Calculation Of Confidence Intervals Research Articles

A Local Sensitivity Analysis of the Trial of Continuous or Interrupted Chest Compressions during Cardiopulmonary Resuscitation: Is a Local Protocol Change Required?

ObjectiveThe “Trial of Continuous (CCC) or Interrupted Chest Compressions (ICC) during Cardiopulmonary Resuscitation (CPR)” compared two CPR strategies for out-of-hospital cardiac arrest (OHCA). Although results were neutral, there was suggestion of benefit for ICC. However, nearly 50% of study patients had a protocol violation; regional variations may have played a role in protocol adherence and outcomes. We analyzed our British Colombia (BC) cohort to decide whether a protocol change from CCC to ICC was justified.MethodsThis was a post-hoc analysis of BC-enrolled study patients. The primary between-group comparison was favorable neurological outcome (modified Rankin scale ≤ 3) using intention-to-treat. Secondary analyses compared those treated per-protocol (adjusted) and the top compliant clusters (unadjusted). We classified protocol violations using a structured algorithm. We used logistic regression and computed the difference in probabilities using the marginal standardization method with bootstrapping to calculate confidence intervals.ResultsThere were 3769 patients included, with a median age of 69 years (IQR: 56–80). There were protocol violations in 3.2% of those in the CCC group and 27% of those in the ICC group. In patients randomized to CCC or ICC, 11.2% and 10.8% (risk difference 0.42%; 95% CI -1.58, 2.41) had favorable neurological outcomes, respectively. In the per-protocol and top compliant clusters comparisons, risk differences were 0.25% (95% CI -1.70, 2.25) and 2.95% (95% CI -0.68, 6.58).ConclusionOur comparisons suggest that CCC may be the preferred strategy in our region and is likely not resulting in worse outcomes. Based on the original study and our local analysis, we found no compelling reasons to change our local strategy from CCC to ICC.

Characterization of a Swine (Sus scrofa) Model of Oral Potassium Cyanide Intoxication.

Cyanide is a readily available and potentially lethal substance. Oral exposure can result in larger doses, compared with other routes. Currently, there are no antidotes specific for use in the treatment of oral cyanide poisoning, and studies cannot be done in humans. We report on a new large animal model of oral cyanide toxicity to evaluate potential antidotes. Six female swine (Sus scrofa; weight, 45 to 55 kg) were anesthetized, intubated, and instrumented. Animals received a KCN bolus of either 5 or 8 mg/kg delivered via orogastric tube. Time to apnea was recorded; parameters monitored included heart rate, respiratory rate, blood pressure, pulse oximetry, end-tidal CO2, arterial blood gasses, and lactate concentrations. The Welch t test was used to calculate confidence intervals, mean, and standard deviation, and a Kaplan-Meier survival curve was used to compare survival between the 2 groups. At baseline, all animals in both groups were similar. Animals in the 5-mg/kg group had a more rapid time to apnea (5.1 ± 2.1 min), longer time to death (48.5 ± 38.1 min), and a greater rate of survival than the 8-mg/kg group (apnea, 10.6 ± 10.7 min; death, 26.1 ± 5.8 min). All animals displayed signs of toxicity (acidemia, hyperlactatemia, hypotension, apnea). We here report a large animal (swine) model of oral cyanide poisoning with dose-dependent effects in regard to time to death and survival rate. This model likely will be valuable for the development of medical countermeasures for oral cyanide poisoning.

Time partitioning between jaguar Panthera onca, puma Puma concolor and ocelot Leopardus pardalis (Carnivora: Felidae) in Costa Rica’s dry and rainforests

Segregation of the daily activity patterns is considered and important mechanism facilitating the coexistance of competing species. Here, we evaluated if temporal separation existed among jaguar (Panthera onca), puma (Puma concolor) and ocelot (Leopardus pardalis) and if their activity patterns were related to that of a particular prey. We used camera trap records to estimate the activity schedules of these predators and their prey. We used the coefficient of overlapping (Δ; ranging from 0 to 1) to quantify the temporal interactions between predators and prey, and calculated confidence intervals from bootstrap samples. Strong temporal overlap occurred among the three felids (Δ = 0.63 - 0.82) in both dry and rainforests. However, a greater temporal separation was observed between the closest competitors (jaguar and puma, puma and ocelot). Jaguar and puma had a strong temporal overlap with medium and large-sized prey, while ocelots’ activity matched that of small-sized prey. High overlapping coefficients among the felids suggest that temporal segregation is not the main mechanism facilitating their coexistence in these areas. However, fine-scale or spatiotemporal differences in their activity patterns might contribute to their coexistence in tropical environments.

Precision of the reportable value—Statistical optimization of the number of replicates

In pharmaceutical analysis, the precision of the reportable value, i.e. the result which is to be compared to the specification limit(s), is relevant for the suitability of the analytical procedure. Using the variance contributions determined in precision studies addressing the levels injection/system precision, repeatability, and intermediate precision, the number of the corresponding replications for analysis/injection, sample preparation, and series/runs can be varied to improve the precision of the mean (reportable) value (Ermer, Agut, J.Chromatogr. A, 1353 (2014) 71–77). However, this calculation will provide only information on the gain for the precision of the calculated reportable value itself. These so-called point estimators have uncertainty associated with them which can be quantified using statistical confidence intervals. Commonly used statistical equations only allow one to calculate confidence intervals for the intermediate precision of the reportable value, which requires that the routine replication strategy must be defined before starting the precision study.In this paper, statistical models are presented that allow optimizing efficiently the replication strategy with respect to the confidence interval of the precision based on the Satterthwaite approximation posterior, i.e. using the results from the precision study without prior knowledge, as for the point estimate. It is further proposed to simplify the model by including only significant variance contributions larger than 20% of the total variation. The advantage of this minimizing the level of nesting is that the upper precision bound will tighten as the level of nesting decreases. This is important as 90% upper confidence bounds are often up to 2 or 3 times the point estimate, even for a larger number of four runs in the precision study. Four models each have been developed both for a 2-fold balanced nested design representing a complete intermediate precision study, and for a 1-fold balanced nested design using injection/system precision from an independent source. An Excel spreadsheet that performs all the calculations in this paper as well as the appropriate model selection is available from the authors.Due to the usually rather low number of series/runs in precision studies, the uncertainty of the reportable value precision is often dominated by the factor runs. For a statistical evaluation of the precision of the reportable value (in case of three precision levels), the authors recommend a minimum of six runs, two preparations per run, and two injections/analyses per preparation, in order to provide sufficient precision of the variance estimates. However, a risk-based approach is recommended for the decision to apply a statistical evaluation of the precision of the reportable value. In case of low patient risk such as for an assay of a well-characterized drug substance with tightly controlled manufacturing and analytical variability dominating the specification range, a point estimator will usually be adequate to demonstrate the suitability of the analytical procedure.

Experimental repetitions and blockage of large stems at ogee crested spillways with piers

ABSTRACTLarge wood is often transported by rivers into reservoirs during heavy rainfall events. When a critical section like a spillway is blocked and discharge capacity reduced, an uncontrolled increase of the reservoir water level may occur. This study aims to statistically analyse the importance of repetitions for the accuracy of experimental campaigns when studying blocking probabilities at ogee crested spillways equipped with piers. Systematic and reliable estimations based on physical models are critical for developing preventive measures against large wood blockage. Two statistical methods have been described and applied to calculate confidence intervals. A minimum number of repetitions for a maximum acceptable error is recommended for blocking probabilities. The minimum number of experimental repetitions has been statistically justified in accordance with a reasonable use of resources for experimental campaigns. In addition, a maximum acceptable level of error is proposed as a common metric of accuracy in large wood studies.

Interval Estimation for Mean Response in a Simple Nested Error Regression Model in Gauge R & R Study

In a measurement system monitoring manufacturing processes, one often uses gauge repeatability and reproducibility (R & R) experiment to determine the amount of variability due to parts, final products, operators, or gauge. A measurement system that employs operators randomly chosen to conduct measurements on randomly selected parts can be statistically modelled. When a measurement is assumed to be linearly related to a predictor variable and a simple nested error regression model is applied for the measurement system, one might be interested in making inferences concerning the variability for the mean response in the model. In general, confidence intervals are uniformly more informative than hypothesis tests in making inferences to parametric values in statistical models. Confidence intervals for the mean response in a simple nested error regression model can be useful tools to determine whether the variability is appropriately managed in a manufacturing process. Several confidence intervals for the mean response in the model are proposed. The confidence intervals proposed in this article are based on a moderate large sample method. Computer simulation is performed to see if the proposed intervals maintain the stated confidence level and a numerical example is provided to explain the proposed intervals by calculating confidence intervals for the mean response in the model.

Does corruption and the institutional characteristics of the contracting authorities affect the execution of healthcare infrastructures?

PurposeThis paper aims at contributing to the research on the role played by corruption in the health procurement by use non-parametric techniques to examine whether the efficient execution of Italian public contracts for healthcare infrastructures is affected by socio-economic variables (including the level of “environmental” corruption) in the area where the work is localised and by the institutional features of the contracting authority.Design/methodology/approachA data envelopment analysis (DEA) is applied to a sample of 405 contracts during the period 2000-2005. Smoothed bootstrap techniques to calculate confidence intervals for the estimated efficiency parameters along with different non-parametric tests and kernel density estimates are used.FindingsResults show that “environmental” corruption negatively influences the performance of healthcare infrastructures. Furthermore, healthcare contracting authorities appear to be less efficient than other public bodies acting as procurers.Originality/valueThe paper highlights the role of environmental corruption in the provision of healthcare infrastructures.

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Bootstrap inference when using multiple imputation.

Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available.

Efficacy and safety of bilateral thoracic paravertebral blocks in outpatient breast surgery.

Unilateral thoracic paravertebral blocks (TPVBs) have demonstrated reliable intraoperative analgesia, low postoperative pain scores, and an opioid-sparing effect in breast cancer surgery. However, secondary to the perceived risk of complications, bilateral TPVB have been less well accepted and are less frequently used. The purpose of this study was to evaluate the feasibility of using bilateral TPVBs in outpatient surgery for patients undergoing bilateral mastectomy with immediate implant-based reconstruction. Electronic medical records were retrospectively reviewed for patients receiving bilateral TPVBs for bilateral mastectomy with immediate implant-based reconstruction performed by a single surgeon from September 2012 to September 2015. Records were reviewed for incidence of complications, time to discharge, and incidence of unplanned admission or readmission. Clopper-Pearson method for binomial distribution was used to calculate confidence intervals for proportions. Forty-five patients undergoing bilateral mastectomy with immediate reconstruction received bilateral TPVBs. There were 4 TPVB-related complications, all of which were symptomatic hypotension or bradycardia (9%; 95% CI, 2%-21%). There was no incidence of symptomatic pneumothorax. Mean time to discharge readiness from the postanesthesia care unit (PACU) was 1.9 hours (SD = 1.0). Overall, 91% (n = 29) of the 32 patients scheduled for day surgery were discharged home as planned. Mean time from entry to PACU to home discharge for day surgery patients (n = 32) and planned admissions (n = 13) was 5.9 hours (SD = 4.3) and 16.3 hours (SD = 3.6), respectively. There was no incidence of readmission following discharge. Bilateral TPVBs can safely facilitate day surgery in carefully selected patients undergoing bilateral mastectomy with immediate implant-based reconstruction.

Calculating confidence intervals for percentiles of accelerated life tests with subsampling

Accelerated life tests usually contain subsampling, which represents a restriction on randomization. The two-stage approach can deal with lifetime data from experiments with subsampling. However, this method did not introduce how to reduce the biases of estimators and compute confidence intervals of low percentiles. In this article, we build the model between percentile and stress factors, and obtain likelihood-based inference on percentile. In addition, we reduce the biases of estimators using an unbiasing factor method. Finally, we illustrate our method through a real example and compare with other methods via simulation study. The simulation results show that our proposed method is better in most cases.

A system approach to decision support on basis of hierarchical and network models

A methodology and a system approach to decision support based on hierarchical and network models are proposed, which include methods for estimating and improving the consistency of pairwise comparison matrices (PCM), calculating fuzzy local weights for model elements based on fuzzy PCM, calculating confidence intervals for local weights, finding aggregated weights and aggregated belief functions for model elements, estimating the sensitivity of the results, tools for analyzing the effectiveness of the methods and an expert evaluation modeling system. A decision support system is built on basis of the proposed methodology. The proposed methodology makes it possible to increase the reliability of solutions of practical weakly structured decision-making problems by using more efficient methods at each of the stages of solving the problem. Reliability of results of application of the methodology while solving a practical decision-making problem can be assessed by means of sensitivity analysis using the stability indices of the computed local and global rankings of the decision alternatives.

Optimization of green sand mould system using Taguchi based grey relational analysis

The strength of the mould cavity in sand casting is very much significant to attain high-quality castings. Optimization of green sand process parameters plays a vital role in minimizing casting defects. In the present research work, the effect of process parameters such as AFS grain fineness number, water, molasses, bentonite, fly ash, and ramming, and their levels on the resultant mould properties were investigated and optimized using Taguchi based grey relational analysis. The Taguchi L18 orthogonal array and analysis of variance (ANOVA) were used. The quality characteristics viz., green compression strength, permeability, bulk density, mould hardness and shatter index of green sand mould were optimized using grey relational grade, based on the experiments designed using Taguchi's Design of Experiments. ANOVA analysis indicated that water content is the most influential parameter followed by bentonite, and degree of ramming that contributes to the quality characteristics. The results are confirmed by calculating confidence intervals, which lies within the interval limits. Finally, microstructure observations and X-ray diffraction analysis have been performed for the optimal sand parametric combination. Results show that presence of maximum amount of Si02, which might be the reason for enhancement of the physical properties of the sand.

A GENETIC ALGORITHM TO CALIBRATE DYNAMICAL SYSTEMS: Confidence intervals for parameters and residuals

This paper presents a genetic algorithm to calibrate dynamical systems that is able to calculate confidence intervals for the parameters of the system. As an application case is used to calibrate the system that reproduces the dynamical response of the General Factor of Personality (GFP) to a given stimulus, particularly to a stimulant drug dose. The model is called in Literature as the response model and includes an integro-differential equation. The presented application case is a single case ABC experimental design where the stimulus is methylphenidate.

Approximation of citation distributions to the Poisson distribution

Bornmann (2017) suggests calculating confidence intervals (CIs) for Journal Impact Factors (IFs) by using a simplistic approximation of citation distributions to the Poisson distribution. We empirically demonstrate that his recommendation for obtaining CIs seems appropriate for low, but not for high impact journals.

Additional statistical and graphical methods for analyzing site formation processes using artifact orientations.

The 3D orientation of clasts within a deposit are known to be informative on processes that formed that deposit. In archaeological sites, a portion of the clasts in the deposit are introduced by non-geological processes, and these are typically systematically recorded in archaeological excavations with total stations. By recording a second point on elongated clasts it is possible to quickly and precisely capture their orientation. The statistical and graphical techniques for analyzing these data are well published, and there is a growing set of actualistic and archaeological comparative data to help with the interpretation of the documented patterns. This paper advances this area of research in presenting methods to address some shortcomings in current methodologies. First, a method for calculating confidence intervals on orientation statistics is presented to help address the question of how many objects are needed to assess the formation of a deposit based on orientations. Second, a method for assessing the probability that two assemblages have different orientations is presented based on permutations testing. This method differs from existing ones in that it considers three-dimensional orientations rather than working separately with the two-dimensional bearing and plunge components. Third, a method is presented to examine spatial variability in orientations based on a moving windows approach. The raw data plus the R code to build this document and to implement these methods plus those already described by McPherron are included to help further their use in assessing archaeological site formation processes.

A Cross-Sectional Study on Factors Associated with Relapse in Patients with Schizophrenia at Mathari Hospital, Nairobi Kenya

Schizophrenia is a chronic and disabling mental disorder with a high prevalence rate of 1.4-4.6 per a thousand populations. Schizophrenia is often accompanied by relapses even while on treatment. Relapse rate vary from 50%-92% and are similar in both developed and developing countries, despite the former having well established Mental health services. This was a crosssectional descriptive study of 209 family members or significant others accompanying patients to Mathari Hospital. Patients were selected by random sampling and the study was carried out between June and July 2011. Schizophrenia was diagnosed using Diagnostic and statistical manual of mental disorder, 4th edition text revision (DSM-IV-TR) criteria. Data was collected using semi-structured questionnaire and analyzed using Statistical package for social science (SPSS) and presented in frequency tables, bar graphs and pie charts. Relationship between independent and dependent variables was determined by calculating confidence interval and summary Chi-square statistics. P-value of < 0.05 was considered significant. The findings indicated that majority of patients (81.8%) were aged between 17-46 years with (54%) of patients having had their first episodes of schizophrenia between ages 17-26 years. The results showed that factors associated with relapse in schizophrenia in Mathari hospital included, non-drug compliance and failure to attend follow-up clinic (67.9%), stressful life events (17.3%), and substance abuse (14.8%). Relapse rate was found to be 58%-97%. This calls for intensified Psycho education to both patients and family members for early recognition of signs of relapse and drug compliance. Improvement of community mental health services will reach more family members in their homes, encourage drug compliance and community involvement in understanding schizophrenia to reduce stigma. Health policy makers will avail atypical antipsychotic drugs with minimal side effects to reduce the high relapse rate. Future research should focus on clinical factors associated with relapse in schizophrenia.

Estimation of the geometrical measurement error at the stage of stereoscopic system design

The article is dedicated to the error estimation method for stereoscopic systems measuring three-dimensional coordinates and geometric parameters of objects. This method is required for stereoscopic system design to optimize the parameters of the image acquisition system and the data processing algorithms. The technique should be suitable for different mathematical models of image acquisition systems and allow to access the measurement uncertainty with a known uncertainty in determining the coordinates of the corresponding points on the images and the uncertainty of the calibration parameters. We analyzed known methods by comparing their results with the Monte Carlo simulation for the pinhole and the ray tracing models. It is shown that the method using the unscented transformation provides better accuracy and versatility than the linearization method. Using the example of measuring the length of a segment, it is demonstrated that the use of a symmetric confidence interval constructed from the mean and variance can lead to an inaccurate estimation of the error in measuring geometric parameters. We propose a method for calculating confidence intervals based on a combination of unscented transformation and interval analysis and confirm its effectiveness by the computer simulation. The analysis is applicable to the design of both passive stereoscopic devices and active triangulation systems as well as improving their software.

Verification, validation, and uncertainty quantification of a sub-grid model for heat transfer in gas-particle flows with immersed horizontal cylinders

In previous work we developed and implemented a sub-grid model for the efficient simulation of heat transfer in gas-particle flows around immersed horizontal cylinders. In this study we apply verification, validation, and uncertainty quantification methods to the developed model to rigorously examine its capabilities and limitations. Numerical verification with small, unit-cell problems shows excellent transient and steady-state behavior. Validation of a bubbling bed and a turbulent bed showed good agreement with high-resolution simulations. To quantify the error of the constitutive model predictions two methods were used to calculate confidence intervals, showing an error of approximately ±20%, well within the range of typical Nusselt number approximations. The sub-grid model was applied to a conceptual pilot-scale 1 MWe carbon capture reactor to compare with alternative modeling methods. Results show fair predictions of hydrodynamics, heat transfer, and carbon capture rates with significant savings in computational runtimes.

Quantifying weathering on variable rocks, an extension of geochemical mass balance: Critical zone and landscape evolution

AbstractWe present a statistical model of soil and rock weathering in deep profiles to expand the capacity to assess weathering to heterogeneous bedrock types, which are common at the Earth's surface. We developed the Weathering Trends (WT) model by extending the fractional mass change calculation (tau) of the geochemical mass balance model in two important ways. First, WT log transforms the elemental ratio data, to discern the log‐linear patterns that naturally develop from thermodynamic and kinetic laws of chemistry. Second, WT statistically fits log‐transformed element concentration ratio data – log(cj/ci), the only depth‐varying term in tau – as a function of depth to determine characteristic depths of transitions in weathering processes, along with confidence intervals. With no prior assumptions, WT estimates average parent material composition, average composition of the upper weathered zone and mean fractional mass change of each element over the entire weathering profile. WT displays the mean shape of weathering profiles of log‐transformed geochemical data bounded by calculated confidence intervals. We share the WT model code as an open‐source R package (https://github.com/fisherba/WeatheringTrends). The WT model was designed to interpret two 21 m cores from the Laurels Schist bedrock in the Christina River Basin Critical Zone Observatory in the Pennsylvania Piedmont, where our morphological and elemental data provided inconclusive estimates of bedrock depth. The WT model differentiated between rock variability and weathering to delineate the maximum extent of weathering at 12.3 m (CI 95% [9.2, 21.3]) in Ridge Well 1 and 7.2 m (CI 95% [4.3, 13.0]) in Interfluve Well 2. The water table was 5–8 m below fresh rock at Ridge Well 1, but at the same depth as fresh rock at the lower elevation interfluve. We assess statistical approaches to identify the best immobile element for use in WT and tau calculations. Copyright © 2017 John Wiley &amp; Sons, Ltd.