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Meta-analysis of data on costs from trials of counselling in primary care: using individual patient data to overcome sample size limitations in economic analyses.

ABSTRACT: OBJECTIVE: To assess the feasibility of overcoming sample size limitations in economic analyses of clinical trials through meta-analysis of data on individual patients from multiple trials. DESIGN: Meta-analysis of individual patient data from trials of counselling in primary care compared with usual care by a general practitioner. SETTING: Primary care. PATIENTS: People with mental health problems. MAIN OUTCOME MEASURES: Direct treatment costs, depressive symptoms, and cost effectiveness. RESULTS: Meta-analysis of individual patient data proved feasible. The results showed that the previous analyses of individual trials were underpowered to provide useful conclusions about the cost comparisons. The results are sensitive to assumptions made about the costs of sessions with a counsellor and the management of patients by a general practitioner. CONCLUSIONS: Meta-analysis of individual patient data may assist in overcoming sample size limitations in economic analyses. Although feasible, such analysis has shortcomings that may limit the validity of the results. The relative costs and benefits of this method, as opposed to further collection of primary data, are as yet unclear.

SUBMITTER: Bower P

PROVIDER: S-EPMC161557 | biostudies-literature | 2003 Jun

REPOSITORIES: biostudies-literature

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Meta-analysis of data on costs from trials of counselling in primary care: using individual patient data to overcome sample size limitations in economic analyses.

Bower Peter P Byford Sarah S Barber Julie J Beecham Jennifer J Simpson Sharon S Friedli Karin K Corney Roslyn R King Michael M Harvey Ian I

BMJ (Clinical research ed.) 20030601 7401

<h4>Objective</h4>To assess the feasibility of overcoming sample size limitations in economic analyses of clinical trials through meta-analysis of data on individual patients from multiple trials.<h4>Design</h4>Meta-analysis of individual patient data from trials of counselling in primary care compared with usual care by a general practitioner.<h4>Setting</h4>Primary care.<h4>Patients</h4>People with mental health problems.<h4>Main outcome measures</h4>Direct treatment costs, depressive symptoms ...[more]

PMID: 12791741

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Project description:BackgroundMulti-centre randomized controlled clinical trials play an important role in modern evidence-based medicine. Advantages of collecting data from more than one site are numerous, including accelerated recruitment and increased generalisability of results. Mixed models can be applied to account for potential clustering in the data, in particular when many small centres contribute patients to the study. Previously proposed methods on sample size calculation for mixed models only considered balanced treatment allocations which is an unlikely outcome in practice if block randomisation with reasonable choices of block length is used.MethodsWe propose a sample size determination procedure for multi-centre trials comparing two treatment groups for a continuous outcome, modelling centre differences using random effects and allowing for arbitrary sample sizes. It is assumed that block randomisation with fixed block length is used at each study site for subject allocation. Simulations are used to assess operation characteristics such as power of the sample size approach. The proposed method is illustrated by an example in disease management systems.ResultsA sample size formula as well as a lower and upper boundary for the required overall sample size are given. We demonstrate the superiority of the new sample size formula over the conventional approach of ignoring the multi-centre structure and show the influence of parameters such as block length or centre heterogeneity. The application of the procedure on the example data shows that large blocks require larger sample sizes, if centre heterogeneity is present.ConclusionUnbalanced treatment allocation can result in substantial power loss when centre heterogeneity is present but not considered at the planning stage. When only few patients by centre will be recruited, one has to weigh the risk of imbalance between treatment groups due to large blocks and the risk of unblinding due to small blocks. The proposed approach should be considered when planning multi-centre trials.

Project description:BackgroundThe costs of medical research are a concern. Clinical Trials Units (CTUs) need to better understand variations in the costs of their activities.MethodsRepresentatives of ten CTUs and two grant-awarding bodies pooled their experiences in discussions over 1.5 years. Five of the CTUs provided estimates of, and written justification for, costs associated with CTU activities required to implement an identical protocol. The protocol described a 5.5-year, nonpharmacological randomized controlled trial (RCT) conducted at 20 centres. Direct and indirect costs, the number of full time equivalents (FTEs) and the FTEs attracting overheads were compared and qualitative methods (unstructured interviews and thematic analysis) were used to interpret the results. Four members of the group (funding-body representatives or award panel members) reviewed the justification statements for transparency and information content. Separately, 163 activities common to trials were assigned to roles used by nine CTUs; the consistency of role delineation was assessed by Cohen's κ.ResultsMedian full economic cost of CTU activities was £769,637 (range: £661,112 to £1,383,323). Indirect costs varied considerably, accounting for between 15% and 59% (median 35%) of the full economic cost of the grant. Excluding one CTU, which used external statisticians, the total number of FTEs ranged from 2.0 to 3.0; total FTEs attracting overheads ranged from 0.3 to 2.0. Variation in directly incurred staff costs depended on whether CTUs: supported particular roles from core funding rather than grants; opted not to cost certain activities into the grant; assigned clerical or data management tasks to research or administrative staff; employed extensive on-site monitoring strategies (also the main source of variation in non-staff costs). Funders preferred written justifications of costs that described both FTEs and indicative tasks for funded roles, with itemised non-staff costs. Consistency in role delineation was fair (κ = 0.21-0.40) for statisticians/data managers and poor for other roles (κ < 0.20).ConclusionsSome variation in costs is due to factors outside the control of CTUs such as access to core funding and levels of indirect costs levied by host institutions. Research is needed on strategies to control costs appropriately, especially the implementation of risk-based monitoring strategies.

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Meta-analysis of data on costs from trials of counselling in primary care: using individual patient data to overcome sample size limitations in economic analyses.

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Meta-analysis of data on costs from trials of counselling in primary care: using individual patient data to overcome sample size limitations in economic analyses.

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