How much Detail is needed in Cost Estimation in an Economic Evaluation alongside a Clinical Trial to Optimise Evidence for Decisions?
type: CreativeWork
description: Acquiring evidence to support decision making is expensive. Collecting resource use data alongside a randomised controlled clinical trial is particularly so due to the multidimensional nature of costs: different costs are incurred by different agencies with varying methods and systems to account for these. Trialists are faced with decisions over how to collect such data, in particular different ‘levels’ of detail are possible. For example, hospitalisations can be costed (1) on a top-down, per admission basis multiplied by a representative unit cost, (2) a bottom-up basis measuring every component of care such as nursing and medic time, investigations and other procedures and drugs used which are each multiplied by relevant unit costs, or (3) some intermediate level of aggregation. The top-down data will be less expensive to obtain but may be less accurate (biased and/or over- or under-estimation of uncertainty) compared with the bottom-up. I refer to these alternative methods as ‘data collection processes’. Currently such decisions are based on the judgement of the trialist(s). However, formal quantification of the added value of one data collection process versus another compared with the added cost would inform the efficient allocation of research resources. In this thesis I extend the use of value of information analysis to compare the incremental cost and benefit of one data process with another, further extending this to estimate the optimal mix of observations between two processes. Using an example dataset I find that the method is workable, requiring prior information on the relationship between the two processes which can be obtained from either a pilot or feasibility study or expert opinion. When incorporated with other concurrent developments in value of information analysis, the method has the potential to provide a decision analytic approach to the complete design of clinical trials.
abstract: Acquiring evidence to support decision making is expensive. Collecting resource use data alongside a randomised controlled clinical trial is particularly so due to the multidimensional nature of costs: different costs are incurred by different agencies with varying methods and systems to account for these. Trialists are faced with decisions over how to collect such data, in particular different ‘levels’ of detail are possible. For example, hospitalisations can be costed (1) on a top-down, per admission basis multiplied by a representative unit cost, (2) a bottom-up basis measuring every component of care such as nursing and medic time, investigations and other procedures and drugs used which are each multiplied by relevant unit costs, or (3) some intermediate level of aggregation. The top-down data will be less expensive to obtain but may be less accurate (biased and/or over- or under-estimation of uncertainty) compared with the bottom-up. I refer to these alternative methods as ‘data collection processes’. Currently such decisions are based on the judgement of the trialist(s). However, formal quantification of the added value of one data collection process versus another compared with the added cost would inform the efficient allocation of research resources. In this thesis I extend the use of value of information analysis to compare the incremental cost and benefit of one data process with another, further extending this to estimate the optimal mix of observations between two processes. Using an example dataset I find that the method is workable, requiring prior information on the relationship between the two processes which can be obtained from either a pilot or feasibility study or expert opinion. When incorporated with other concurrent developments in value of information analysis, the method has the potential to provide a decision analytic approach to the complete design of clinical trials.
author: Wilson, Edward
language: en
date published: 2014-04
date modified: 2026-07-24T02:12:03Z
provider: University of East Anglia
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of East Anglia