Finally, our study suffers from the limitations of retrospective studies such as missing data, lack of a gold standard against which our measurements were made, reliance on qualitative rather than quantitative agreements between hTEE-trained individuals, and the absence of concomitant conventional transthoracic echocardiography to rule out cardiac abnormalities undetected by hTEE.
Another drawback of this study is the lack of comparative data to a "gold-standard," such as catheter-based angiography, for direct comparison in the sizing of the ductus arteriosus.
When it comes to understanding the mass behavior of individuals, mobile phone data is something of a gold mine.
Employing new experimental data for validation and testing of the algorithm is difficult because it would require yet another level of experimental data as a gold standard.
Because we had access to laboratory data allowing a gold standard assessment of kidney function, we primarily assessed the presence of chronic kidney disease using estimated glomerular filtration rate (eGFR) and albuminuria rather than administrative data.
The functions that make up the implementation of this algorithm are written so that they can be applied to any type of problem with a dichotomous outcome (e.g case/control analysis), a two or three level categorical predictor variable (e.g. a genotype) and discordance data (e.g. from a subset of the records with data from a gold standard measure, as here, or from other reference sources).
The various MI methods are compared to analysis of the complete data, a gold standard, and analysis of the complete cases, which any imputation method must improve upon to be worthwhile.
By comparing the data gathered to that of a gold standard, it could be possible to 'calibrate' for the bias due to errors in exposure measurement [ 34].
A major challenge in determining the best way to elicit these data is the lack of a gold standard to assess validity of responses; an absolute measure of what patients experience is likely unachievable.
Despite rigorous training and thorough fieldwork operations and quality control measures, it is difficult to comprehensively evaluate the completeness and accuracy of HDSS data given the absence of a gold standard against which to measure findings.
