Article

# Sample size determinations in original research protocols for randomised clinical trials submitted to UK research ethics committees: Review

Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Faculty of Medicine, Ludwig-Maximilians University, Munich, Germany.

BMJ (online) (Impact Factor: 17.45). 03/2013; 346(mar21 1):f1135. DOI: 10.1136/bmj.f1135 Source: PubMed

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**ABSTRACT:**The increase in annual global investment in biomedical research--reaching US$240 billion in 2010--has resulted in important health dividends for patients and the public. However, much research does not lead to worthwhile achievements, partly because some studies are done to improve understanding of basic mechanisms that might not have relevance for human health. Additionally, good research ideas often do not yield the anticipated results. As long as the way in which these ideas are prioritised for research is transparent and warranted, these disappointments should not be deemed wasteful; they are simply an inevitable feature of the way science works. However, some sources of waste cannot be justified. In this report, we discuss how avoidable waste can be considered when research priorities are set. We have four recommendations. First, ways to improve the yield from basic research should be investigated. Second, the transparency of processes by which funders prioritise important uncertainties should be increased, making clear how they take account of the needs of potential users of research. Third, investment in additional research should always be preceded by systematic assessment of existing evidence. Fourth, sources of information about research that is in progress should be strengthened and developed and used by researchers. Research funders have primary responsibility for reductions in waste resulting from decisions about what research to do.The Lancet 01/2014; 383(9912):156-65. DOI:10.1016/S0140-6736(13)62229-1 · 45.22 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Several methods exist to calculate sample size for the difference of proportions (risk difference). Researchers are often unaware that there are different formulae, different underlying assumptions, and what the impact of choice of formula is on the calculated sample size. The aim of this study was to discuss and compare different sample size formulae for the risk difference. Four sample size formulae were used to calculate sample size for nine scenarios. Software documentation for SAS, Stata, G*Power, PASS, StatXact, and several R libraries were searched for default assumptions. Each package was used to calculate sample size for two scenarios. We demonstrate that for a set of parameters, sample size can vary as much as 60% depending on the formula used. Varying software and assumptions yielded discrepancies of 78% and 7% between the smallest and largest calculated sizes, respectively. Discrepancies were most pronounced when powering for large risk differences. The default assumptions varied considerably between software packages, and defaults were not clearly documented. Researchers should be aware of the assumptions in power calculations made by different statistical software packages. Assumptions should be explicitly stated in grant proposals and manuscripts and should match proposed analyses.Journal of clinical epidemiology 01/2014; 67(5). DOI:10.1016/j.jclinepi.2013.10.008 · 3.42 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Background External pilot or feasibility studies can be used to estimate key unknown parameters to inform the design of the definitive randomised controlled trial (RCT). However, there is little consensus on how large pilot studies need to be, and some suggest inflating estimates to adjust for the lack of precision when planning the definitive RCT. Methods We use a simulation approach to illustrate the sampling distribution of the standard deviation for continuous outcomes and the event rate for binary outcomes. We present the impact of increasing the pilot sample size on the precision and bias of these estimates, and predicted power under three realistic scenarios. We also illustrate the consequences of using a confidence interval argument to inflate estimates so the required power is achieved with a pre-specified level of confidence. We limit our attention to external pilot and feasibility studies prior to a two-parallel-balanced-group superiority RCT. Results For normally distributed outcomes, the relative gain in precision of the pooled standard deviation (SDp) is less than 10% (for each five subjects added per group) once the total sample size is 70. For true proportions between 0.1 and 0.5, we find the gain in precision for each five subjects added to the pilot sample is less than 5% once the sample size is 60. Adjusting the required sample sizes for the imprecision in the pilot study estimates can result in excessively large definitive RCTs and also requires a pilot sample size of 60 to 90 for the true effect sizes considered here. Conclusions We recommend that an external pilot study has at least 70 measured subjects (35 per group) when estimating the SDp for a continuous outcome. If the event rate in an intervention group needs to be estimated by the pilot then a total of 60 to 100 subjects is required. Hence if the primary outcome is binary a total of at least 120 subjects (60 in each group) may be required in the pilot trial. It is very much more efficient to use a larger pilot study, than to guard against the lack of precision by using inflated estimates.Trials 07/2014; 15(1):264. DOI:10.1186/1745-6215-15-264 · 1.73 Impact Factor