BACKGROUND: In a meta-analysis of trials with missing outcome data, a parameter known as informative missing odds ratio (IMOR) can be used to quantify the relationship between informative missingness and a binary outcome. IMORs also account for the increased uncertainty due to missingness in the meta-analysis results. PURPOSE: To extend the idea of IMOR into a network meta-analysis (NMA) setting in order to explore the impact of missing outcome data on the inferences about the relative effectiveness of several competing treatments in psychiatric trials. METHODS: IMORs were estimated in two datasets comparing anti-manic treatments and antidepressants. The outcome was response to treatments. In the original meta-analyses, missing participants were assumed to have failed regardless the treatment they were allocated to. To evaluate the robustness of this assumption in each dataset, several imputations of the missing outcomes were studied by an IMOR parameter in the NMA model. By comparing the odds ratios for efficacy under the initial analysis and under several assumptions about the missingness, we assessed the consistency of the conclusions. The missing data mechanism was studied by comparing the prior with the posterior IMOR distribution. Models were fitted using Markov chain Monte Carlo (MCMC) in WinBUGS. RESULTS: In both datasets, the relative effectiveness of the treatments seems to be affected only by the two extreme imputation scenarios of worst- and best-case analyses. Moreover, heterogeneity increases in both datasets under these two extreme scenarios. Overall, there is a non-significant change on the ranking of the anti-manic and antidepressant treatments. The posterior and prior IMOR distributions are very similar showing that the data do not provide any information about the true outcome in missing participants. There is a very weak indication that missing participants tend to fail in placebo and paroxetine, while the opposite occurs for sertraline, fluoxetine, and fluvoxamine. LIMITATIONS: Investigation of informative missingness was limited two classes of treatments and for dichotomous outcome measures. The proportion of missing outcomes was very low overall, and hence, the power of detecting any differences in effectiveness estimated under the various imputation methods is small. CONCLUSIONS: Sensitivity analysis to account for missing outcome data and their uncertainty in the NMA can be undertaken by extending the idea of IMOR. In two case examples, we found no differences between the various models due to low missing data rate. In line with previous observations, data carry little information about the reason of missingness.
BACKGROUND: In a meta-analysis of trials with missing outcome data, a parameter known as informative missing odds ratio (IMOR) can be used to quantify the relationship between informative missingness and a binary outcome. IMORs also account for the increased uncertainty due to missingness in the meta-analysis results. PURPOSE: To extend the idea of IMOR into a network meta-analysis (NMA) setting in order to explore the impact of missing outcome data on the inferences about the relative effectiveness of several competing treatments in psychiatric trials. METHODS: IMORs were estimated in two datasets comparing anti-manic treatments and antidepressants. The outcome was response to treatments. In the original meta-analyses, missing participants were assumed to have failed regardless the treatment they were allocated to. To evaluate the robustness of this assumption in each dataset, several imputations of the missing outcomes were studied by an IMOR parameter in the NMA model. By comparing the odds ratios for efficacy under the initial analysis and under several assumptions about the missingness, we assessed the consistency of the conclusions. The missing data mechanism was studied by comparing the prior with the posterior IMOR distribution. Models were fitted using Markov chain Monte Carlo (MCMC) in WinBUGS. RESULTS: In both datasets, the relative effectiveness of the treatments seems to be affected only by the two extreme imputation scenarios of worst- and best-case analyses. Moreover, heterogeneity increases in both datasets under these two extreme scenarios. Overall, there is a non-significant change on the ranking of the anti-manic and antidepressant treatments. The posterior and prior IMOR distributions are very similar showing that the data do not provide any information about the true outcome in missing participants. There is a very weak indication that missing participants tend to fail in placebo and paroxetine, while the opposite occurs for sertraline, fluoxetine, and fluvoxamine. LIMITATIONS: Investigation of informative missingness was limited two classes of treatments and for dichotomous outcome measures. The proportion of missing outcomes was very low overall, and hence, the power of detecting any differences in effectiveness estimated under the various imputation methods is small. CONCLUSIONS: Sensitivity analysis to account for missing outcome data and their uncertainty in the NMA can be undertaken by extending the idea of IMOR. In two case examples, we found no differences between the various models due to low missing data rate. In line with previous observations, data carry little information about the reason of missingness.
Authors: Toshi A Furukawa; Georgia Salanti; Lauren Z Atkinson; Stefan Leucht; Henricus G Ruhe; Erick H Turner; Anna Chaimani; Yusuke Ogawa; Nozomi Takeshima; Yu Hayasaka; Hissei Imai; Kiyomi Shinohara; Aya Suganuma; Norio Watanabe; Sarah Stockton; John R Geddes; Andrea Cipriani Journal: BMJ Open Date: 2016-07-08 Impact factor: 2.692
Authors: Shannon Cope; Jie Zhang; Stephen Saletan; Brielan Smiechowski; Jeroen P Jansen; Peter Schmid Journal: BMC Med Date: 2014-06-05 Impact factor: 8.775
Authors: Areti Angeliki Veroniki; Sharon E Straus; Huda M Ashoor; Jemila S Hamid; Brenda R Hemmelgarn; Jayna Holroyd-Leduc; Sumit R Majumdar; Glenn McAuley; Andrea C Tricco Journal: BMJ Open Date: 2016-01-13 Impact factor: 2.692