Determinants of self-reported smoking and misclassification during pregnancy, and analysis of optimal cut-off points for urinary cotinine: a cross-sectional study

10 p.

Revisiting youden’s index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous score for classification of study participants into healthy or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might be confounded by a potentially unknown variation of the cut-off value. To cope with this phenomena it is suggested to use, instead, an overall estimate of the misclassification error previously suggested and used as Youden’s index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel–Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden’s index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous Score for classification of study participants into healthy, or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between Studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might he confounded by a potentially unknown variation of the cut-off Value. To cope with this phenomena it is suggested to use, instead an overall estimate of the misclassification error previously suggested and used as Youden's index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel-Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden's index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Misclassification probability as obese or lean in hypercaloric and normocaloric diet

The aim of the present study was to determine the classification error probabilities, as lean or obese, in hypercaloric diet-induced obesity, which depends on the variable used to characterize animal obesity. In addition, the misclassification probabilities in animals submitted to normocaloric diet were also evaluated. Male Wistar rats were randomly distributed into two groups: normal diet (ND; n=3 1; 3,5 Kcal/g) and hypercaloric diet (HD; n=31; 4,6 Kcal/g). The ND group received commercial Labina rat feed and HD animals a cycle of five hypercaloric diets for a 14-week period. The variables analysed were body weight, body composition, body weight to length ratio, Lee index, body mass index and misclassification probability A 5% significance level was used. The hypercaloric pellet-diet cycle promoted increase of body weight, carcass fat, body weight to length ratio and Lee index. The total misclassification probabilities ranged from 19.21 % to 40.91 %. In Conclusion, the results of this experiment show that rnisclassification probabilities Occur when dietary manipulation is used to promote obesity in animals. This misjudgement ranges from 19.49% to 40.52% in hypercaloric diet and 18.94% to 41.30% in normocaloric diet.

Avoiding misclassification bias with the traditional Charnley classification: rationale for a fourth Charnley class BB

Long-term follow up of patients with total hip arthroplasty (THA) revealed a marked deterioration of walking capacities in Charnley class B after postoperative year 4. We hypothesized that a specific group of patients, namely those with unilateral hip arthroplasty and an untreated but affected contralateral hip was responsible for this observation. Therefore, we conducted a study taking into consideration the two subclasses that make up Charnley class B: patients with unilateral THA and contralateral hip disease and patients with bilateral THA. A sample of 15,160 patients with 35,773 follow ups that were prospectively collected over 10 years was evaluated. The sample was categorized into four classes according to a new modified Charnley classification. Annual analyses of the proportion of patients with ambulation longer than 60 min were conducted. The traditionally labeled Charnley class B consists of two very different patient groups with respect to their walking capacities. Those with unilateral THA and contralateral hip disease have underaverage walking capacities and a deterioration of ambulation beginning 3 to 4 years after surgery. Those with bilateral THA have stable overaverage walking capacities similar to Charnley class A. An extension of the traditional Charnley classification is proposed, taking into account the two different patient groups in Charnley class B. The new fourth Charnley class consists of patients with bilateral THA and was labeled BB in order to express the presence of two artificial hip joints and to preserve the traditional classification A through C.

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

A methodology for analyzing general categorical data with misclassification errors with an application in studying seat belt effectiveness. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Evaluating Misclassification Probability Using Empirical Risk

* The work is supported by RFBR, grant 04-01-00858-a

Strong inference from transect sign surveys : combining spatial autocorrelation and misclassification occupancy models to quantify the detectability of a recovering carnivore

Funding — Forest Enterprise Scotland and the University of Aberdeen provided funding for the project. The Carnegie Trust supported the lead author, E. McHenry, in this research through the award of a tuition fees bursary.

Correction of the misclassification of species in the Portuguese collection of Cucurbita pepo L. using DNA markers

In this study, the genetic variability among 130 accessions of the Portuguese germplasm collection of Cucurbita pepo L. maintained at the Banco Portugues de Germoplasma Vegetal was assessed using AFLP (amplified fragment length polymorphism) and RAPD (random amplified polymorphic DNA) techniques for the identification of a genetically diverse core group of accessions for field phenotypic analysis. The surprisingly completely different molecular patterns exhibited by multiple accessions was later confirmed in the distribution of the putative C. pepo plants into two clusters drastically separated at a very low level of genetic similarity (DICE coefficient = 0.37). Additional analyses with RAPD and ISSR (inter single sequence repeat) markers and the introduction of standard genotypes of C. maxima L. and C. moschata L. into the analyses allowed the identification of multiple accessions of the last species wrongly included in the C. pepo collection. This study is a good example of the usefulness of DNA markers in the establishment and management of plant germplasm collections.

Correction of the misclassification of species in the Portuguese collection of Cucurbita pepo L. using DNA markers

In this study, the genetic variability among 130 accessions of the Portuguese germplasm collection of Cucurbita pepo L. maintained at the Banco Portugues de Germoplasma Vegetal was assessed using AFLP (amplified fragment length polymorphism) and RAPD (random amplified polymorphic DNA) techniques for the identification of a genetically diverse core group of accessions for field phenotypic analysis. The surprisingly completely different molecular patterns exhibited by multiple accessions was later confirmed in the distribution of the putative C. pepo plants into two clusters drastically separated at a very low level of genetic similarity (DICE coefficient = 0.37). Additional analyses with RAPD and ISSR (inter single sequence repeat) markers and the introduction of standard genotypes of C. maxima L. and C. moschata L. into the analyses allowed the identification of multiple accessions of the last species wrongly included in the C. pepo collection. This study is a good example of the usefulness of DNA markers in the establishment and management of plant germplasm collections.