Sentence examples for misclassification of area from inspiring English sources

The advantage of having contemporaneously recorded addresses helped in achieving almost 100% linkage of addresses and postcodes to areas for all three study years, reducing missing data, and misclassification of area socioeconomic exposure.

There may also be misclassification of area-based SEP and urban/rural residence in cancer registrations, but in each instance, any such misclassification is unlikely to be associated with subsequent survival and, if anything, is likely to produce underestimates of the differences in survival between these various demographic groups.

A combination of the two approaches is applied because remote sensing data may lead to misclassification of large areas of trees or grass within an urban boundary, while population density mapping may miss isolated areas of manufacturing, transportation or resource extraction.

Evaluation of the method showed that 74% of the defoliation was detected with a misclassification of undisturbed areas of 39% in MODIS pixels with at least 50% birch forest cover.

The lower SUV obtained with KF compared to A50% was caused by the larger tumour segmentation as well as by KF misclassification of highly active areas in the lung tumour as being liver, which were excluded from the obtained tumour segmentation.

These include using a p-value threshold of 0.001 from a chi-square distribution with six degrees of freedom (as opposed to three in order to provide a more stringent cutoff) on the minus 2 log likelihood difference from the models, as well as a simultaneous requirement of misclassification area <0.1 based on the best estimated bimodal distribution [ 21, 22, 45].

Misclassification of socioeconomic status by area of residence is likely to be very minor because the areas used are very small (N=34 378, mean population 1500) and they were designed to be fairly homogeneous for socioeconomic status.

The latter method identifies local areas with dissimilar rates and excludes these from the local high- or low-rate clusters, thus preventing misclassification of FSAs in study areas with relatively high numbers of dissimilar neighbouring areas.

Sensitivity analyses were done under several different assumptions within the following three areas: misclassification of surgery; selection bias, and unmeasured confounder.

Such an effect would attenuate rather than increase the true effects of multidisciplinary care, by introducing some misclassification of intervention and non-intervention areas.

Anatomical differences might affect misclassification of disease severity and represent an area where research is needed.