Efficient and flexible simulation-based sample size determination for clinical trials with multiple design parameters.

Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of sample size determination problems, often minimising a single parameter (the overall sample size) subject to power being above a target level. We describe a general framework for solving simulation-based sample size determination problems with several design parameters over which to optimise and several conflicting criteria to be minimised. The method is based on an established global optimisation algorithm widely used in the design and analysis of computer experiments, using a non-parametric regression model as an approximation of the true underlying power function. The method is flexible, can be used for almost any problem for which power can be estimated using simulation, and can be implemented using existing statistical software packages. We illustrate its application to a sample size determination problem involving complex clustering structures, two primary endpoints and small sample considerations.

Dementia Care Mapping™ to reduce agitation in care home residents with dementia: the EPIC cluster RCT.

BACKGROUND: The quality of care for people with dementia in care homes is of concern. Interventions that can improve care outcomes are required. OBJECTIVE: To investigate the clinical effectiveness and cost-effectiveness of Dementia Care Mapping™ (DCM) for reducing agitation and improving care outcomes for people living with dementia in care homes, versus usual care. DESIGN: A pragmatic, cluster randomised controlled trial with an open-cohort design, follow-up at 6 and 16 months, integrated cost-effectiveness analysis and process evaluation. Clusters were not blinded to allocation. The primary end point was completed by staff proxy and independent assessors. SETTING: Stratified randomisation of 50 care homes to the intervention and control groups on a 3 : 2 ratio by type, size, staff exposure to dementia training and recruiting hub. PARTICIPANTS: Fifty care homes were randomised (intervention, n = 31; control, n = 19), with 726 residents recruited at baseline and a further 261 recruited after 16 months. Care homes were eligible if they recruited a minimum of 10 residents, were not subject to improvement notices, had not used DCM in the previous 18 months and were not participating in conflicting research. Residents were eligible if they lived there permanently, had a formal diagnosis of dementia or a score of 4+ on the Functional Assessment Staging Test of Alzheimer's Disease, were proficient in English and were not terminally ill or permanently cared for in bed. All homes were audited on the delivery of dementia and person-centred care awareness training. Those not reaching a minimum standard were provided training ahead of randomisation. Eighteen homes took part in the process evaluation. INTERVENTION: Two staff members from each intervention home were trained to use DCM and were asked to carry out three DCM cycles; the first was supported by an external expert. MAIN OUTCOME MEASURES: The primary outcome was agitation (Cohen-Mansfield Agitation Inventory), measured at 16 months. Secondary outcomes included resident behaviours and quality of life. RESULTS: There were 675 residents in the final analysis (intervention, n = 388; control, n = 287). There was no evidence of a difference in agitation levels between the treatment arms. The adjusted mean difference in Cohen-Mansfield Agitation Inventory score was -2.11 points, being lower in the intervention group than in the control (95% confidence interval -4.66 to 0.44; p = 0.104; adjusted intracluster correlation coefficient: control = 0, intervention = 0.001). The sensitivity analyses results supported the primary analysis. No differences were detected in any of the secondary outcomes. The health economic analyses indicated that DCM was not cost-effective. Intervention adherence was problematic; only 26% of homes completed more than their first DCM cycle. Impacts, barriers to and facilitators of DCM implementation were identified. LIMITATIONS: The primary completion of resident outcomes was by staff proxy, owing to self-report difficulties for residents with advanced dementia. Clusters were not blinded to allocation, although supportive analyses suggested that any reporting bias was not clinically important. CONCLUSIONS: There was no benefit of DCM over control for any outcomes. The implementation of DCM by care home staff was suboptimal compared with the protocol in the majority of homes. FUTURE WORK: Alternative models of DCM implementation should be considered that do not rely solely on leadership by care home staff. TRIAL REGISTRATION: Current Controlled Trials ISRCTN82288852. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 16. See the NIHR Journals Library website for further project information.

Agitation is common in care home residents and may result from care that does not meet individual needs. Dementia Care Mapping™ (DCM) is a tool used within care homes to improve the delivery of person-centred care, which may help reduce agitation. This randomised controlled trial aimed to understand whether or not DCM is better than usual care at reducing resident agitation, behaviours that staff may find difficult to support and the use of antipsychotic medicines, as well as at improving residents' quality of life and staff communication. It also assessed its value for money. We recruited 726 residents with dementia from 50 care homes. After initial data collection, care homes were randomly assigned to DCM (31/50) or told to continue with usual care (19/50) and data were collected again after 6 and 16 months. A further 261 residents were recruited after 16 months. We also interviewed staff, relatives and residents about the use of DCM after the final data collection had taken place. Two staff members in each DCM home were trained to use DCM and were helped by an expert to use it for the first time. They were asked to use it again a further two times without support. Results showed that DCM was no better than usual care in relation to any of the outcomes. It was also not shown to be value for money. Only one-quarter of care homes used DCM more than once. The care staff who were interviewed said that the benefits of using DCM included reduced resident boredom and increased staff confidence. There were also many challenges, including the time needed to complete DCM, a lack of managerial support and problems with staffing levels. Putting DCM into practice in care homes was difficult, even with expert support, and most care homes did not complete three DCM cycles. Future research should explore models of implementing DCM that do not rely on care home staff to lead them.

Statistical challenges in assessing potential efficacy of complex interventions in pilot or feasibility studies.

Early phase trials of complex interventions currently focus on assessing the feasibility of a large randomised control trial and on conducting pilot work. Assessing the efficacy of the proposed intervention is generally discouraged, due to concerns of underpowered hypothesis testing. In contrast, early assessment of efficacy is common for drug therapies, where phase II trials are often used as a screening mechanism to identify promising treatments. In this paper, we outline the challenges encountered in extending ideas developed in the phase II drug trial literature to the complex intervention setting. The prevalence of multiple endpoints and clustering of outcome data are identified as important considerations, having implications for timely and robust determination of optimal trial design parameters. The potential for Bayesian methods to help to identify robust trial designs and optimal decision rules is also explored.