The only incorporation bias I am familiar with is related to diagnostic test accuracy studies—as opposed to prognostic studies; see this review article for definitions.
In studies in which disease is adjudicated by experts (including chart review), incorporation bias might affect study results. This occurs when the index test results are included in the adjudication process. Incorporation bias falsely results in elevated sensitivity and specificity.
They then give an example of using high-sensitivity troponin T as the “index test” for acute myocardial infarction, and mention that the authors of that study were blinded to the test results; i.e., they didn’t provide the test results to the expert panel during adjudication.
I can give some text from the book I mention in this forum post (the book is largely concerned with diagnostic test accuracy studies, such as computer-aided diagnosis [CAD]):
For studies measuring the effect of CAD, there are several ways to minimize the effects of incorporation bias: 1) include multiple expert readers in the expert panel and use the majority opinion as the gold standard diagnosis, 2) provide the expert readers only the images without the CAD marks for determining the gold standard diagnosis, and 3) for very large studies and/or when image interpretation is very time consuming, show the expert readers the compilation of findings found by the study readers and/or by CAD - ask them to make a determination about the presence or absence of a suspicious lesion, and do not tell the experts which lesions were identified by CAD and which were identified without CAD. A third option for avoiding imperfect gold standard bias is to use a mathematical correction.
I realize you mention diagnostic studies, and I’m not sure if this kind of bias “operates” the same way in prognostic/outcome studies. But perhaps someone else can reply with suggestions.
P.S. Your quote seems to come from this article. My first suggestion would be to follow their citations, although at a glance they don’t appear like they would be too helpful.