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Kidney disease trials for the 21st century: innovations in design and conduct

Nature Reviews Nephrology volume 16, pages 173–185 (2020)Cite this article

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Abstract

Compared to other specialties, nephrology has reported relatively few clinical trials, and most of these are too small to detect moderate treatment effects. Consequently, interventions that are commonly used by nephrologists have not been adequately tested and some may be ineffective or harmful. More randomized trials are urgently needed to address important clinical questions in patients with kidney disease. The use of robust surrogate markers may accelerate early-phase drug development. However, scientific innovations in trial conduct developed by other specialties should also be adopted to improve trial quality and enable more, larger trials in kidney disease to be completed in the current era of burdensome regulation and escalating research costs. Examples of such innovations include utilizing routinely collected health-care data and disease-specific registries to identify and invite potential trial participants, and for long-term follow-up; use of prescreening to facilitate rapid recruitment of participants; use of pre-randomization run-in periods to improve participant adherence and assess responses to study interventions prior to randomization; and appropriate use of statistics to monitor studies and analyse their results. Nephrology is well positioned to harness such innovations due to its advanced use of electronic health-care records and the development of disease-specific registries. Adopting a population approach and efficient trial conduct along with challenging unscientific regulation may increase the number of definitive clinical trials in nephrology and improve the care of current and future patients.

Key points

  • Nephrology has the potential to benefit from large streamlined trials similar to those that have led to advances in cardiology and diabetology.

  • As effective interventions are developed and population-level risks fall, larger trial sample sizes are often needed to ensure sufficient statistical power to demonstrate the effects of new therapies.

  • When moderate effect sizes are anticipated, real-world evidence from association studies cannot provide a reliable estimate of the effect of an intervention; only ‘randomization’ guarantees the elimination of moderate biases.

  • Potential surrogate outcomes in renal trials include change in albuminuria and estimated glomerular filtration rate slopes; however, no surrogate exists for safety, and large trials with sufficiently long follow-up remain necessary.

  • Precision medicine approaches have the potential to reduce trial sample sizes but might exclude at-risk groups who could potentially benefit from an intervention and can lead to time-consuming and costly recruitment procedures.

  • In this era of complex research governance and burdensome regulation, innovations in trial conduct to enable large-scale invitation, better adherence and low-cost follow-up may be more important than innovations in trial design.

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Fig. 1: Avoiding bias in responder analyses.
Fig. 2: Parallel-group randomized trial designs.
Fig. 3: Cluster randomized trial designs.
Fig. 4: Master protocol trial designs.

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Acknowledgements

The Medical Research Council Population Health Research Unit at the University of Oxford is funded by the UK Medical Research Council. W.G.H. is funded by a Medical Research Council-Kidney Research UK Professor David Kerr Clinician Scientist Award.

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Nature Reviews Nephrology thanks M. Takeuchi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Medical Research Council Population Health Research Unit at the University of Oxford, Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Oxford, UK

    William G. Herrington, Natalie Staplin & Richard Haynes

  2. Oxford Kidney Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK

    William G. Herrington & Richard Haynes

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All authors researched the data for the article, contributed to discussions of the content, wrote the text and reviewed or edited the manuscript before submission.

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W.G.H., R.H. and N.S. report grants to the Renal Studies Group at the University of Oxford from Novartis, Boehringer Ingelheim, and Pfizer. CTSU has a staff policy of not accepting honoraria or other payments from the pharmaceutical industry, except for the reimbursement of costs to participate in scientific meetings (www.ctsu.ox.ac.uk).

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Glossary

Propensity-score matching

A statistical matching technique employed in observational studies to reduce the potential for bias by factors that predict use of an intervention rather than the effect of the intervention. Elimination of such biases can only be ensured by randomization in a trial.

Mendelian randomization

(MR). A method of analysing observational study data in which allele variation in genes encoding risk factors or biomarkers are used to infer whether a risk factor may be a cause of disease. Such design requires assumptions but may control for biases (for example, reverse causation or confounding) that are inherent in classic observational studies.

Statistical tests for heterogeneity

In trials, this term refers to a statistical test of whether effects in a particular subgroup of participants differ significantly from the overall effect (for example, a chi-squared test).

Linear mixed models

Extension of simple linear models to include random effects (for example, patient-specific intercepts and/or slopes) to account for dependence between data points (such as multiple estimated glomerular filtration rate measurements from the same patient).

Shared parameter models

Statistical method that jointly models longitudinal data (using linear mixed models) and a censoring event (using a survival model) to enable unbiased estimation of a longitudinal outcome (for example, rate of decline in estimated glomerular filtration rate) in the presence of a non-ignorable dropout mechanism (in other words, patients are dropped from analyses for reasons related to their decline, such as end-stage renal disease or death).

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Herrington, W.G., Staplin, N. & Haynes, R. Kidney disease trials for the 21st century: innovations in design and conduct. Nat Rev Nephrol 16, 173–185 (2020). https://doi.org/10.1038/s41581-019-0212-x

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