Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial
<p>Abstract</p> <p>Background</p> <p>Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on...
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| Format: | Article |
| Language: | English |
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BMC
2010-06-01
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| Series: | BMC Medical Research Methodology |
| Online Access: | http://www.biomedcentral.com/1471-2288/10/54 |
| _version_ | 1828492901710036992 |
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| author | Hawkins Neil Woods Beth S Scott David A |
| author_facet | Hawkins Neil Woods Beth S Scott David A |
| author_sort | Hawkins Neil |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias.</p> <p>Methods</p> <p>In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards).</p> <p>Results</p> <p>A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided.</p> <p>Conclusions</p> <p>By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics.</p> |
| first_indexed | 2024-12-11T11:29:58Z |
| format | Article |
| id | doaj.art-b19329561ca348ea8ec3d01d55b574f7 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-11T11:29:58Z |
| publishDate | 2010-06-01 |
| publisher | BMC |
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| series | BMC Medical Research Methodology |
| spelling | doaj.art-b19329561ca348ea8ec3d01d55b574f72022-12-22T01:08:55ZengBMCBMC Medical Research Methodology1471-22882010-06-011015410.1186/1471-2288-10-54Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorialHawkins NeilWoods Beth SScott David A<p>Abstract</p> <p>Background</p> <p>Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias.</p> <p>Methods</p> <p>In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards).</p> <p>Results</p> <p>A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided.</p> <p>Conclusions</p> <p>By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics.</p>http://www.biomedcentral.com/1471-2288/10/54 |
| spellingShingle | Hawkins Neil Woods Beth S Scott David A Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial BMC Medical Research Methodology |
| title | Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial |
| title_full | Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial |
| title_fullStr | Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial |
| title_full_unstemmed | Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial |
| title_short | Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial |
| title_sort | network meta analysis on the log hazard scale combining count and hazard ratio statistics accounting for multi arm trials a tutorial |
| url | http://www.biomedcentral.com/1471-2288/10/54 |
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