scholarly journals Group testing can improve the cost-efficiency of prospective-retrospective biomarker studies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Zhang ◽  
Zhiwei Zhang ◽  
Julia Krushkal ◽  
Aiyi Liu
Keyword(s):  
Cost Efficiency ◽  
Random Sampling ◽  
Sampling Design ◽  
Group Testing ◽  
Molecular Alteration ◽  
Predictive Values ◽  
Standard Design ◽  
Estimation Procedures ◽  
The Cost ◽  
Testing Design

Abstract Background Cancer treatment is increasingly dependent on biomarkers for prognostication and treatment selection. Potential biomarkers are frequently evaluated in prospective-retrospective studies in which biomarkers are measured retrospectively on archived specimens after completion of prospective clinical trials. In light of the high costs of some assays, random sampling designs have been proposed that measure biomarkers for a random sub-sample of subjects selected on the basis of observed outcome and possibly other variables. Compared with a standard design that measures biomarkers on all subjects, a random sampling design can be cost-efficient in the sense of reducing the cost of the study substantially while achieving a reasonable level of precision. Methods For a biomarker that indicates the presence of some molecular alteration (e.g., mutation in a gene), we explore the use of a group testing strategy, which involves physically pooling specimens across subjects and assaying pooled samples for the presence of the molecular alteration of interest, for further improvement in cost-efficiency beyond random sampling. We propose simple and general approaches to estimating the prognostic and predictive values of biomarkers with group testing, and conduct simulation studies to validate the proposed estimation procedures and to assess the cost-efficiency of the group testing design in comparison to the standard and random sampling designs. Results Simulation results show that the proposed estimation procedures perform well in realistic settings and that a group testing design can have considerably higher cost-efficiency than a random sampling design. Conclusions Group testing can be used to improve the cost-efficiency of biomarker studies.

scholarly journals Group Testing Can Improve the Cost-Efficiency of Prospective-Retrospective Biomarker Studies

2021 ◽  
Author(s):  
Wei Zhang ◽  
Zhiwei Zhang ◽  
Julia Krushkal ◽  
Aiyi Liu
Keyword(s):  
Cost Efficiency ◽  
Random Sampling ◽  
Sampling Design ◽  
Group Testing ◽  
Molecular Alteration ◽  
Predictive Values ◽  
Standard Design ◽  
Estimation Procedures ◽  
The Cost ◽  
Testing Design

Abstract Background: Cancer treatment is increasingly dependent on biomarkers for prognostication and treatment selection. Potential biomarkers are frequently evaluated in prospective-retrospective studies in which biomarkers are measured retrospectively on archived specimens after completion of prospective clinical trials. In light of the high costs of some assays, random sampling designs have been proposed that measure biomarkers for a random sub-sample of subjects selected on the basis of observed outcome and possibly other variables. Compared with a standard design that measures biomarkers on all subjects, a random sampling design can be cost-efficient in the sense of reducing the cost of the study substantially while achieving a reasonable level of precision.Methods: For a biomarker that indicates the presence of some molecular alteration (e.g., mutation in a gene), we explore the use of a group testing strategy, which involves physically pooling specimens across subjects and assaying pooled samples for the presence of the molecular alteration of interest, for further improvement in cost-efficiency beyond random sampling. We propose simple and general approaches to estimating the prognostic and predictive values of biomarkers with group testing, and conduct simulation studies to validate the proposed estimation procedures and to assess the cost-efficiency of the group testing design in comparison to the standard and random sampling designs.Results: Simulation results show that the proposed estimation procedures perform well in realistic settings and that a group testing design can have considerably higher cost-efficiency than a random sampling design. Conclusions: Group testing can be used to improve the cost-efficiency of biomarker studies.

scholarly journals Group Testing Can Improve the Cost-Efficiency of Prospective-Retrospective Biomarker Studies

2020 ◽  
Author(s):  
Wei Zhang ◽  
Zhiwei Zhang ◽  
Julia Krushkal ◽  
Aiyi Liu
Keyword(s):  
Cost Efficiency ◽  
Random Sampling ◽  
Sampling Design ◽  
Group Testing ◽  
Molecular Alteration ◽  
Predictive Values ◽  
Standard Design ◽  
Estimation Procedures ◽  
The Cost ◽  
Testing Design

Abstract Background: Cancer treatment is increasingly dependent on biomarkers for prognostication and treatment selection. Potential biomarkers are frequently evaluated in prospective-retrospective studies in which biomarkers are measured retrospectively on archived specimens after completion of prospective clinical trials. In light of the high costs of some assays, random sampling designs have been proposed that measure biomarkers for a random sub-sample of subjects selected on the basis of observed outcome and possibly other variables. Compared with a standard design that measures biomarkers on all subjects, a random sampling design can be cost-efficient in the sense of reducing the cost of the study substantially while achieving a reasonable level of precision.Methods: For a biomarker that indicates the presence of some molecular alteration (e.g., mutation in a gene), we explore the use of a group testing strategy, which involves physically pooling specimens across subjects and assaying pooled samples for the presence of the molecular alteration of interest, for further improvement in cost-efficiency beyond random sampling. We propose simple and general approaches to estimating the prognostic and predictive values of biomarkers with group testing, and conduct simulation studies to validate the proposed estimation procedures and to assess the cost-efficiency of the group testing design in comparison to the standard and random sampling designs.Results: Simulation results show that the proposed estimation procedures perform well in realistic settings and that a group testing design can have considerably higher cost-efficiency than a random sampling design. Conclusions: Group testing can be used to improve the cost-efficiency of biomarker studies.

scholarly journals Systematic Screening for Atrial Fibrillation in the Community: Evidence and Obstacles

2018 ◽  
Vol 7 (1) ◽  
pp. 39 ◽  
Author(s):  
Ngai-Yin Chan ◽  
Keyword(s):  
Atrial Fibrillation ◽  
Cost Efficiency ◽  
Screening Programme ◽  
Ageing Population ◽  
Community Screening ◽  
Systematic Screening ◽  
Predictive Values ◽  
Risk Of Mortality ◽  
The Cost ◽  
Pulse Check

With an ageing population globally, the burden of atrial fibrillation (AF) and its consequent complication of stroke and risk of mortality will continue to increase. Although opportunistic screening for AF by pulse check or ECG rhythm strip for people >65 years of age is currently recommended, data are now emerging that demonstrate the possible benefits of systematic community screening. Such screening is capable of identifying previously undiagnosed AF in 0.5–3.0 % of all those screened. The effectiveness of screening programmes will be markedly weakened by the lack of a structured downstream management pathway, making it a mandatory component in any AF screening programme for the general population. Different tools, especially smartphone-based devices, have made AF screening in the community more feasible. However, the sensitivities and positive predictive values of the current versions of automated diagnostic algorithms for AF have to be improved further to increase the cost-efficiency of screening programmes.

A Cost-Efficiency Theory of Dispersed Network Equilibria

1988 ◽  
Vol 20 (2) ◽  
pp. 231-266 ◽  
Author(s):  
T E Smith
Keyword(s):  
Cost Efficiency ◽  
Statistical Estimation ◽  
Spatial Interaction ◽  
Flow Patterns ◽  
Smoothing Techniques ◽  
Interaction Behavior ◽  
Estimation Procedures ◽  
Efficiency Theory ◽  
Cost Efficient ◽  
The Cost

The purpose of this paper is to unite two current approaches to modeling dispersed spatial-interaction behavior: the entropy-smoothing approach, and the cost-efficiency approach. The main result of the paper is to show that those interaction flows determined by entropy-smoothing techniques correspond (for large flows) to the most probable flow patterns consistent with cost-efficient spatial-interaction behavior. In addition, it is shown that under very general conditions, these flow patterns are indeed overwhelmingly most probable. Thus, these results establish a clear behavioral foundation for entropy-smoothing techniques in terms of the cost-efficiency theory. Finally, a number of statistical estimation procedures are developed for operationalizing this theory.

A generalized fuzzy cost efficiency model

2020 ◽  
Vol 54 (6) ◽  
pp. 1775-1791
Author(s):  
Nazila Aghayi ◽  
Samira Salehpour
Keyword(s):  
Cost Efficiency ◽  
Minimum Cost ◽  
Extensive Literature ◽  
Data Envelopment ◽  
Fuzzy Environment ◽  
Decision Making Unit ◽  
Production Possibility Set ◽  
Fuzzy Cost ◽  
The Cost ◽  
Generalized Cost

The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

ANALISIS PENGARUH BIAYA OPERASIONAL PADA PERUSAHAAN PENERBANGAN NASIONAL

2017 ◽  
Vol 1 (2) ◽  
pp. 81-107
Author(s):  
Dheny Biantara
Keyword(s):  
Value Chain ◽  
Cost Efficiency ◽  
Cost Reduction ◽  
Cost Leadership ◽  
Oil Pricing ◽  
The Cost ◽  
Operation Flexibility ◽  
Airline Maintenance

Summarized Indonesian airline executive views on the reason for the cost problem in mayor airline andon the potential areas and measures of cost reduction in airline operation. Present an introductionsurvey where 3 executives from 3 Indonesian airlines were respondent. In the executive opinion the costproblem in mayor Indonesian airline is primarily due to fuel and oil pricing and money currency. Of thevarious function in airline maintenance was seen as least cost efficiency, whereas flight operation wasseen as an area with most potential for cost reduction. Indonesian airline had made route and fleetchanges after the beginning of 2011 to reduce cost, concludes from the analisys result havingprivatization would be an important step towards more efficient airline operation. Flexibility fromIndonesian airline regulatory would be very much welcome and the value chain concept to improveIndonesian airline having competitive adventage and cost leadership differentiation.

A seven year plan for upgrading the 200.000 p.e. treatment plant of Uppsala, Sweden

1994 ◽  
Vol 29 (12) ◽  
pp. 117-127
Author(s):  
Jan Erik Lind ◽  
Ernst Olof Swedling
Keyword(s):  
Cost Efficiency ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Working Environment ◽  
Regional Environment ◽  
Nitrogen Reduction ◽  
Total Investment ◽  
Close Cooperation ◽  
The Cost

The sewage treatment plant of Uppsala was originally built in 1946 and has since then been extended and upgraded several times up to 1972 when the last major upgrading was completed. In 1987 it was decided to renew the treatment plant for at least another 20-30 years of operation and to upgrade the biological process to include nitrogen reduction. A 7 year plan covering some 18 items with a total investment cost of approximately 120 MSEK was set in action during 1987. The aim was to raise the cost efficiency by introducing modern techniques, new machinery, a better working environment and a better understanding of the processes used. The need to keep the plant in operation during reconstruction work has caused difficulties, delays and unforseen costs but a close cooperation between all parties concerned (operators, contractors, engineers and the regional environment administration) has solved most of the problems. Experiences so far include an improved effluent quality, a better cost efficiency, a healthier and more engaged operating staff. A research team has been engaged to develop and introduce a nitrogen reduction scheme in the activated sludge process. This has been a challenging and fruitful experience.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Cost-efficiency assessments of marine monitoring methods lack rigor—a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis

2021 ◽  
Vol 193 (7) ◽  
Author(s):  
Heini Hyvärinen ◽  
Annaliina Skyttä ◽  
Susanna Jernberg ◽  
Kristian Meissner ◽  
Harri Kuosa ◽  
...  
Keyword(s):  
Comparative Studies ◽  
Cost Efficiency ◽  
Cost Benefit ◽  
Marine Ecosystems ◽  
Actual Cost ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Monitoring ◽  
The Status ◽  
The Cost

AbstractGlobal deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contradicting the prevalent use of the term “cost-efficiency.” We identified five different ways to compare the cost-efficiency of a marine monitoring method: (1) the cost–benefit ratio, (2) comparative studies based on an experiment, (3) comparative studies based on a literature review, (4) comparisons with other methods based on literature, and (5) subjective comparisons with other methods based on experience or intuition. Because of the observed high frequency of insufficient cost–benefit assessments, we strongly advise that more attention is paid to the coverage of both cost and efficiency parameters when evaluating the actual cost-efficiency of novel methods. Our results emphasize the need to improve the reliability and comparability of cost-efficiency assessments. We provide guidelines for future initiatives to develop a cost-efficiency assessment framework and suggestions for more unified cost-efficiency criteria.

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