Abstract
Background:
The gold standard in conducting clinical trials/studies is to follow what is prespecified in the study protocol. However, deviations from the study protocol may occur. This article discusses the issues of protocol deviation in pivotal clinical trials or studies for medical device and provides statistical approaches to mitigating bias such as selection bias specifically for diagnostic test clinical trials or studies.
Method:
Bias correction methods are developed for 2 specific types of selection biases, prescreening bias and verification bias. Statistical approaches are discussed on how to estimate device performance adjusted for enrollment enrichment and discrepant testing results. We use an FDA-approved Roche Cobas Human Papillomavirus (HPV) test for detecting high-grade cervical disease (>CIN2) as an example to illustrate how to correct for verification bias. A recently FDA-cleared Microarray Assay in detecting copy number variation is used to illustrate how to properly estimate sensitivity and specificity for the discrepancy analysis.
Results:
The unadjusted sensitivity and specificity based on verified samples were 83.2% and 60.4% for the Roche’s HPV test. However, using the correction method with the missing-at-random assumption, the verification bias–adjusted sensitivity and specificity were 34.5% and 93.6%, respectively.
Conclusion:
Protocol deviations can lead to biased estimates of device clinical performance if not handled appropriately. Statistical methods correcting for bias and protocol deviations are recommended in estimating device performance.
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