Data Mining in Urology: Understanding Real-world Treatment Pathways for Lower Urinary Tract Systems via Exploration of Big Data

With an increasing number of novel therapeutic options for lower urinary tract symptoms (LUTS), the spectrum of potential treatment pathways resulting from different combinations of treatment decisions is expanding and evolving. Treatment decisions are frequently made with little or no evidence from randomized controlled trials (RCTs) and thus require evidence from other data sources. Clinical routine data reflect real-world treatment pathways. However, evidence for LUTS from routine data means that heterogeneous pathways need to be simultaneously analyzed for compiling evidence in the absence of RCTs. Statistical multi-state model approaches can provide a powerful framework for achieving this goal. More extensive statistical and methodological efforts in the area of similarity of small data are needed to enable the valid pooling of pathways towards joining evidence. Patient summary: Treatment decisions should rely primarily on evidence from clinical trials. When treatment for which there is limited trial evidence needs to be provided, analysis of results from routine clinical practice can represent valuable complementary evidence, but this requires integration of data from heterogeneous treatment pathways. (C) 2022 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Automated summary

With the increasing number of treatment options, different combinations of treatment decisions lead to diverse treatment pathways. Lack of randomized controlled trial evidence necessitates the integration of evidence from routine clinical data. Statistical multi-state models offer a powerful framework for analyzing heterogeneous pathways.