Sequential Sampling Procedure Research Articles

Hygienic practices among consultant anesthetists in teaching hospitals of Sindh Pakistan

Background: Infection susceptibility during anesthesia administration and surgical procedures continues to be a primary cause of morbidity and mortality, despite the advent of prophylactic antibiotics and advanced technology. During surgery, nosocomial infections frequently result in sepsis, highlighting the importance of Operation Theatre (OT) sanitation. Objectives: To evaluate the hygienic practices of anesthesiologists operating in teaching hospitals in Sindh, Pakistan. Methods: Using cross-sectional design, six-month survey of consultant anesthetists in Sindh's numerous educational institutions was conducted. A thorough, self-administered questionnaire was used to collect data on aspects of sanitation practices including knowledge of infection control, existing practices, perceived obstacles, and adherence to guidelines. Utilizing a sequential sampling procedure, participants meeting specific eligibility criteria were selected to represent a wide range of demographics, socioeconomic statuses, and healthcare delivery contexts. Results: The study contributed to closing the disparity between global standards and actual practices, thereby aligning with the global initiative to improve anesthesia hygiene practices. A total of 83 consultants were approached, but 18 declined, culminating in the analysis of data from 65 participants. Their mean age was 49.27+11.17 years. Participants were predominantly employed in private institutions (n=43) and had an average of 13.97 (SD=10.24) years of experience (p> 0.05).

ON PERFORMANCE OF ACCEPTANCE SAMPLING PLANS USING SEQUENTIAL PROBABILITY RATIO TEST BASED ON TRUNCATED LIFE TESTS USING SOME DISTRIBUTION FUNCTION

Sequential sampling procedure constitute a powerful tool to achieve experimental efficiency a sequence of sample are taken from the lot and allow the number of samples to be determined entirely by the result of the sampling process, it forms the basis of numerous sequential technique for different applications which was the special case for multiple acceptance plans, the performance of sequential probability ratio test provides better detection for a wide range of source strength and useful in choosing the best statistical test, which take less time to make decision, was developed on the software reliability measure based on failure intensity for the life time random variable for a truncated life test. In this paper, we considered the performance of acceptance sampling plans using sequential probability ration (SPRT) based on truncated life tests on Maxwell distribution and exponential distribution function in terms of obtaining the minimum number of sample sizes necessary to obtain specified average life time under a given consumer’s and producer’s risk. The SPRT provide an alternative to fix the plans that can help diminish producer and consumer risk of reaching at a wrong decision.

Bayesian Log-Rank Test

Comparison of two survival curves is a fundamental problem in survival analysis. Although abundant frequentist methods have been developed for comparing survival functions, inference procedures from the Bayesian perspective are rather limited. In this article, we extract the quantity of interest from the classic log-rank test and propose its Bayesian counterpart. Monte Carlo methods, including a Gibbs sampler and a sequential importance sampling procedure, are developed to draw posterior samples of survival functions and a decision rule of hypothesis testing is constructed for making inference. Via simulations and real data analysis, the proposed Bayesian log-rank test is shown to be asymptotically equivalent to the classic one when noninformative prior distributions are used, which provides a Bayesian interpretation of the log-rank test. When using the correct prior information from historical data, the Bayesian log-rank test is shown to outperform the classic one in terms of power. R codes to implement the Bayesian log-rank test are also provided with step-by-step instructions.

Bounded risk per unit cost index constraint for sequential estimation of the mean in a two-parameter exponential distribution

In this article, the two-stage sequential sampling procedure is proposed to estimate the mean of an exponential distribution under the modified square error loss function. The main aim of the article is to consider the associated risk per unit cost function by bounding it from above with a fixed preassigned positive number, ω. We provide the exact distribution of the total sample size, explicit formulas for the mathematical expectation of the rth power of the total sample size, the expected value and mean squared error of the maximum likelihood estimate of the scale, and mean parameters of the exponential distribution under the two-stage sequential procedure. The performances of the proposed methodologies are investigated with the help of simulations. In the end, we supplement with extensive sets of data analysis via computer simulations, validating that the two-stage sequential method performs very well. Finally, using a real data set, the procedure is clearly illustrated.

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent developments in generative deep neural networks rendered possible the parallel, statistically independent sampling of molecular configurations. To be able to accurately generate samples of large molecular systems from a high-dimensional multimodal equilibrium distribution function, we developed a hierarchical approach based on expressive normalizing flows with rational quadratic neural splines and coarse-grained representation. Furthermore, system specific priors and adaptive and property-based controlled learning was designed to diminish the likelihood for the generation of high-energy structures during sampling. Finally, backmapping from a coarse-grained to fully atomistic representation is performed through an equivariant transformer model. We demonstrate the applicability of the method on the one-shot configurational sampling of a protein system with more than a hundred amino acids. The results show enhanced expressivity that diminish the invertibility constraints inherent in the normalizing flow framework. Moreover, the capacity of the hierarchical normalizing flow model was tested on a challenging case study of the folding/unfolding dynamics of the peptide chignolin.

Unifying offline and online simulation for online decision-making

Stochastic simulation is typically deployed for offline system design and control; however, the time delay in executing simulation hinders its application in making online decisions. With the rapid growth of computing power, simulation-based online optimization has emerged as an attractive research topic. We consider a problem of ranking and selection via simulation in the context of online decision-making, in which there exists a short time (referred to as online budget) after observing online scenarios. The goal is to select the best alternative conditional on each scenario. We propose a Unified Offline and Online Learning (UOOL) paradigm that exploits offline simulation, online scenarios, and online simulation budget simultaneously. Specifically, we model the mean performance of each alternative as a function of scenarios and learn a predictive model based on offline data. Then, we develop a sequential sampling procedure to generate online simulation data. The predictive model is updated based on offline and online data. Our theoretical result shows that online budget should be allocated to the revealed online scenario. Numerical experiments are conducted to demonstrate the superior performance of the UOOL paradigm and the benefits of offline and online simulation.

McClusky’s Theory of Margin and its implications on adult learners in higher education institutions

This paper draws on McClusky’s Theory of Margin which contains elements of life transitions. Adulthood period is one of growth, change and integration of balance between energy needed to engage in learning. There is an increasing participation of adult learners in higher education institutions. This paper determines whether learners complete degree programmes on time, or otherwise, by examining responsibilities, resources required for learning, challenges faced and margin available to learners. This qualitative case study used Accra Learning Centre and adopted homogenous, random and sequential purposive sampling procedures. Fifteen participants participated in the in-depth interview conducted. Thematic, narrative and interpretivist approaches were adapted in analysing data collected. It emerged that adult learners have limitless potentials to learn, yet poor time management, work commitments and financial challenges made it difficult to complete programmes on time. Engagement in multiplicities of responsibilities consumed adult learners’ time and energy to learn. The findings are discussed and recommendations are made.

Monitoring of Bioaerosol Emission from a Sludge Composting Facility

This paper presents results of one-year monitoring of bioaerosol dispersion from a full-scale sludge composting facility in the east coast of the USA. By using two-stage Andersen air samplers with a sequential sampling procedure developed in this study, a total of 24 sets of bioaerosol samples were collected on petri dishes for plate counting. The sampling program utilized a computer air dispersion model (ISCST) to predict the downwind distance at the maximum concentration. Field samplings were performed at upwind, onsite, and predicted downwind locations. The results of this study conclude that the 95% confidence intervals esti- mated for the background concentrations were: 75-173 cfu/m

Safe and Efficient Practice of Bronchoscopic Sampling from Mechanically Ventilated Patients: A Structured Evaluation of the Ambu Bronchosampler-Ascope 4 Integrated System

Background: Bronchoscopic sampling of bronchoalveolar fluid (BAL) should be safe and effective. Current sampling practice risks loss of sample to the attached negative flow, aerosolisation, or spillage, due to repeated circuit breaks, when replacing sample containers. Such concerns were highlighted during the recent coronavirus pandemic. Objectives: Evaluation of an alternative integrated sampling solution, with the Ambu Bronchosampler with aScope 4, by an experienced bronchoscopist in ICU. Methods: An observational study of 20 sequential bronchoscopic diagnostic sampling procedures was performed on mechanically ventilated patients with suspected ventilator-associated pneumonia. Mixed methods assessment was done. The predefined outcome measures were (1) ease of set up, (2) ease of specimen collection, (3) ease of protecting specimen from loss or spillage, and (4) overall workflow. The duration of the procedure and the % volume of sample retrieved were recorded. Results: The mean (±standard deviation [SD]) time for collecting 1 sample was 2.5 ± 0.8 min. The mean (±SD) specimen yield for instilled miniBAL was 54.2 ± 17.9%. Compared with standard sampling, the set-up was much easier in 18 (90%), or easier in 2 (10%) of procedures, reducing the connection steps. It was much more intuitive to use in 14 (70%), more intuitive in 4 (20%), and no more intuitive to use in 2 (10%). The overall set-up and workflow was much easier in 69% of the 13 intraprocedural connections and easier or as easy in the remaining 31% procedures. All procedures where pre connection was established were much easier (7, 100%). The Ambu Bronchosampler remained upright in all procedures with no loss or spillage of sample. Obtaining a sample was much easier in 60%, easier in 10%, no different in 20%, and worse in 10%. The ability to protect a sample from start to finish compared to standard procedures was much easier in 80%, easier in 15%, and no different in 5% of procedures. Overall workflow was much easier in 14 (70%), easier in 4 (20%), and no different in 2 (10%) of procedures. Conclusions: The Ambu Bronchosampler unit was a reliable, effective, and possibly safer technique for diagnostic sampling in ICU. It may improve safety standards during the coronavirus pandemic. A randomized control trial against the standard sampling technique is warranted.

Multistage Estimation of the Scale Parameter of Rayleigh Distribution with Simulation

This paper discusses the sequential estimation of the scale parameter of the Rayleigh distribution using the three-stage sequential sampling procedure proposed by Hall (Ann. Stat.1981, 9, 1229–1238). Both point and confidence interval estimation are considered via a unified optimal decision framework, which enables one to make the maximum use of the available data and, at the same time, reduces the number of sampling operations by using bulk samples. The asymptotic characteristics of the proposed sampling procedure are fully discussed for both point and confidence interval estimation. Since the results are asymptotic, Monte Carlo simulation studies are conducted to provide the feel of small, moderate, and large sample size performance in typical situations using the Microsoft Developer Studio software. The procedure enjoys several interesting asymptotic characteristics illustrated by the asymptotic results and supported by simulation.

Improving Hall’s Accelerated Sequential Procedure: Generalized Multistage Fixed-Width Confidence Intervals for a Normal Mean

We have developed an innovative generalized multistage fixed-width confidence interval (FWCI) estimation procedure for an unknown normal mean when the variance also remains unknown, which can be viewed as a mixture of sequential sampling and batch sampling procedures. Requiring the same expected sample size as Hall’s accelerated sequential sampling procedure, it enables the number of sampling operations to be further reduced by a predetermined factor. Extensive sets of simulations are provided to highlight the remarkable performance of the new multistage FWCI estimation procedure, and real data illustrations are included to demonstrate the practical applicability.

On Fixed width Confidence Interval Estimation of the Common Mean in Symmetric Multivariate Normal Distribution

This article deals with purely and accelerated sequential sampling procedures to find fixed-width confidence interval of completely symmetric multivariate normal mean. Procedures are studied asymptotically and are evaluated numerically. AMS 2000 subject classification: 62F25 62H12

Estimating the Probability that a Function Observed with Noise Is Convex

Consider a real-valued function that can only be observed with stochastic noise at a finite set of design points within a Euclidean space. We wish to determine whether there exists a convex function that goes through the true function values at the design points. We develop an asymptotically consistent Bayesian sequential sampling procedure that estimates the posterior probability of this being true. In each iteration, the posterior probability is estimated using Monte Carlo simulation. We offer three variance reduction methods -- change of measure, acceptance-rejection, and conditional Monte Carlo. Numerical experiments suggest that the conditional Monte Carlo method should be preferred.

Sequential fixed-width confidence interval for the rth power of the exponential scale parameter: Two-stage and sequential sampling procedures

In this paper, we study two-stage and sequential sampling procedures for estimating the rth power of the exponential scale parameter, i.e., βr, where the fixed-width confidence interval is considered. The exact operating characteristics of sequential procedures for fixed-width, (), confidence interval estimation is given. We show that the results of Zacks (2009), for two-stage and sequential procedures, can be deduced from our general results.

Proportional closeness estimation of probability of contamination under group testing

The article is focused on the problem of estimating the probability p of individual contaminated sample, under group testing. The precision of the estimator is given by the probability of proportional closeness, a concept defined in the Introduction. Two-stage and sequential sampling procedures are characterized. An adaptive procedure is examined.

Optimal Sampling for Simulated Annealing Under Noise

This paper proposes a simulated annealing variant for optimization problems in which the solution quality can only be estimated by sampling from a random distribution. The aim is to find the solution with the best expected performance, as, e.g., is typical for problems where solutions are evaluated using a stochastic simulation. Assuming Gaussian noise with known standard deviation, we derive a fully sequential sampling procedure and decision rule. The procedure starts with a single sample of the value of a proposed move to a neighboring solution and then continues to draw more samples until it is able to make a decision to accept or reject the move. Under constraints of equilibrium detailed balance at each draw, we find a decoupling between the acceptance criterion and the choice of the rejection criterion. We derive a universally optimal acceptance criterion in the sense of maximizing the acceptance probability per sample and thus the efficiency of the optimization process. We show that the choice of the move rejection criterion depends on expectations of possible alternative moves and propose a simple and practical (albeit more empirical) solution that preserves detailed balance. An empirical evaluation shows that the resulting approach is indeed more efficient than several previously proposed simulated annealing variants.

Assessing performance of sequential analysis methods for active drug safety surveillance using observational data

ABSTRACTThe routine use of sequential methods is well established in clinical studies. Recently, there has been increasing interest in applying these methods to prospectively monitor the safety of newly approved drugs through accrual of real-world data. However, the application to marketed drugs using real-world data has been limited and work is needed to determine which sequential approaches are most suited to such data. In this study, the conditional sequential sampling procedure (CSSP), a group sequential method, was compared with a log-linear model with Poisson distribution (LLMP) through a SAS procedure (PROC GENMOD) combined with an alpha-spending function on two large longitudinal US administrative health claims databases. Relative performance in identifying known drug–outcome associations was examined using a set of 50 well-studied drug–outcome pairs. The study finds that neither method correctly identified all pairs but that LLMP often provides better ability and shorter time for identifying the known drug–outcome associations with superior computational performance when compared with CSSP, albeit with more false positives. With the features of flexible confounding control and ease of implementation, LLMP may be a good alternative or complement to CSSP.

Posterior exploration based sequential Monte Carlo for global optimization

We propose a global optimization algorithm based on the Sequential Monte Carlo (SMC) sampling framework. In this framework, the objective function is normalized to be a probabilistic density function (pdf), based on which a sequence of annealed target pdfs is designed to asymptotically converge on the set of global optima. A sequential importance sampling (SIS) procedure is performed to simulate the resulting targets, and the maxima of the objective function is assessed from the yielded samples. The disturbing issue lies in the design of the importance sampling (IS) pdf, which crucially influences the IS efficiency. We propose an approach to design the IS pdf online by embedding a posterior exploration (PE) procedure into each iteration of the SMC framework. The PE procedure can also explore the important regions of the parameter space supported by the target pdf. A byproduct of the PE procedure is an adaptive mechanism to design the annealing temperature schedule online. We compare the performance of the proposed algorithm with those of several existing related alternatives by applying them to over a dozen standard benchmark functions. The result demonstrates the appealing properties of our algorithm.

Estimating the standardized mean difference with minimum risk: Maximizing accuracy and minimizing cost with sequential estimation.

The standardized mean difference is a widely used effect size measure. In this article, we develop a general theory for estimating the population standardized mean difference by minimizing both the mean square error of the estimator and the total sampling cost. Fixed sample size methods, when sample size is planned before the start of a study, cannot simultaneously minimize both the mean square error of the estimator and the total sampling cost. To overcome this limitation of the current state of affairs, this article develops a purely sequential sampling procedure, which provides an estimate of the sample size required to achieve a sufficiently accurate estimate with minimum expected sampling cost. Performance of the purely sequential procedure is examined via a simulation study to show that our analytic developments are highly accurate. Additionally, we provide freely available functions in R to implement the algorithm of the purely sequential procedure. (PsycINFO Database Record

Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost

The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize both the sampling error and the study cost. The sequential procedure we develop is the first sequential sampling procedure developed for estimating the coefficient of variation. We first present a method of planning a pilot sample size after the research goals are specified by the researcher. Then, after collecting a sample size as large as the estimated pilot sample size, a check is performed to assess whether the conditions necessary to stop the data collection have been satisfied. If not an additional observation is collected and the check is performed again. This process continues, sequentially, until a stopping rule involving a risk function is satisfied. Our method ensures that the sampling error and the study costs are considered simultaneously so that the cost is not higher than necessary for the tolerable sampling error. We also demonstrate a variety of properties of the distribution of the final sample size for five different distributions under a variety of conditions with a Monte Carlo simulation study. In addition, we provide freely available functions via the MBESS package in R to implement the methods discussed.