Pharma Focus Asia

Methods for Specifying the Target Difference in a Randomised Controlled Trial: The Difference ELicitation in TriAls (DELTA) Systematic Review

Jenni Hislop, Temitope E. Adewuyi, Luke D. Vale, Kirsten Harrild, Cynthia Fraser, Tara Gurung, Douglas G. Altman, Andrew H. Briggs, Peter Fayers, Craig R. Ramsay, John D. Norrie, Ian M. Harvey, Brian Buckley, Jonathan A. Cook mail, for the DELTA group

Abstract

Background

Randomised controlled trials (RCTs) are widely accepted as the preferred study design for evaluating healthcare interventions. When the sample size is determined, a (target) difference is typically specified that the RCT is designed to detect. This provides reassurance that the study will be informative, i.e., should such a difference exist, it is likely to be detected with the required statistical precision. The aim of this review was to identify potential methods for specifying the target difference in an RCT sample size calculation.

Methods and Findings

A comprehensive systematic review of medical and non-medical literature was carried out for methods that could be used to specify the target difference for an RCT sample size calculation. The databases searched were MEDLINE, MEDLINE In-Process, EMBASE, the Cochrane Central Register of Controlled Trials, the Cochrane Methodology Register, PsycINFO, Science Citation Index, EconLit, the Education Resources Information Center (ERIC), and Scopus (for in-press publications); the search period was from 1966 or the earliest date covered, to between November 2010 and January 2011. Additionally, textbooks addressing the methodology of clinical trials and International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) tripartite guidelines for clinical trials were also consulted. A narrative synthesis of methods was produced. Studies that described a method that could be used for specifying an important and/or realistic difference were included. The search identified 11,485 potentially relevant articles from the databases searched. Of these, 1,434 were selected for full-text assessment, and a further nine were identified from other sources. Fifteen clinical trial textbooks and the ICH tripartite guidelines were also reviewed. In total, 777 studies were included, and within them, seven methods were identified—anchor, distribution, health economic, opinion-seeking, pilot study, review of the evidence base, and standardised effect size.

Conclusions

A variety of methods are available that researchers can use for specifying the target difference in an RCT sample size calculation. Appropriate methods may vary depending on the aim (e.g., specifying an important difference versus a realistic difference), context (e.g., research question and availability of data), and underlying framework adopted (e.g., Bayesian versus conventional statistical approach). Guidance on the use of each method is given. No single method provides a perfect solution for all contexts.

Please see later in the article for the Editors' Summary

Editors' Summary

Background

A clinical trial is a research study in which human volunteers are randomized to receive a given intervention or not, and outcomes are measured in both groups to determine the effect of the intervention. Randomized controlled trials (RCTs) are widely accepted as the preferred study design because by randomly assigning participants to groups, any differences between the two groups, other than the intervention under study, are due to chance. To conduct a RCT, investigators calculate how many patients they need to enroll to determine whether the intervention is effective. The number of patients they need to enroll depends on how effective the intervention is expected to be, or would need to be in order to be clinically important. The assumed difference between the two groups is the target difference. A larger target difference generally means that fewer patients need to be enrolled, relative to a smaller target difference. The target difference and number of patients enrolled contribute to the study's statistical precision, and the ability of the study to determine whether the intervention is effective. Selecting an appropriate target difference is important from both a scientific and ethical standpoint.

Why Was This Study Done?

There are several ways to determine an appropriate target difference. The authors wanted to determine what methods for specifying the target difference are available and when they can be used.

What Did the Researchers Do and Find?

To identify studies that used a method for determining an important and/or realistic difference, the investigators systematically surveyed the research literature. Two reviewers screened each of the abstracts chosen, and a third reviewer was consulted if necessary. The authors identified seven methods to determine target differences. They evaluated the studies to establish similarities and differences of each application. Points about the strengths and limitations of the method and how frequently the method was chosen were also noted.

What Do these Findings Mean?

The study draws attention to an understudied but important part of designing a clinical trial. Enrolling the right number of patients is very important—too few patients and the study may not be able to answer the study question; too many and the study will be more expensive and more difficult to conduct, and will unnecessarily expose more patients to any study risks. The target difference may also be helpful in interpreting the results of the trial. The authors discuss the pros and cons of different ways to calculate target differences and which methods are best for which types of studies, to help inform researchers designing such studies.

Additional Information

Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1?001645.

  • Wikipedia has an entry on sample size determination that discusses the factors that influence sample size calculation, including the target difference and the statistical power of a study (statistical power is the ability of a study to find a difference between treatments when a true difference exists). (Note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages.)
  • The University of Ottawa has an article that explains how different factors influence the power of a study
  • Citation: Hislop J, Adewuyi TE, Vale LD, Harrild K, Fraser C, et al. (2014) Methods for Specifying the Target Difference in a Randomised Controlled Trial: The Difference ELicitation in TriAls (DELTA) Systematic Review. PLoS Med 11(5): e1001645. doi:10.1371/journal.pmed.1001645
  • Academic Editor: Michael Dewey, Institute of Psychiatry, King′s College London, United Kingdom
  • Received: September 10, 2013; Accepted: April 4, 2014; Published: May 13, 2014
  • Copyright: © 2014 Hislop et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
  • Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. Reviewing documentation is available from the authors.
  • Funding: This study was part of a project commissioned and funded by the UK Medical Research Council & National Institute for Health Research Joint Methodology Research Programme (G0902147 & 06/98/01). JAC holds a Medical Research Council Methodology Fellowship (G1002292). The Health Services Research Unit is funded by the Scottish Government Health and Social Care Directorates. The funders had no involvement in study design, collection, analysis and interpretation of data, reporting or the decision to publish. The full project findings will be published in the Health Technology Assessment Journal. Views express are those of the authors and do not necessarily reflect the views of the funders nor of the UK Government's Department of Health.
  • Competing interests: The authors have declared that no competing interests exist.
  • Abbreviations: ICH, International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use; RCT, randomised controlled trial; SD, standard deviation; SEM, standard error of measurement; SES, standardised effect size
  • Membership of the DELTA group is provided in the Acknowledgments.
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