Imputation methods for missing outcome data in meta-analysis of clinical trials.
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We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving ;informative missingness odds ratios' (IMORs).We propose that available reasons for missingness be used to determine appropriate IMORs.We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges.
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PubMed Central - PubMed
Affiliation: MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK. julian.higgins@mrc-bsu.cam.ac.uk
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Background: Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations. Purpose: To review and develop imputation methods for missing outcome data in meta-analysis of clinical trials with binary outcomes. Methods: We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving ;informative missingness odds ratios' (IMORs). We describe several choices for weighting studies in the meta-analysis, and illustrate methods using a meta-analysis of trials of haloperidol for schizophrenia. Results: IMORs describe the relationship between the unknown risk among missing participants and the known risk among observed participants. They are allowed to differ between treatment groups and across trials. Application of IMORs and other methods to the haloperidol trials reveals the overall conclusion to be robust to different assumptions about the missing data. Limitations: The methods are based on summary data from each trial (number of observed positive outcomes, number of observed negative outcomes and number of missing outcomes) for each intervention group. This limits the options for analysis, and greater flexibility would be available with individual participant data. Conclusions: We propose that available reasons for missingness be used to determine appropriate IMORs. We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges. Related in: MedlinePlus |
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Mentions: It is useful to classify missing outcome data according to the relationship between nonavailability of a particular value and the observed and unobserved values. We will use the term ‘missingness’ for the nonavailability of a participant's outcome. First, if missingness of an outcome is not related to any observed or unobserved variables, then the missing data are described as ‘missing completely at random’ (Figure 1(a) and (b)). Analysis restricted to individuals with complete data is always valid when the data are missing completely at random. If missingness of an outcome may be related to observed or unobserved variables, but is not related to the actual value of the outcome, conditional on the observed variables, then the missing data are described as ‘missing at random’ (Figure 1(c) and (d)). An alternative term is ‘ignorable’, because a correct likelihood-based analysis of all the observed data is valid [3]. (Strictly, a further condition is required, but this is true in almost all practical applications.) ‘Missing completely at random’ is a special case of ‘missing at random’. Finally, if missingness of an outcome is related to the value of that outcome, even conditional on other observed variables, then the missing data are described as ‘informatively missing’. This could be because of some common unobserved cause of both missingness and the outcomes (Figure 1(e)) or because the outcome directly causes missingness (Figure 1(f )). Alternative terms are ‘missing not at random’, ‘not missing at random’ or ‘nonignorable’, the last so called because a likelihood-based analysis of the observed data alone is typically biased [3]. Figure 1 |
View Article: PubMed Central - PubMed
Affiliation: MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK. julian.higgins@mrc-bsu.cam.ac.uk
Background: Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.
Purpose: To review and develop imputation methods for missing outcome data in meta-analysis of clinical trials with binary outcomes.
Methods: We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving ;informative missingness odds ratios' (IMORs). We describe several choices for weighting studies in the meta-analysis, and illustrate methods using a meta-analysis of trials of haloperidol for schizophrenia.
Results: IMORs describe the relationship between the unknown risk among missing participants and the known risk among observed participants. They are allowed to differ between treatment groups and across trials. Application of IMORs and other methods to the haloperidol trials reveals the overall conclusion to be robust to different assumptions about the missing data.
Limitations: The methods are based on summary data from each trial (number of observed positive outcomes, number of observed negative outcomes and number of missing outcomes) for each intervention group. This limits the options for analysis, and greater flexibility would be available with individual participant data.
Conclusions: We propose that available reasons for missingness be used to determine appropriate IMORs. We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges.