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However, index test outcomes can be non-evaluable as well, especially for tests yielding dichotomous results.
There was substantial variation between practices in the distribution of index test outcomes.
When the MAR assumption is violated, i.e., the probability of non-evaluation depends on unobserved index test outcomes, the direction and magnitude of bias are hard to predict.
On the other hand, in meta-analysis, there is little discussion on how to deal with missing index test outcomes [ 6].
The clinical questions were structured using the PIO format (Patients, Participants or Problem; Intervention or Index test; Outcomes or target conditions).
We generalize the TGLMM approach to account for missing index test outcomes by extending the "classic" 2×2 table to Table 1.
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Under MAR, T and M are independent given disease status D, where M=1,0 indicates missingness of index test outcome, D=1,0 indicates diseased or non-diseased and T=1,0 represents index test positive or negative.
Let n itd denote the cell counts in study i with index test outcome T= t and reference test outcome D= d, where t=1,0, m stands for positive, negative and missing, and d=1,0 denotes positive and negative.
This is an ordinary logistic regression model with outcome disease present (y/n) and one covariable (index test result, positive or negative), with weights for cases and controls.
Studies scored poorly for adequate description of index test and outcome measure, and consecutive recruitment.
All included studies were unclear about the use of medical intervention between the index test and outcome measure.
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