Abstract

BackgroundData collection for economic evaluation alongside clinical trials is burdensome and cost-intensive. Limiting both the frequency of data collection and recall periods can solve the problem. As a consequence, gaps in survey periods arise and must be filled appropriately. The aims of our study are to assess the validity of incomplete cost data collection and define suitable resource categories.MethodsIn the randomised KORINNA study, cost data from 234 elderly patients were collected quarterly over a 1-year period. Different strategies for incomplete data collection were compared with complete data collection. The sample size calculation was modified in response to elasticity of variance.ResultsResource categories suitable for incomplete data collection were physiotherapy, ambulatory clinic in hospital, medication, consultations, outpatient nursing service and paid household help.Cost estimation from complete and incomplete data collection showed no difference when omitting information from one quarter. When omitting information from two quarters, costs were underestimated by 3.9% to 4.6%.With respect to the observed increased standard deviation, a larger sample size would be required, increased by 3%. Nevertheless, more time was saved than extra time would be required for additional patients.ConclusionCost data can be collected efficiently by reducing the frequency of data collection. This can be achieved by incomplete data collection for shortened periods or complete data collection by extending recall windows. In our analysis, cost estimates per year for ambulatory healthcare and non-healthcare services in terms of three data collections was as valid and accurate as a four complete data collections. In contrast, data on hospitalisation, rehabilitation stays and care insurance benefits should be collected for the entire target period, using extended recall windows. When applying the method of incomplete data collection, sample size calculation has to be modified because of the increased standard deviation. This approach is suitable to enable economic evaluation with lower costs to both study participants and investigators.Trial registrationThe trial registration number is ISRCTN02893746

Highlights

  • Data collection for economic evaluation alongside clinical trials is burdensome and cost-intensive

  • Economic evaluation alongside clinical trials is gaining importance because of demographic trends towards an ageing population, which is a relevant driver for resource use in healthcare [1,2,3]

  • As the variance and standard deviation influence sample size calculation, we modelled the impact of a 1% increase in standard deviation on sample size, called elasticity (E)

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Summary

Introduction

Data collection for economic evaluation alongside clinical trials is burdensome and cost-intensive. Limiting both the frequency of data collection and recall periods can solve the problem. Economic evaluation alongside clinical trials is gaining importance because of demographic trends towards an ageing population, which is a relevant driver for resource use in healthcare [1,2,3]. Patients’ self-reported resource use can be recorded prospectively with cost diaries or retrospectively by means of questionnaires. For economic evaluation from a societal perspective, only patients’ self-reported resource use offers an account of out-of-pocket payments (e.g. paid household help or over-the-counter medications). Prospective or retrospective methods based on patient recall are widely used [8,9,10]

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