A B-SHADE based best linear unbiased estimation tool for biased samples

If not handled properly, a biased sample from a population usually results in a biased estimation of the population. The bias of a sample can be caused by selection bias or attrition bias. The B-SHADE (Biased Sentinel Hospital based Area Disease Estimation) model provides a best linear unbiased estimation (BLUE) solution by incorporating the ratio between the sample and the population, the autocorrelation within the population, and support from historical data. Three extensions are proposed and implemented in the software based on the B-SHADE model. First, we extend the original population total-oriented estimation method to population mean estimation, which is another important parameter in sampling. Second, a historical sample rather than a historical population is found to be applicable in population mean estimation. This is particularly important in practice, where there is no integrated historical population information but good historical samples. Finally, efficient sampling optimization based on the simulated annealing algorithm is proposed and implemented in the software. This is useful in evaluating the efficiency of old samples and designing new samples. A demonstration shows that when the “vertical” relationship and “horizontal” correlation can be well represented and calculated from historical data, the result estimated by the B-SHADE model is better than results from traditional simple random sampling and ratio estimation. Although the B-SHADE model was originally designed for sentinel hospitals, the software is a common tool for similar problems in different applications.