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The future of clinical trials in urological oncology

Nature Reviews Urology volume 16, pages 722–733 (2019)Cite this article

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Abstract

Well-designed clinical trials in urological oncology help to guide treatment decisions and aid in counselling patients, ultimately serving to improve outcomes. Since the term evidence-based medicine was first used by Gordon Guyatt in 1991, a renewed emphasis on methodology, transparent trial design and study reporting has helped to improve clinical research and in turn, the landscape of medical literature. Novel clinical trial designs (including multi-arm, multistage trials, basket and umbrella studies and research from big data sources, such as electronic health records, administrative claims databases and quality monitoring registries) are well suited to advance innovation in urological oncology. Existing urological clinical trials are often limited by small numbers, are statistically underpowered and many face difficulties with accrual. Thus, efforts to improve trial design are of considerable importance. The development and use of standard outcome sets and adherence to reporting guidelines offer researchers the opportunity to guide value-oriented care, minimize research waste and efficiently identify solutions to the unanswered questions in urology cancer care.

Key points

  • Innovations in trial design, such as the multi-arm, multistage (MAMS) study type as well as basket and umbrella trial designs, are being used to investigate therapies for urological cancers in a cost-efficient and rapid manner.

  • Urologists should become familiar with the strengths and limitations of big data sources, such as information gathered from systems including electronic health records, administrative claims databases, prescription monitoring registries, whole-genome sequencing and social media.

  • Standard (or core) outcome sets are agreed sets of standardized outcomes that should be measured and reported as a minimum in all clinical trials in a specific area of health or health care; these are developed in conjunction with a panel of treatment experts and patient advocates. Their development and adoption can improve the utility of clinical trial data.

  • Familiarity and adherence to reporting guidelines such as CONSORT and STARD by trial designers, researchers, publishers and users of the medical literature can help to standardize and improve the transparency of trial reporting.

  • Future trials in urological oncology should focus on opportunities to fill gaps within areas of existing guideline deficiency, including areas where current clinical guidance is driven primarily by retrospective observational data or expert opinion.

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Fig. 1: MAMS trial versus traditional RCTs.
Fig. 2: Basket and umbrella trials.

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Authors and Affiliations

  1. Minneapolis VA Medical Center and University of Minnesota Department of Urology, Minneapolis, MN, 55417, USA

    Vikram M. Narayan & Philipp Dahm

  2. University of Texas MD Anderson Cancer Center, Department of Urology, Houston, TX, 77030, USA

    Vikram M. Narayan

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Both authors made substantial contribution to the discussion of content, wrote the article and reviewed and edited the article before submission. V.M.N. researched data for the article.

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Correspondence to Philipp Dahm.

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Glossary

Type I error

The incorrect rejection of a true null hypothesis, resulting in a false-positive conclusion.

Family-wise error

The occurrence of making a type I (false-positive) error, and doing so within the context of multiple testing (for example, when more than one hypothesis is being tested).

Natural language processing software

Software that is able to impute discrete data from free-text information entered using human syntax; for example, natural language processing software could be used to scan a patient’s progress note and identify the patient’s age, demographic information, current symptoms and treatment plan.

Meaningful use reporting initiatives

US government standards for electronic medical record use, specifically focused on promoting the adoption of electronic medical records, exchange of patient information electronically and standardizing methods for electronic prescribing, care coordination, case reporting and billing.

Cohort inception tool

A method of identifying study groups (cohorts), usually used within the contexts of claims or quality registries.

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Narayan, V.M., Dahm, P. The future of clinical trials in urological oncology. Nat Rev Urol 16, 722–733 (2019). https://doi.org/10.1038/s41585-019-0243-x

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