[Calibration of a polarographic electrode with different gas samples as an error factor in the measurement of partial pressure of blood oxygen].
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Blood Gas Analysis↗
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Superfund soil cleanup: developing the Piazza Road remedial design.
A statistical approach was used to develop the remediation and soil sampling strategy at the Piazza Road dioxin site (an EPA Superfund site in Missouri). The source of the dioxin was contaminated waste oil that was used as a dust suppressant. This site was used as a test case to determine if a "surgical" remedial design could be developed. This new approach was compared to the historical approach taken by U.S. EPA Region 7 in Missouri. A pilot study provided information on the local spatial pattern of dioxin, so that sampling designs could be evaluated and costs of remediation could be estimated for different remedial strategies. Sampling designs were evaluated by a Monte Carlo approach, where the laboratory analytical error and sampling error due to spatial variation of dioxin were modeled based on the results of the pilot study and the historical site data. The optimal cleanup unit size was determined to be 14x14 ft, or 1/24th of the historical cleanup unit size. A companion paper describes the performance achieved and the dollar 5,900,000 cost savings that resulted from applying the improved design. This case study clearly shows the value of environmental data, where the use and quality of the data are established as a part of planning the site remediation.
Costs and Cost Analysis↗
Refractive Error Study in Children: sampling and measurement methods for a multi-country survey.
PURPOSE: The Refractive Error Study in Children was designed to assess the prevalence of refractive error and vision impairment in children of different ethnic origins and cultural settings. METHODS: Population-based cross-sectional samples of children 5 to 15 years of age were obtained through cluster sampling. Presenting, uncorrected, and best-corrected visual acuity, along with refractive error under cycloplegia, were the main outcome measures. Amblyopia and other causes of uncorrectable vision impairment were determined. RESULTS: Study design and sample size calculations, survey enumeration and ophthalmic examination methods, quality assurance monitoring, and da ta analyses and statistical methods are described. CONCLUSIONS: The study design, sample size, and measurement methods ensure that the prevalence of age-specific and sex-specific refractive error can be estimated with reasonable accuracy in the target populations. With commonality of methods, a comparison of findings between studies in different ethnic origins and cultural settings is possible.
Adolescent↗
Factors influencing evaporation from sample cups and assessment of their effect on analytical error.
We studied sample evaporation and its effect on analytical error. Several factors influencing evaporative loss have been identified and measured: environmental, instrumental, and operational factors, and the chemical and physical properties of the sample and its container. Such losses from several different types of sample cups have been measured, either chemically or gravimetrically, and compared with those calculated by using a model that allows evaporative loss from a cup of known geometry to be predicted under various environmental conditions. We discuss some steps that may be taken to minimize evaporative loss and give an example to demonstrate that analytical error from this source can be decreased to a routine 1--2% or less by selecting a particular cup design.
Analysis of Variance↗
Measurement uncertainty from physical sample preparation: estimation including systematic error.
A methodology is proposed, which employs duplicated primary sampling and subsequent duplicated physical preparation coupled with duplicated chemical analyses. Sample preparation duplicates should be prepared under conditions that represent normal variability in routine laboratory practice. The proposed methodology requires duplicated chemical analysis on a minimum of two of the sample preparation duplicates. Data produced from the hierarchical design is treated with robust analysis of variance (ANOVA) to generate uncertainty estimates, as standard uncertainties ('u' expressed as standard deviation), for primary sampling (ssamp), physical sample preparation (sprep) and chemical analysis (sanal). The ANOVA results allow the contribution of the sample preparation process to the overall uncertainty to be assessed. This methodology has been applied for the first time to a case study of pesticide residues in retail strawberry samples. Duplicated sample preparation was performed under ambient conditions on two consecutive days. Multi-residue analysis (quantification by GC-MS) was undertaken for a range of incurred pesticide residues including those suspected of being susceptible to loss during sample preparation procedures. Sampling and analytical uncertainties dominated at low analyte concentrations. The sample preparation process contributed up to 20% to the total variability and had a relative uncertainty (Uprep%) of up to 66% (for bupirimate at 95% confidence). Estimates of systematic errors during physical sample preparation were also made using spike recovery experiments. Four options for the estimation of measurement uncertainty are discussed, which both include and exclude systematic error arising from sample preparation and chemical analysis. A holistic approach to the combination and subsequent expression of uncertainty is advised.
Calibration↗
Alcohol consumption and academic performance in a population of Spanish high school students.
OBJECTIVE: The present study was designed to identify patterns of alcohol consumption among Spanish high school students and describe the relationship between alcohol intake and school performance. METHOD: The sample population consisted of students, aged 14 to 19 years, who were attending high school during the academic year 1994-95 in the city of Granada in southern Spain. We studied 1,602 (861 female) students (alpha error - 0.05, sampling error = 5%), using a self-administered questionnaire that contained items about individual and family demographics, quantity and frequency of alcohol consumption, and school performance. Total alcohol consumption was recorded as grams (g) of alcohol per week and per day for three categories of alcoholic drinks: wine, beer and distilled spirits. RESULTS: The percentage of nondrinkers was 21.05% for male adolescents and 28.56% for female adolescents. The mean amount of alcohol consumed per week was larger in male than in female students (F= 18.36, l/l,594 df, p < .001) and distilled spirits accounted for the largest proportion of alcohol consumed. No significant differences in drinking patterns were found between students at public and private schools. The risk of academic failure increased considerably when more than 150 g of alcohol were consumed per week (OR: 2.91; 95% CI: 1.94-4.43). CONCLUSIONS: Although we cannot draw any conclusions about the causes of the association between academic failure and teenage drinking, our results do show that the risk of failing increases together with alcohol intake. However, it should be noted that academic achievement is also influenced by many factors other than alcohol consumption.
Achievement↗
Erroneous mass spectrometer data caused by a faulty patient sampling tube: case report and laboratory study.
We report an error due to faulty sampling of gas for mass spectrometry by side-stream analysis that occurred during a general anesthetic for a surgical procedure. Two defects in the patient sampling tube were present. First, a crack was discovered in the polyvinylchloride tubing at the connection to the patient circuit. Second, secretions had accumulated in the end of the tubing that caused a partial obstruction to gas sampling. This combination promoted the contamination of respiratory gases sampled from the anesthesia circuit with entrained room air. This entrainment, however, occurred only during exhalation while ventilation was being controlled with a descending (during exhalation) bellows. The particular sampling error was reproduced and characterized in a mock circuit to simulate the sampling tube defects. It was determined that both a leak and a partial obstruction were necessary conditions for the sampling error to exist.
Anesthesia, Inhalation↗
Sampling frequencies and measurement error for linear and temporal gait parameters in primate locomotion.
Quantitative analyses of animal motion are increasingly easy to conduct using simple video equipment and relatively inexpensive software packages. With careful use, such analytical tools have the potential to quantify differences in movement between individuals or species and to allow insights into the behavioral consequences of morphological differences between taxa. However, as with any other type of measurement, there are errors associated with kinematic measurements. Because normative kinematic data on human and nonhuman primate locomotion are used to model aspects of gait of fossil hominins, errors in the extant data influence the accuracy of fossil gait reconstructions. The principal goal of this paper is to illustrate the effect of camera speeds (frame rates) on kinematic measurement errors, and to demonstrate how these errors vary with subject size, movement velocity, and sample size. Kinematic data for human walking and running (240 Hz), as well as data for primate quadrupedal walking and running (180 Hz) were used as inputs for a simulation of the measurement errors associated with various linear and temporal kinematic variables. Measurement errors were shown to increase as camera speed, subject body size, and interval duration all decrease, and as movement velocity increases. These results have implications for the methods used to calculate subject velocity and suggest that using a moving marker to measure the linear displacements of the body is preferable to the use of a stationary marker. Finally, while slower camera speeds will always result in higher measurement errors than do faster camera speeds, this effect can be moderated to some extent by collecting sufficiently large samples of data.
Animals↗
Genome-scale phylogeny and the detection of systematic biases.
Phylogenetic inference from sequences can be misled by both sampling (stochastic) error and systematic error (nonhistorical signals where reality differs from our simplified models). A recent study of eight yeast species using 106 concatenated genes from complete genomes showed that even small internal edges of a tree received 100% bootstrap support. This effective negation of stochastic error from large data sets is important, but longer sequences exacerbate the potential for biases (systematic error) to be positively misleading. Indeed, when we analyzed the same data set using minimum evolution optimality criteria, an alternative tree received 100% bootstrap support. We identified a compositional bias as responsible for this inconsistency and showed that it is reduced effectively by coding the nucleotides as purines and pyrimidines (RY-coding), reinforcing the original tree. Thus, a comprehensive exploration of potential systematic biases is still required, even though genome-scale data sets greatly reduce sampling error.
Genome, Fungal↗
Biochemical test profiles and laboratory system design.
More appropriate utilization of laboratory services is achieved when the hospital laboratory system is designed to meet clinical needs efficiently. Structured test profiles should be constructed to match the clinical problems, treatments, and test ordering patterns of clinicians. Minor modifications in test composition may be necessary by the laboratory physician, but instrument configuration alone should not be the controlling factor. Structured test profiles reduce the number of communication and sample collection errors. More frequent scheduled sample collections and test "runs" reduce the number of emergency tests ordered. Computer analysis and transmission of test results reduce the number of telephone communications and increase the usefulness of data. When guidelines are provided to the clinician about test selection and result interpretation, the usefulness of laboratory information increases, personnel efficiency increases, personnel errors and staffing requirements decrease, the quality of service improves, and the rate of cost increase declines. Most clinicians willingly modify their test needs and the manner in which tests are ordered if the structured profiles are readily available and convenient to order, the measurements are made frequently and the results are reported promptly, more assistance is provided for test result interpretation, and the quality of the test results is high without an increase in cost.
Clinical Laboratory Techniques↗
Transient response of diffusion dosimeters.
A gas sampler that operates on the principle of molecular diffusion was analyzed theoretically for its time dependent response. Applying Fick's second law of diffusion and the mathematical procedure of separation of variables and Duhamel's superposition integral, a simple technique is developed to correct concentration measurements for short term sampling and to estimate error when sampling real-time transient atmospheres.
Air Pollutants↗
Error analysis and efficient sampling in Markovian state models for molecular dynamics.
In previous work, we described a Markovian state model (MSM) for analyzing molecular-dynamics trajectories, which involved grouping conformations into states and estimating the transition probabilities between states. In this paper, we analyze the errors in this model caused by finite sampling. We give different methods with various approximations to determine the precision of the reported mean first passage times. These approximations are validated on an 87 state toy Markovian system. In addition, we propose an efficient and practical sampling algorithm that uses these error calculations to build a MSM that has the same precision in mean first passage time values but requires an order of magnitude fewer samples. We also show how these methods can be scaled to large systems using sparse matrix methods.
Algorithms↗
Accuracy of haplotype frequency estimation for biallelic loci, via the expectation-maximization algorithm for unphased diploid genotype data.
Haplotype analyses have become increasingly common in genetic studies of human disease because of their ability to identify unique chromosomal segments likely to harbor disease-predisposing genes. The study of haplotypes is also used to investigate many population processes, such as migration and immigration rates, linkage-disequilibrium strength, and the relatedness of populations. Unfortunately, many haplotype-analysis methods require phase information that can be difficult to obtain from samples of nonhaploid species. There are, however, strategies for estimating haplotype frequencies from unphased diploid genotype data collected on a sample of individuals that make use of the expectation-maximization (EM) algorithm to overcome the missing phase information. The accuracy of such strategies, compared with other phase-determination methods, must be assessed before their use can be advocated. In this study, we consider and explore sources of error between EM-derived haplotype frequency estimates and their population parameters, noting that much of this error is due to sampling error, which is inherent in all studies, even when phase can be determined. In light of this, we focus on the additional error between haplotype frequencies within a sample data set and EM-derived haplotype frequency estimates incurred by the estimation procedure. We assess the accuracy of haplotype frequency estimation as a function of a number of factors, including sample size, number of loci studied, allele frequencies, and locus-specific allelic departures from Hardy-Weinberg and linkage equilibrium. We point out the relative impacts of sampling error and estimation error, calling attention to the pronounced accuracy of EM estimates once sampling error has been accounted for. We also suggest that many factors that may influence accuracy can be assessed empirically within a data set-a fact that can be used to create "diagnostics" that a user can turn to for assessing potential inaccuracies in estimation.
Algorithms↗
Efficient sampling for three-dimensional atom probe microscopy data.
The best calculation of concentration profiles, isoconcentration surfaces or Gibbsian interfacial excesses from three-dimensional atom-probe microscopy data requires a compromise between spatial positioning error and statistical sampling error. For example, sampling from larger spatial regions decreases the statistical error, but increases the error in spatial positioning. Finding the appropriate balance for a particular calculation can be tricky, especially when the three-dimensional nature of the data presents an infinite number of degrees of freedom in defining surfaces, and when the statistical error is changing from one region of a sample to another due to differences in collection efficiency or atomic density. We present some strategies for approaching these problems, focusing on efficient algorithms for generating different spatial samplings. We present a unique double-splat algorithm, in which an initial, fine-grained sampling is taken to convert the data to a regular grid, followed by a second, variable width splat, to spread the effective sampling distance to any value desired. The first sampling is time consuming for a large dataset, but needs only be performed once. The second splat is done on a regular grid, so it is efficient, and can be repeated as many times as necessary to find the correct balance of statistical and positioning error. The net effect is equivalent to a Gaussian spreading of each data point, without the necessity of calculating Gaussian coefficients for millions of data points. We show examples of isoconcentration surfaces calculated under different circumstances from the same dataset.
Journal Article↗
Optimal sampling theory: effect of error in a nominal parameter value on bias and precision of parameter estimation.
The authors examined the robustness of optimal sampling theory in estimating the parameter values of two different populations of patients receiving a constant rate, half-hour intravenous infusion of theophylline. One population consisted of smokers; the other included nonsmokers. The smoking population was predicted to have a serum clearance approximately 50% greater than the nonsmokers because of an induction of the cytochrome P450 system. After an initial study to provide both patient-specific and population mean parameter values, optimal sampling strategies that were derived from each population (seven sample split designs) and the patient's seven sample and four sample design were determined. A second study was performed with an overall sampling strategy that was superset of all the above strategies. The analysis of all samples served as the reference for the parameter values. Bias and precision of the values determined with each of the optimal sampling sets (seven sample sets based on the "correct" and "wrong" populations, the patient's seven and four sample sets) were determined relative to these reference values. Irrespective of the sample set used for analysis, unbiased and precise parameter estimates, particularly of hybrid parameters were provided. With the patient's four sample set, Vss was significantly biased, but the value of (2.2%) was clinically insignificant. The authors conclude that optimal sampling theory, as implemented in this study, provides robust estimates of important pharmacokinetic parameter values, even when errors of 50% are present in the clearance of the population used to calculate the optimal sampling design.
Adolescent↗
Detection of SNP epistasis effects of quantitative traits using an extended Kempthorne model.
Epistasis effects (gene interactions) have been increasingly recognized as important genetic factors underlying complex traits. The existence of a large number of single nucleotide polymorphisms (SNPs) provides opportunities and challenges to screen DNA variations affecting complex traits using a candidate gene analysis. In this article, four types of epistasis effects of two candidate gene SNPs with Hardy-Weinberg disequilibrium (HWD) and linkage disequilibrium (LD) are considered: additive x additive, additive x dominance, dominance x additive, and dominance x dominance. The Kempthorne genetic model was chosen for its appealing genetic interpretations of the epistasis effects. The method in this study consists of extension of Kempthorne's definitions of 35 individual genetic effects to allow HWD and LD, genetic contrasts of the 35 extended individual genetic effects to define the 4 epistasis effects, and a linear model method for testing epistasis effects. Formulas to predict statistical power (as a function of contrast heritability, sample size, and type I error) and sample size (as a function of contrast heritability, type I error, and type II error) for detecting each epistasis effect were derived, and the theoretical predictions agreed well with simulation studies. The accuracy in estimating each epistasis effect and rates of false positives in the absence of all or three epistasis effects were evaluated using simulations. The method for epistasis testing can be a useful tool to understand the exact mode of epistasis, to assemble genome-wide SNPs into an epistasis network, and to assemble all SNP effects affecting a phenotype using pairwise epistasis tests.
Computer Simulation↗
Histological diagnosis of precancerous lesions of the stomach: a reliability study.
BACKGROUND: Within the framework of a chemoprevention trial on stomach cancer, two substudies based on repeat measurement were undertaken to evaluate reliability of histological diagnoses of gastric precancerous lesions. METHODS: A subgroup of 45 subjects received two endoscopies separated by a period of one month. The two biopsies were reviewed by a single pathologist. A second subsample of 50 subjects had a single endoscopy and the biopsy results were reviewed by two pathologists. Agreement between the two diagnoses was assessed by Cohen's Kappa and by repeat frequency. RESULTS: When the same samples were reviewed by the pathologists involved in the trial, agreement was very high for advanced lesions (repeat frequency = 0.96 for intestinal metaplasia and 1.00 for dysplasia) but lower for less advanced lesions (repeat frequency = 0.73 for superficial gastritis and chronic gastritis, 0.65 for atrophic gastritis). When the same pathologist reviewed two sets of biopsies taken less than 2 months apart, the combination of random observer error and biopsy sampling error gave rise to quite low agreement, especially for early lesions, mainly attributable to biopsy sampling error. Comparison of diagnoses made at routine reading and at review by the same pathologist and by different pathologists showed substantial overall agreement with the exception of one pathologist for whom agreement was moderate. CONCLUSIONS: These results confirm that misclassification of histological diagnosis may be a relevant problem in chemoprevention trials of stomach cancer, more so when baseline diagnosis is taken into account in the analysis to estimate progression and regression rates of precancerous lesions. Further, the results suggest that misclassification is limited to early lesions, while diagnostic reliability of severe lesions is quite high.
Adult↗
Design and methods for a survey of lead usage and exposure monitoring in California industry.
A probability sample of California workplaces in 1986 estimated the numbers of workers using lead and their coverage by biological and environmental monitoring. Study steps included (1) identifying 505 stand ard industrial classifications (SICs) with possible lead use; (2) creating an employer lis; (3) identifying for oversampling seven single SICs and a group of 165 SICs with documented high ambient air lead; (4) further stratification by workforce size; (5) prediction from National Institute for Occupational Safety and Health surveys the number of lead-using workers for each employer; (6) drawing facilities into the sample with selection probability proportional to the number of workers predicted to use lead; (7) adjusting the sample selection if an employer listing covered multiple facilities; (8) sending a first-stage telephone/mail questionnaire to identify lead users, who were sent a second questionnaire; (10)follow-up which, aided by statutory authority, resulted in completion rates over 90%; (11) ranking lead-using processes by intensity of exposure; and (12) analysis. Sources of error include random sampling error, misleadingly large standard errors because sampling was with replacement, gaps in the employer list, and inaccurate responses. As a measure for the probability proportional to size sampling, total work force count would have been superior to the predicted number of lead users. Restriction to a single hazard makes a questionnaire-based study feasible, but names of workers, rather than counts, should be requested and a subsample should be visited.
California↗