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Results for “confidence limits”

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PubMed2002

Community data--where does the value lie? Assessing confidence limits of community collected water quality data.

Waterwatch is a national community-based monitoring network that aims to involve community groups and individuals in the protection and management of waterways. Waterwatch Victoria has the dual objectives of catchment education and water quality monitoring. The educational outcomes are evident, with the Waterwatch programme facilitating over 9,000 students to monitor more than 2,000 sites in waterways in 2000. This paper aims to assess the scientific value of community-collected data, through examining differences between Waterwatch data and professionally collected data. The study looked at all aspects of volunteer data collection, including data confidence protocols, equipment, and data analysis. All professional data was collated by the Victorian Water Quality Monitoring Network (VWQMN). The parameters examined in this study were turbidity, electrical conductivity (EC), pH and total phosphorus. The level of agreement between community-collected data and professional data varied temporally and spatially. Waterwatch data for EC and pH appeared to be very similar to professionally collected data. Equipment used by Waterwatch volunteers for turbidity and total phosphorus appeared to be limited in accuracy to moderate ranges. Overall the VWQMN professionally collected data showed less variance, suggesting greater variability, potentially due to inaccuracies, in volunteer collected data

Community-Institutional Relations↗
PubMed1990

Exponential fitting of pressure-volume curves: confidence limits and sensitivity to noise.

It has recently become common to model the static pressure-volume curve of the lung as a monoexponential function of the form V = A - Be-KP, where V is volume and P is transpulmonary pressure. The parameters A, B, and particularly K have been employed as descriptors of the intrinsic mechanical properties of the lung. The purpose of the present study was to investigate the sensitivities of A, B, and K to noise in measurements of P and V and to incompleteness of data. Using Monte-Carlo simulation, we found that the presence of typical levels of noise in P led to biased estimates of K and that the 95% confidence intervals about A and K were large compared with the parameter values themselves. These effects were increased as points were systematically removed from either end of the data set. These findings show that values of K estimated from PV data are difficult to interpret without accompanying confidence intervals.

Air Pressure↗
PubMed2000

Variance and confidence limits in validation studies based on comparison between three different types of measurements.

BACKGROUND: The methods used in epidemiological studies to assess exposure are often affected by a conspicuous amount of measurement error. Exposure-measurement error is recognised to cause attenuation in the association between exposure and disease. Among different possible approaches, the validity coefficient of a measurement can be estimated by a comparison of three types of measurements, using either structural equation models or factor analysis (the triads method). These approaches assume that the measurements are linearly related to true intake and have independent random errors. METHODS: In this paper we present an estimator of the variance of the estimated validity coefficient to compute the associated confidence intervals. Standard error for the validity coefficient allows the efficiency of validation studies to be evaluated. Our work was motivated by the fact that existing software does not provide correct standard errors for the estimated validity coefficient. The approach is illustrated using selected examples from dietary validation studies. RESULTS: The accuracy of our formula is evaluated by comparison with the results of a simulation study, which shows that our variance estimator provides good results for sample sizes of at least n = 100 and when the expected value of the validity coefficient is not too close to 1.0, independent of the sample size. Our estimator formula performs better than either a naïve approach, that computes the standard error for a validity coefficient as if it is a straightforward correlation coefficient, or the SAS-CALIS procedure, which uses a maximum likelihood method. CONCLUSIONS: In evaluating the validity of the type of measurement chosen to assess exposure in an epidemiological study, it is important to provide an estimate of the precision of the validity coefficient of the measurement. Our variance estimator may help calculate sample size requirements for validation studies.

Analysis of Variance↗
PubMed2004

Confidence limits of dipole source reconstruction results.

OBJECTIVE: Equivalent dipole models are widely used in electro-encephalo-graphic (EEG) and magneto-encephalo-graphic (MEG) source reconstruction. Despite their point-like definition, the best-fit solutions have a certain probability volume depending on the source position and orientation as well as on the actually used sensor set-up and the signal-to-noise ratio (SNR). In order to avoid the misleading impression of exact localization results, a measure of the SD of the dipole localization is desirable. METHODS: This measure can be obtained by performing a deviation scan around the best-fit positions, where the explainable field is determined and compared to the best-fit field. Using a linear approximation, confidence ellipsoids can then be computed and their axes and volumes can be determined by relating the field differences to the noise of the measured data. Test-dipoles inside of a 3 spherical shells volume conductor model were used to simulate EEG- and MEG-data with sources of known positions, orientations, and noise levels. Confidence ellipsoids were computed for these test-dipole solutions and deviation scans around the best-fit dipole positions were performed in order to compare the size and the shape of the confidence ellipsoids with the real error-hypersurface. SDs of repeated dipole localizations at different depths were computed to show the validity of the linear approximation over the whole eccentricity range. RESULTS: The size of the axes of the confidence ellipsoids is inversely proportional to the SNR of the measured data, thus the confidence volume is inversely proportional to the third power of the SNR. Good agreement between SDs of repeated dipole localizations and the confidence ellipsoids was found for both EEG- and MEG-cases. CONCLUSIONS: The new method adds a new and important dimension to dipole source reconstruction results by characterizing their reliability. It is also very helpful in deciding how many dipoles are necessary to explain the measured data, since superfluous dipoles exhibit rather large confidence volumes.

Algorithms↗
PubMed1985

Confidence limits for homology in protein or gene sequences. The c-myc oncogene and adenovirus E1a protein.

We describe new tests, of general application, for deciding whether two proteins or DNA sequences are significantly homologous, in cases where the relationship is neither evidently true nor evidently false. Ralston and Bishop's comparison of the c-myc oncogene with the adenovirus E1a protein is discussed as an example. When the comparison matrix test is used to establish a homology between two sequences it is necessary that the number of high scores exceeds the expected mean level for random sequences by a statistically significant margin. The mean level itself is found from the double matching probability distribution. In examples where the number of high scores is larger than expected, but the highest score is not in itself exceptional, the variance of the numbers of scores expected for unrelated sequences is an important factor. We have analysed these variances by several methods. A simple binomial distribution gives only a rather inaccurate and low first estimate, but we derive a more rigorous and accurate statistical treatment, to take account of the correlations between scores in different parts of the comparison matrix. The theory is exact for random DNA or protein sequences with fluctuating compositions, selected by random draws from an infinite pool. In the more realistic situation, where sequences of fixed composition are formed by random permutations of the original sets, the deviations are smaller, and have been analysed by computer simulation. We find that although the relationship proposed by Ralston & Bishop, between the c-myc oncogene and adenovirus E1a proteins, appears to be significant in the binomial approximation, it is not supported by the full analysis. We conclude that, in general, great care is needed to establish any weak homology on the basis of comparisons that include no truly exceptional high scores, but merely have an enhanced number of scores at the upper end of the expected distribution.

Adenoviridae↗