefore the synergic use of the proposed approach and the traditional multivariate gauge R&R studies can be a useful strategy for improving the overall quality of multivariate measurement systems and is effective for reducing the costs of a multivariate MSA performed with a certain frequency.

BACKGROUND AND AIM: Despite the increasing evidence of MAP/DNA isolation in Crohn's disease (CD), its potential pathogenetic role remains unclear. To further clarify the possible relationship between MAP and CD, we investigated the presence of IS900 DNA fragment in feces from Crohn's disease and ulcerative colitis (UC) patients and from healthy controls (HC). METHODS: Stool samples were collected from 31 CD, 20 UC, and 23 HC and stored at -20°C in 200-mg aliquots. DNA was extracted. MAP presence was detected with a specific PCR amplifying a 409-bp fragment from IS900. The specificity of PCR for IS900 was confirmed sequencing three positive products. Statistical analysis was performed using the Chi-square test. RESULTS: Twenty-one of 31 CD (68%), 13 of 20 UC (65%) and 11 of 23 HC (48%) were MAP-positive (CD vs. HC: p = ns; UC vs. HC: p = ns). With the limits of a small sample size, the IS900-positive percentage in CD and UC was higher than HC, although the difference was not statistically significant. CONCLUSIONS: The possibility to track the MAP presence in human feces represents a new approach to the "MAP hypothesis". Detection of MAP DNA in feces is very common, reaching very high prevalence both in CD and in UC and even in HC. Our findings seem consistent with a high prevalence of MAP asymptomatic infection among the general population and so the possible involvement of MAP in CD pathogenesis could be linked to a specific immune defective response.

Various different definitions of multivariate process capability indices have been proposed in the literature. Most of the research works related to multivariate process capability indices assume no gauge measurement errors. However, in industrial applications, despite the use of highly advanced measuring instruments, account needs to be taken of gauge imprecision. In this paper we are going to examine the effects of measurement errors on multivariate process capability indices computed using the principal components analysis. We show that measurement errors alter the results of a multivariate process capability analysis, resulting in either a decrease or an increase in the capability of the process. In order to achieve accurate process capability assessments, we propose a method useful for overcoming the effects of gauge measurement errors.

This paper studies the effects of multivariate measurement errors on mul- tivariate capability indices computed using the principal components analysis. This study shows that measurement errors influence the results of a multivariate process capability analysis, resulting in either a decrease or an increase in the capability of the process. To avoid unreliable conclusions a method is proposed for overcom- ing the effects of measurement errors. Furthermore, a statistical test that allows one to determine whether measurement errors alter the process covariance structure is discussed