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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Timothy Clark, Jul 24, 2015 Available from: Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

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**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 - [Show abstract] [Hide abstract]

**ABSTRACT:**Evidence suggests that research protocols often lack important information on study design, which hinders external review. The study protocol should provide an adequate explanation for why the proposed study methodology is appropriate for the question posed, why the study design is likely to answer the research question, and why it is the best approach. It is especially important that researchers explain why the treatment difference sought is worthwhile to patients, and they should reference consultations with the public and patient groups and existing literature. Moreover, the study design should be underpinned by a systematic review of the existing evidence, which should be included in the research protocol. The Health Research Authority in collaboration with partners has published guidance entitled 'Specific questions that need answering when considering the design of clinical trials'. The guidance will help those designing research and those reviewing it to address key issues.Trials 07/2014; 15(1):286. DOI:10.1186/1745-6215-15-286 · 1.73 Impact Factor