Comparing Micro-Ultrasound to mpMRI in Detecting Clinically Significant Prostate Cancer

Objectives To compare the performance of micro-ultrasound (mUS) with multi-parametric magnetic resonance imaging (mpMRI) in detecting clinically significant prostate cancer. Materials and Methods Retrospective data from consecutive patients with any indication for prostate biopsy in 2 academic institutions were included. The operator, blinded to mpMRI, would first scan the prostate and annotate any mUS lesions. All mUS lesions were biopsied. Any mpMRI lesions that did not correspond to mUS lesion upon unblinding were additionally biopsied. Grade group (GG) ≥ 2 was considered clinically significant cancer. The Jeffreys interval method was used to compare performance of mUS with mpMRI with the non-inferiority limit set at −5%. Results Imaging and biopsy were performed in 82 patients with 153 lesions. mUS had similar sensitivity to mpMRI (per-lesion analysis: 78.4% versus 72.5%), but lower specificity, positive predictive value, negative predictive value, and area under the curve. Micro-ultrasound found GG ≥ 2 in 13% of cases missed by mpMRI, while mpMRI found GG ≥ 2 in 11% of cases missed by mUS. The difference 0.020 (95% CI −0.070 to 0.110) was not statistically significant (P = 0.33). Conclusion The sensitivity of mUS in detecting GG ≥ 2 disease was similar to that of mpMRI, but the specificity was lower. Further evaluation with a larger sample size and experienced operators is warranted.

Confidence interval methods for antimicrobial resistance surveillance data

Background Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents. Methods We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume data independence. Using samples of the full dataset with increasing facility coverage levels, we examined how likely the estimated confidence intervals were to include the population mean. Results Methods constructing 95% confidence intervals while accounting for possible within-cluster correlations (Survey and standard methods adjusted to employ cluster-robust errors), were more likely to include the sample mean than standard methods (Logit, Wilson score and Jeffreys interval) operating under the assumption of independence. While increased geographical coverage improved the probability of encompassing the mean for all methods, large samples still did not compensate for the bias introduced from the violation of the data independence assumption. Conclusion General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.

Improving interval estimation of binomial proportions

In this paper, we propose one new confidence interval for the binomial proportion; our interval is based on the Edgeworth expansion of a logit transformation of the sample proportion. We provide theoretical justification for the proposed interval and also compare the finite-sample performance of the proposed interval with the three best existing intervals—the Wilson interval, the Agresti–Coull interval and the Jeffreys interval—in terms of their coverage probabilities and expected lengths. We illustrate the proposed method in two real clinical studies.