Computational protocol: Repeat Prostate Biopsy Strategies after Initial Negative Biopsy: Meta-Regression Comparing Cancer Detection of Transperineal, Transrectal Saturation and MRI Guided Biopsy

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Protocol publication

[…] The following data were extracted from each paper: first author, year of publication, study size, mean patient age, mean PSA at repeat biopsy, mean number of previous biopsy episodes, mean number of biopsy cores taken at repeat biopsy, cancer detection rate, and Gleason scores of detected cancers. Statistical analysis was performed by RCH and RAP. For each strategy, weighted summary statistics, which weight according to the sample size of each individual study, were calculated for mean patient age, mean PSA, mean number of previous biopsy episodes, and mean number of cores obtained by the repeat biopsy strategy. We were not able to weight by the inverse study variance for these variables because estimates of within-study variability were unavailable. For the primary outcome (cancer detection rate) however an estimate of the variance could be extracted using the formula for the variance of a proportion, and so this variable was weighted by the inverse variances. A meta-regression analysis compared the overall prostate cancer detection rate (percentage of patients diagnosed with prostate cancer) between the three re-biopsy strategies having adjusted for differences in mean patient age and PSA between the strategy cohorts. In order to account for heterogeneity between studies, a random-effects meta-regression analysis was conducted using inverse-variance weights. The amount of residual heterogeneity between studies was assessed using a Q-test, based on the DerSimonian-Laird estimator. Publication bias was assessed using a funnel plot of the inverse sample variance against model residuals () and a rank correlation test for funnel plot asymmetry performed . A sensitivity analysis was then performed to see if adjusting for the number of previous biopsy episodes affected the meta-regression results. Finally, to compare average pathological outcomes across the repeat biopsy strategies, a Fisher’s Exact test was applied to test for any association between average tumour grade and re-biopsy strategy. The ‘metafor’ package in R software was used to perform the meta-regression analysis. R software was also used to produce the bubble plot and perform the Fisher’s Exact test for the Gleason score analysis. SPSS software was used for all other analyses (SPSS/PASW for Windows, Rel. 18.0.3. 2010. Chicago: SPSS Inc.). […]

Pipeline specifications

Software tools metafor, SPSS
Applications Miscellaneous, Gene expression microarray analysis
Organisms Homo sapiens
Diseases Neoplasms, Prostatic Neoplasms