Linear mixed models to handle missing at random data in trial‐based economic evaluations
Trial‐based cost‐effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the partici...
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| Published in | Health economics Vol. 31; no. 6; pp. 1276 - 1287 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Wiley Periodicals Inc
01.06.2022
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1057-9230 1099-1050 1099-1050 |
| DOI | 10.1002/hec.4510 |
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| Abstract | Trial‐based cost‐effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods. |
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| AbstractList | Trial-based cost-effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods.Trial-based cost-effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods. Trial‐based cost‐effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods. |
| Author | Leurent, Baptiste Gabrio, Andrea Banerjee, Sube Plumpton, Catrin |
| AuthorAffiliation | 1 Department of Methodology and Statistics Faculty of Health Medicine and Life Science Maastricht University Maastricht The Netherlands 2 Centre for Health Economics and Medicines Evaluation Bangor University Bangor UK 3 Faculty of Health University of Plymouth Plymouth UK 4 Department of Medical Statistics London School of Hygiene and Tropical Medicine London UK 5 MRC International Statistics and Epidemiology Group Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London UK |
| AuthorAffiliation_xml | – name: 1 Department of Methodology and Statistics Faculty of Health Medicine and Life Science Maastricht University Maastricht The Netherlands – name: 2 Centre for Health Economics and Medicines Evaluation Bangor University Bangor UK – name: 4 Department of Medical Statistics London School of Hygiene and Tropical Medicine London UK – name: 3 Faculty of Health University of Plymouth Plymouth UK – name: 5 MRC International Statistics and Epidemiology Group Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London UK |
| Author_xml | – sequence: 1 givenname: Andrea orcidid: 0000-0002-7650-4534 surname: Gabrio fullname: Gabrio, Andrea organization: Maastricht University – sequence: 2 givenname: Catrin orcidid: 0000-0003-2710-9199 surname: Plumpton fullname: Plumpton, Catrin organization: Bangor University – sequence: 3 givenname: Sube orcidid: 0000-0002-8083-7649 surname: Banerjee fullname: Banerjee, Sube organization: University of Plymouth – sequence: 4 givenname: Baptiste orcidid: 0000-0001-6420-6567 surname: Leurent fullname: Leurent, Baptiste email: baptiste.leurent@ucl.ac.uk organization: London School of Hygiene and Tropical Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35368119$$D View this record in MEDLINE/PubMed |
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| Keywords | repeated measures model mixed-effects cost-effectiveness analysis randomized trial missing data |
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| SubjectTerms | Antidepressants Cost analysis Cost-Benefit Analysis cost‐effectiveness analysis Data Interpretation, Statistical Databases, Factual Health economics Humans Implementation Linear Models Missing data mixed‐effects Models, Statistical Multiple imputation randomized trial repeated measures model Short SHORT RESEARCH ARTICLES Within-subjects design |
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| Title | Linear mixed models to handle missing at random data in trial‐based economic evaluations |
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