| Title |
A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials
|
|---|---|
| Published in |
PharmacoEconomics, July 2014
|
| DOI | 10.1007/s40273-014-0193-3 |
| Pubmed ID | |
| Authors |
Rita Faria, Manuel Gomes, David Epstein, Ian R. White |
| Abstract |
Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a randomised controlled trial. Inappropriate methods to handle missing data can lead to misleading results and ultimately can affect the decision of whether an intervention is good value for money. This article provides practical guidance on how to handle missing data in within-trial CEAs following a principled approach: (i) the analysis should be based on a plausible assumption for the missing data mechanism, i.e. whether the probability that data are missing is independent of or dependent on the observed and/or unobserved values; (ii) the method chosen for the base-case should fit with the assumed mechanism; and (iii) sensitivity analysis should be conducted to explore to what extent the results change with the assumption made. This approach is implemented in three stages, which are described in detail: (1) descriptive analysis to inform the assumption on the missing data mechanism; (2) how to choose between alternative methods given their underlying assumptions; and (3) methods for sensitivity analysis. The case study illustrates how to apply this approach in practice, including software code. The article concludes with recommendations for practice and suggestions for future research. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 5 | 50% |
| New Zealand | 2 | 20% |
| Australia | 1 | 10% |
| Unknown | 2 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 2 | 20% |
| Science communicators (journalists, bloggers, editors) | 2 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 298 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | <1% |
| United States | 1 | <1% |
| Denmark | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 293 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 65 | 22% |
| Student > Master | 53 | 18% |
| Student > Ph. D. Student | 39 | 13% |
| Other | 13 | 4% |
| Student > Doctoral Student | 13 | 4% |
| Other | 50 | 17% |
| Unknown | 65 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 68 | 23% |
| Economics, Econometrics and Finance | 53 | 18% |
| Psychology | 21 | 7% |
| Nursing and Health Professions | 11 | 4% |
| Social Sciences | 11 | 4% |
| Other | 52 | 17% |
| Unknown | 82 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 February 2023.
All research outputs
#1,768,255
of 23,901,621 outputs
Outputs from PharmacoEconomics
#112
of 1,925 outputs
Outputs of similar age
#18,246
of 232,032 outputs
Outputs of similar age from PharmacoEconomics
#2
of 32 outputs
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So far Altmetric has tracked 1,925 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done particularly well, scoring higher than 94% of its peers.
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