Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis
Baseline risk is a proxy for unmeasured but important patient‐level characteristics, which may be modifiers of treatment effect, and is a potential source of heterogeneity in meta‐analysis. Models adjusting for baseline risk have been developed for pairwise meta‐analysis using the observed event rat...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Wiley
2012
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| _version_ | 1797087506814992384 |
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| author | Felix A Achana Nicola J Cooper Sofia Dias Guobing Lu Stephen JC Rice Denise Kendrick Alex J Sutton |
| author_facet | Felix A Achana Nicola J Cooper Sofia Dias Guobing Lu Stephen JC Rice Denise Kendrick Alex J Sutton |
| author_sort | Felix A Achana |
| collection | OXFORD |
| description | Baseline risk is a proxy for unmeasured but important patient‐level characteristics, which may be modifiers of treatment effect, and is a potential source of heterogeneity in meta‐analysis. Models adjusting for baseline risk have been developed for pairwise meta‐analysis using the observed event rate in the placebo arm and taking into account the measurement error in the covariate to ensure that an unbiased estimate of the relationship is obtained. Our objective is to extend these methods to network meta‐analysis where it is of interest to adjust for baseline imbalances in the non‐intervention group event rate to reduce both heterogeneity and possibly inconsistency. This objective is complicated in network meta‐analysis by this covariate being sometimes missing, because of the fact that not all studies in a network may have a non‐active intervention arm. A random‐effects meta‐regression model allowing for inclusion of multi‐arm trials and trials without a ‘non‐intervention’ arm is developed. Analyses are conducted within a Bayesian framework using the WinBUGS software. The method is illustrated using two examples: (i) interventions to promote functional smoke alarm ownership by households with children and (ii) analgesics to reduce post‐operative morphine consumption following a major surgery. The results showed no evidence of baseline effect in the smoke alarm example, but the analgesics example shows that the adjustment can greatly reduce heterogeneity and improve overall model fit. Copyright © 2012 John Wiley & Sons, Ltd. |
| first_indexed | 2024-03-07T02:36:38Z |
| format | Journal article |
| id | oxford-uuid:a90168a3-f0fc-41f4-9d82-3a80c7a48868 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:36:38Z |
| publishDate | 2012 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:a90168a3-f0fc-41f4-9d82-3a80c7a488682022-03-27T03:05:34ZExtending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysisJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a90168a3-f0fc-41f4-9d82-3a80c7a48868EnglishSymplectic ElementsWiley2012Felix A AchanaNicola J CooperSofia DiasGuobing LuStephen JC RiceDenise KendrickAlex J SuttonBaseline risk is a proxy for unmeasured but important patient‐level characteristics, which may be modifiers of treatment effect, and is a potential source of heterogeneity in meta‐analysis. Models adjusting for baseline risk have been developed for pairwise meta‐analysis using the observed event rate in the placebo arm and taking into account the measurement error in the covariate to ensure that an unbiased estimate of the relationship is obtained. Our objective is to extend these methods to network meta‐analysis where it is of interest to adjust for baseline imbalances in the non‐intervention group event rate to reduce both heterogeneity and possibly inconsistency. This objective is complicated in network meta‐analysis by this covariate being sometimes missing, because of the fact that not all studies in a network may have a non‐active intervention arm. A random‐effects meta‐regression model allowing for inclusion of multi‐arm trials and trials without a ‘non‐intervention’ arm is developed. Analyses are conducted within a Bayesian framework using the WinBUGS software. The method is illustrated using two examples: (i) interventions to promote functional smoke alarm ownership by households with children and (ii) analgesics to reduce post‐operative morphine consumption following a major surgery. The results showed no evidence of baseline effect in the smoke alarm example, but the analgesics example shows that the adjustment can greatly reduce heterogeneity and improve overall model fit. Copyright © 2012 John Wiley & Sons, Ltd. |
| spellingShingle | Felix A Achana Nicola J Cooper Sofia Dias Guobing Lu Stephen JC Rice Denise Kendrick Alex J Sutton Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis |
| title | Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis |
| title_full | Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis |
| title_fullStr | Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis |
| title_full_unstemmed | Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis |
| title_short | Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis |
| title_sort | extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta analysis to network meta analysis |
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