Results
We present the application of MI meta-analysis based on two cases previously published in literature.
Case 1. Detection of endometrial cancer using endovaginal ultrasonography (US).
For this case, we used data presented in Deeks 22, originally published in Smith-Bindman et al 23. The dataset is the result of a systematic review process on 35 papers presenting the diagnostic performance of endovaginal US in the detection of endometrial cancer. Evidence synthesis tables on test sensitivity and specificity are provided in Deeks 22.
Figure 1 displays the meta-analytic summary plots based on US studies. It includes the summary ROC curve, individual study estimate, and summary point estimate of the “traditional” measures of performance of endovaginal US in the detection of endometrial cancer. It is difficult to interpret, how “good” the test is, and in particular how much uncertainty the test reduced in each study where US was evaluated. For example, a US test diagnosing endometrial cancer is 40% specific and 94% sensitive. The combination of these values is difficult to interpret and may lead to inappropriate assessment (e.g. one could favour the test due to its high sensitivity, ignoring its low specificity). In terms of MI however, the test reduces diagnostic uncertainty by 10%, which is marginal and thus the test is clearly not very useful.
Figure 2 demonstrates meta-analysis of MI. We can clearly see that the US results provided only 0.05 (0.04 to 0.07) bits of information (recall, that the maximum amount information in the binary diagnostic case is 1). Although this gives us an estimate about overall diagnostic performance of US for diagnosis of endometrial cancer, what we really want to know is the amount of diagnostic uncertainty the US can possibly reduce (on scale 0 to 100%). This can be expressed by calculating RMI.
Figure 3a shows the performance of US expressed in terms of RMI. The information presented is much clearer: a decision-maker has much better understanding on how much diagnostic uncertainty was reduced in each study. The pooled estimate of the reduction in diagnostic uncertainty is 13% for pre-test probability of disease 14%. That is, US can reduce the uncertainty related to endometrial cancer by 13%. Figure 3b presents the sample size of each study.