7,658 research outputs found
Are we getting what we pay for?
No full textThe British NHS delivers health care free at the point of access to whomever needs it. It is often claimed to be the envy of the world. But does it deliver health? Or could the resource put into the health service be better spent elsewhere? In this article, we discuss the determinants of health in the United Kingdom in the past, the rise of public health and the impact medical technology has had on health. We discuss resource distribution in health care, and apply the principles of health economics to the wider context of the delivery of health, rather than health care. With a background of rising demand for health care and rationing of resources in the UK, combined with inequalities in life expectancy related to position in society, we conclude that wealth redistribution, environmental regulation, improved nutrition and better education must come first in the priorities for achieving a healthy population
sj-docx-1-smm-10.1177_09622802211037073 - Supplemental material for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate
No full textSupplemental material, sj-docx-1-smm-10.1177_09622802211037073 for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate by Jen Lewis and Steven A Julious in Statistical Methods in Medical Research</p
sj-docx-2-smm-10.1177_09622802211037073 - Supplemental material for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate
No full textSupplemental material, sj-docx-2-smm-10.1177_09622802211037073 for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate by Jen Lewis and Steven A Julious in Statistical Methods in Medical Research</p
sj-docx-3-smm-10.1177_09622802211037073 - Supplemental material for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate
No full textSupplemental material, sj-docx-3-smm-10.1177_09622802211037073 for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate by Jen Lewis and Steven A Julious in Statistical Methods in Medical Research</p
sj-R-4-smm-10.1177_09622802211037073 - Supplemental material for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate
No full textSupplemental material, sj-R-4-smm-10.1177_09622802211037073 for Sample sizes for cluster-randomised trials with continuous outcomes: Accounting for uncertainty in a single intra-cluster correlation estimate by Jen Lewis and Steven A Julious in Statistical Methods in Medical Research</p
An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database
No full textThere is little published guidance as to the sample size required for a pilot or feasibility trial despite the fact that a sample size justification is a key element in the design of a trial. A sample size justification should give the minimum number of participants needed in order to meet the objectives of the trial. This paper seeks to describe the target sample sizes set for pilot and feasibility randomised controlled trials, currently running within the United Kingdom. Data were gathered from the United Kingdom Clinical Research Network (UKCRN) database using the search terms 'pilot' and 'feasibility.' From this search 513 studies were assessed for eligibility of which 79 met the inclusion criteria. Where the data summary on the UKCRN Database was incomplete, data were also gathered from: the International Standardised Randomised Controlled Trial Number (ISRCTN) register; the clinicaltrials.gov website and the website of the funders. For 62 of the trials, it was necessary to contact members of the research team by email to ensure completeness. Of the 79 trials analysed, 50 (63.3%) were labelled as pilot trials, 25 (31.6%) feasibility and 14 were described as both pilot and feasibility trials. The majority had two arms (n = 68, 86.1%) and the two most common endpoints were continuous (n = 45, 57.0%) and dichotomous (n = 31, 39.2%). Pilot trials were found to have a smaller sample size per arm (median = 30, range = 8 to 114 participants) than feasibility trials (median = 36, range = 10 to 300 participants). By type of endpoint, across feasibility and pilot trials, the median sample size per arm was 36 (range = 10 to 300 participants) for trials with a dichotomous endpoint and 30 (range = 8 to 114 participants) for trials with a continuous endpoint. Publicly funded pilot trials appear to be larger than industry funded pilot trials: median sample sizes of 33 (range = 15 to 114 participants) and 25 (range = 8 to 100 participants) respectively. All studies should have a sample size justification. Not all studies however need to have a sample size calculation. For pilot and feasibility trials, while a sample size justification is important, a formal sample size calculation may not be appropriate. The results in this paper describe the observed sample sizes in feasibility and pilot randomised controlled trials on the UKCRN Database
Issues with using baseline in last observation carried forward analysis
No full textThe topic of this paper was prompted by a study for which one of us was the statistician. It was submitted to Annals of Internal Medicine. The paper had positive reviewer comment; however, the statistical reviewer stated that for the analysis to be acceptable for publication, the missing data had to be accounted for in the analysis through the use of baseline in a last observation carried forward imputation. We discuss the issues associated with this form of imputation and recommend that it should not be undertaken as a primary analysis
sj-docx-1-mdm-10.1177_0272989X211045036 – Supplemental material for Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials
No full textSupplemental material, sj-docx-1-mdm-10.1177_0272989X211045036 for Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials by Laura Flight, Steven Julious, Alan Brennan and Susan Todd in Medical Decision Making</p
Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes
No full textMelanie L Bell,1 Amy L Whitehead,2 Steven A Julious2 1Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA; 2Medical Statistics Group, Design, Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK Background: A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial’s sample size calculations should be undertaken.Methods: We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized.Results: The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates.Conclusion: Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial. Keywords: pilot, feasibility, sample size, power, randomized controlled trial, sensitivity analysi
- …
