Estimating Sensitivity of Laboratory Testing for Influenza in Canada through Modelling

Background

The weekly proportion of laboratory tests that are positive for influenza is used in public health surveillance systems to identify periods of influenza activity. We aimed to estimate the sensitivity of influenza testing in Canada based on results of a national respiratory virus surveillance system.

Methods and Findings

The weekly number of influenza-negative tests from 1999 to 2006 was modelled as a function of laboratory-confirmed positive tests for influenza, respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses, seasonality, and trend using Poisson regression. Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests. The sensitivity of influenza testing was estimated to be 33% (95%CI 32–34%), varying from 30–40% depending on the season and region.

Conclusions

The estimated sensitivity of influenza tests reported to this national laboratory surveillance system is considerably less than reported test characteristics for most laboratory tests. A number of factors may explain this difference, including sample quality and specimen procurement issues as well as test characteristics. Improved diagnosis would permit better estimation of the burden of influenza.     

HOW TO ESTIMATE AND USE THE VARIANCE OF d' FROM DIFFERENCE TESTS

d' is an estimate of δ, a measure of the degree of sensory difference between two products, that can be obtained easily using tables, from the proportion of difference tests performed correctly. Tables of δ are available for the 2-AFC, 3-AFC, triangular and duo-trio tests. Tables for calculating the variance of d' for these tests are provided in this paper. They can be used for comparison of d's, especially for those obtained from different difference tests. A simple procedure is described here for computing values for the variance of d'. Having obtained the variance, confidence intervals for d' can be obtained, tests of significance for d' can be made as well as tests of whether two or more d's are significantly different. The formula and tables for the number of judgments required for the estimation of δ are given also in this paper.     

Most Powerful Permutation Invariant Tests for Relatedness Hypotheses Using Genotypic Data

The problem of inferring kinship structure among a sample of individuals using genetic markers is considered with the objective of developing hypothesis tests for genetic relatedness with nearly optimal properties. The class of tests considered are those that are constrained to be permutation invariant, which in this context defines tests whose properties do not depend on the labeling of the individuals. This is appropriate when all individuals are to be treated identically from a statistical point of view. The approach taken is to derive tests that are probably most powerful for a permutation invariant alternative hypothesis that is, in some sense, close to a null hypothesis of mutual independence. This is analagous to the locally most powerful test commonly used in parametric inference. Although the resulting test statistic is a U-statistic, normal approximation theory is found to be inapplicable because of high skewness. As an alternative it is found that a conditional procedure based on the most powerful test statistic can calculate accurate significance levels without much loss in power. Examples are given in which this type of test proves to be more powerful than a number of alternatives considered in the literature, including Queller and Goodknight's (1989) estimate of genetic relatedness, the average number of shared alleles (Blouin, 1996), and the number of feasible sibling triples (Almudevar and Field, 1999).     

On Testing Equality of Means in the Paired Case with Incomplete Data on Both Responses

Several non iterative tests for the equality of two correlated means with missing data on both responses are compared by means of Monte Carlo studies. The main results are that a test due to LIN and STIVERS (1974) is to be preferred when the number of complete pairs is large, a WELCH type statistic with degrees of freedom modified in this paper when the correlation between the two responses is known to be low, and one of four suggested, one in this paper, maximum likelihood based tests when the number of complete pairs is small and there is no large variance inequality believed to exist.     

Estimating the accuracy of polymerase chain reaction-based tests using endpoint dilution

Polymerase chain reaction (PCR)-based tests for various microorganisms or target DNA sequences are generally acknowledged to be highly "sensitive," yet the concept of sensitivity is ill-defined in the literature on these tests. We propose that sensitivity should be expressed as a function of the number of target DNA molecules in the sample (or specificity, when the target number is 0). However, estimating this "sensitivity curve" is problematic, since it is difficult to construct samples with a fixed number of targets. Nonetheless, using serially diluted replicate aliquots of a known concentration of the target DNA sequence, we show that it is possible to disentangle random variations in the number of target DNA molecules from the underlying test sensitivity. We develop parametric, nonparametric, and semiparametric (spline-based) models for the sensitivity curve. The methods are compared on a new test for M. genitalium.     

The use of PCR to aid in the rapid identification of Vibrio harveyi isolates

AIMS: Vibrio harveyi is an important pathogen, causing potential devastation to marine aquaculture. This organism, however, is extremely difficult to identify because it is phenotypically diverse. Biochemical identification can involve many tests and take weeks to perform. The aim of this work is to develop a PCR that can reduce the number of biochemical tests, and the time taken, to get a definitive identification of this organism. METHODS AND RESULTS: The PCR was developed using 16S rDNA sequences from a number of V. harveyi strains, and other vibrios. The described test gave positive results for all strains of V. harveyi tested. However, some strains of V. alginolyticus also gave positive results and a small number of biochemical tests were required to differentiate between these two species. This indicated that preisolation of the bacteria was needed and therefore the test was not applicable to the testing of mixed populations directly. CONCLUSION, SIGNIFICANCE AND IMPACT OF THE STUDY: The duration of identification of this species was significantly reduced from a number of weeks to a few days. Hence, diagnosis of affected animals will be faster and earlier treatment can be administered which may increase the survival rate from vibriosis.     

A STATISTICAL ANALYSIS OF DIFFERENCE TESTS WITH REPLICATIONS

This paper proposes a statistical method for difference tests with repetitions. Classical methods for difference tests are based upon the binomial distribution, and are not concerned with the number of repetitions per judge. But when more than one replication of a difference test is required, judgements from different judges are more independent than replicates from the same judge; these two cannot be combined in the classical methods. In this paper, we propose another approach that takes into account two points: the number of repetitions per judge, and the differences within subjects. Two examples are presented to illustrate this approach.     

A bootstrap test for the analysis of microarray experiments with a very small number of replications

In microarray studies it is common that the number of replications (i.e. the sample size) is small and that the distribution of expression values differs from normality. In this situation, permutation and bootstrap tests may be appropriate for the identification of differentially expressed genes. However, unlike bootstrap tests, permutation tests are not suitable for very small sample sizes, such as three per group. A variety of different bootstrap tests exists. For example, it is possible to adjust the data to have a common mean before the bootstrap samples are drawn. For small significance levels, which can occur when a large number of genes is investigated, the original bootstrap test, as well as a bootstrap test suggested for the Behrens-Fisher problem, have no power in cases of very small sample sizes. In contrast, the modified test based on adjusted data is powerful. Using a Monte Carlo simulation study, we demonstrate that the difference in power can be huge. In addition, the different tests are illustrated using microarray data.     

Comparison of independent samples of high-dimensional data by pairwise distance measures

Pairwise distance or association measures of sample elements are often used as a basis for hierarchical cluster analyses. They can also be used in tests for the comparison of pre-defined subgroups of the total sample. Usually this is done with permutation tests In this paper, we compare such a procedure with alternative tests for high-dimensional data based on spherically distributed scores in simulation experiments and with real data. The tests based on the pairwise distance or similarity measures perform quite well in this comparison. As the number of possible permutations is small in very small samples, this might restrict the use of the test. Therefore, we propose an exact parametric small sample version of the test using randomly rotated samples.     

A covariance estimator for GEE with improved small-sample properties

In this paper, we propose an alternative covariance estimator to the robust covariance estimator of generalized estimating equations (GEE). Hypothesis tests using the robust covariance estimator can have inflated size when the number of independent clusters is small. Resampling methods, such as the jackknife and bootstrap, have been suggested for covariance estimation when the number of clusters is small. A drawback of the resampling methods when the response is binary is that the methods can break down when the number of subjects is small due to zero or near-zero cell counts caused by resampling. We propose a bias-corrected covariance estimator that avoids this problem. In a small simulation study, we compare the bias-corrected covariance estimator to the robust and jackknife covariance estimators for binary responses for situations involving 10-40 subjects with equal and unequal cluster sizes of 16-64 observations. The bias-corrected covariance estimator gave tests with sizes close to the nominal level even when the number of subjects was 10 and cluster sizes were unequal, whereas the robust and jackknife covariance estimators gave tests with sizes that could be 2-3 times the nominal level. The methods are illustrated using data from a randomized clinical trial on treatment for bone loss in subjects with periodontal disease.     

L'utilisation des tests diagnostiques dans une unité de médecine familiale.

For a random sample of 1029 visits occurring over a 1-year period in a family medicine service 1067 diagnostic tests were done within 1 week (or within 3 weeks in the case of nuclear medicine) following the visit; this represents a mean of 1.04 tests, costing $ 8.30, per visit. There was no test ordered in most (62.5%) of the visits. The results of 909 tests were recorded; 36.6% were abnormal. The pattern of use of diagnostic tests varied considerably among the physicians; however, no association was observed between this pattern and the status of the physician, the site of the encounter, or the age or sex of the patient. There was a weak and not statistically significant correlation between the number of problems identified and the number of tests with abnormal results per visit. These results suggest that the problem of overuse of diagnostic tests may not be as acute in a family medicine service as it has been observed to be in other settings.     

Instrumentos para medir la sobrecarga en el cuidador informal del paciente con demencia

The complexity of dementia caregiver burden concept has led to a significant number of assessment tests using various approaches. For this reason, a review of these measurementss could be useful for clinical or research purposes.     

Testing Heterozygote Excess and Deficiency

Currently used tests of Hardy-Weinberg proportions do not take into account the nature of the alternative hypothesis, which is generally a heterozygote deficiency. Different exact tests, appropriate for small sample size and large number of alleles, are proposed in this perspective, and their properties are evaluated by power comparisons. Some tests are found to be close to optimal for the detection of inbreeding or heterozygote excess, one of which is a score test closely related to Robertson and Hill's estimator of the inbreeding coefficient. This test is also easily applied to multiple samples. Such tests are not always the most appropriate if alternative hypotheses differ from those considered here.     

Comparisons of site- and haplotype-frequency methods for detecting positive selection

In this report, we compare the differences between various site- and haplotype-frequency tests in their power to detect positive selection by doing computer simulations. Our results are the following. 1) Although haplotype-frequency tests that are conditional on the number of haplotypes (K) were developed for nonrecombining haplotypes, these tests are insensitive to recombination. Such tests, including the Ewens-Watterson (EW) test, can therefore be applied to recombining haplotypes. 2) Tests conditional on the number of segregating sites (S) become overly conservative in the presence of recombination. 3) The EW test is usually the most powerful test during the sweep phase, especially when the local recombination rate is high. 4) The "extended haplotype homozygosity" test relies heavily on the prior knowledge of the target of selection. With that knowledge, it is the most powerful test, whereas in the absence of this prior information, the test has little power. We also study the sensitivities of the haplotype-frequency tests to background selection and various demographic forces. We find that these tests are sensitive to some forces other than positive selection. To alleviate the problem of low specificity, compound tests, such as the DH test (Zeng et al. 2006), may be a solution. In the companion paper (Zeng K, Shi S, Wu C-I, in preparation), we use the EW test to devise 2 compound tests, which are more powerful in detecting positive selection than DH, but are also relatively insensitive to demography.     

Robust trend tests for genetic association in case-control studies using family data

We studied a trend test for genetic association between disease and the number of risk alleles using case-control data. When the data are sampled from families, this trend test can be adjusted to take into account the correlations among family members in complex pedigrees. However, the test depends on the scores based on the underlying genetic model and thus it may have substantial loss of power when the model is misspecified. Since the mode of inheritance will be unknown for complex diseases, we have developed two robust trend tests for case-control studies using family data. These robust tests have relatively good power for a class of possible genetic models. The trend tests and robust trend tests were applied to a dataset of Genetic Analysis Workshop 14 from the Collaborative Study on the Genetics of Alcoholism.     

Correlations among assays of porcine semen quality following cryopreservation

Correlations between in vitro tests of semen quality, used to predict the in vivo fertilizing potential of sperm, indicate that the tests may substitute for each other in predicting fertilizing potential. Lack of correlation between tests suggest that both tests should be used to estimate the fertilizing potential. The purpose of this study was to establish correlations between several in vitro tests of porcine semen quality following freezing. Tests of motility with and without caffeine, spermatozoa with normal apical ridges, sephadex filtration with and without caffeine and acrosin activity were all correlated with each other. Correlations among these tests ranged from 0.45 to 0.83 (P<0.05). Assays for glutamic oxalacetic transaninase (GOT) were not consistently correlated with other tests. None of these tests of semen quality were correlated with the sperm penetration assay except for the test of motility without caffeine, which was correlated with the number of penetrations per hamster oocyte (r = 0.71, P<0.05).     

A new gene-finding tool: using the Caenorhabditis elegans operons for identifying functional partner proteins in human cells

How can a large number of different phenotypes be generated by a limited number of genotypes? Promiscuity between different, structurally related and/or unrelated proteins seems to provide a plausible explanation to this pertinent question. Strategies able to predict such functional interrelations between different proteins are important to restrict the number of putative candidate proteins, which can then be subjected to time-consuming functional tests. Here we describe the use of the operon structure of the nematode genome to identify partner proteins in human cells. In this work we focus on ion channels proteins, which build an interface between the cell and the outside world and are responsible for a growing number of diseases in humans. However, the proposed strategy for the partner protein quest is not restricted to this scientific area but can be adopted in virtually every field of human biology where protein-protein interactions are assumed.     

Some simple tests for spatial effects around putative sources of health risk

The need for tests dealing with different features of small area health data is less important with the increase in computation speed of computers and the access to MCMC methods. However there are many situations where exploratory testing could be useful and where MCMC methods are not readily usable or available. In this paper, a number of simple tests are derived for the logistic model for case events. This model assumes that a control disease is available and that the events have a binary label relating to case or control state. The tests are derived from likelihood considerations and Monte Carlo critical regions are examined. A simulated evaluation of the tests is presented in terms of Monte Carlo power. A data example is considered.     

Independence tests for VNTR alleles defined as quantile bins.

VNTR fragment lengths in three databases maintained by the FBI for forensic purposes were partitioned into quantile bins, and tests for independence of the two bins at each of six loci were conducted. Whether independence was declared depended on the number of quantiles used. For a large number of quantile bins, equal to the number of fixed bins used by the FBI, 10 of 18 likelihood-ratio tests showed significant departures from independence when all genotypes were considered, and this changed to 7 of 18 when only heterozygotes were tested. This is in contrast to likelihood-ratio tests on fixed bins, when there were five significant departures over all genotypes and two departures for heterozygotes.     

Bootstrap method of interior-branch test for phylogenetic trees

Statistical properties of the bootstrap test of interior branch lengths of phylogenetic trees have been studied and compared with those of the standard interior-branch test in computer simulations. Examination of the properties of the tests under the null hypothesis showed that both tests for an interior branch of a predetermined topology are quite reliable when the distribution of the branch length estimate approaches a normal distribution. Unlike the standard interior-branch test, the bootstrap test appears to retain this property even when the substitution rate varies among sites. In this case, the distribution of the branch length estimate deviates from a normal distribution, and the standard interior-branch test gives conservative confidence probability values. A simple correction method was developed for both interior- branch tests to be applied for testing the reliability of tree topologies estimated from sequence data. This correction for the standard interior-branch test appears to be as effective as that obtained in our previous study, though it is much simpler. The bootstrap and standard interior-branch tests for estimated topologies become conservative as the number of sequence groups in a star-like tree increases.