EMA Publish Guidance on Missing Data from Confirmatory Clinical Trials.

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It should be the aim of those conducting clinical trials to achieve complete capture of all data from all patients, including those who discontinue from treatment. Whilst it is unavoidable that some data are missing from all confirmatory clinical trials, it should be noted that just ignoring missing data is not an acceptable option when planning, conducting or interpreting the analysis of a confirmatory clinical trial. The reason for missing data and handling of missing data in the analysis represent critical factors in
the regulatory assessment of all confirmatory clinical trials. The main focus of this guideline is issues associated with the analysis of the primary efficacy endpoint where patients are followed up over time. However, by careful planning it is possible to reduce the amount of data that are missing. This is important because missing data are a potential source of bias when analysing data from clinical trials. Interpretation of the results of a trial is always problematic when the proportion of missing values is
substantial. When this occurs, the uncertainty of the likely treatment effect can become such that it is not possible to conclude that evidence of efficacy has been established.
In confirmatory trials the primary analysis is commonly performed on the full analysis set as this analysis is consistent with the intention to treat (ITT) principle. If data for some subjects are missing for the primary endpoint it is necessary to specify how all randomised patients can be included in the statistical analysis. However, there is no universally applicable method that adjusts the analysis to take into account that some values are missing, and different approaches may lead to different conclusions.
To avoid concerns over data-driven selection of methods, it is essential to pre-specify the selected methods in the statistical section of the study protocol or analysis plan. Unfortunately, when there are missing data, all approaches to analysis rely on assumptions that cannot be verified. It should be noted that the strategy employed to handle missing values might in itself be a source of bias. A critical discussion of the number, timing, pattern, reason for and possible implications of missing values in
efficacy and safety assessments should be included in the clinical report as a matter of routine. It will be useful to investigate the pattern of missing data in previous trials in the same or similar indications for related medicinal products. This could assist in identifying additional actions to minimise the amount of missing data during the conduct of the trial, the choice of the primary analysis method and in determining how missing data will be handled in this analysis.
A positive regulatory decision must be based on an analysis where the possibility of important bias in favour of the experimental agent can be excluded. The justification for selecting a particular method should not be based primarily on the properties of the method under particular assumptions but on whether it is likely that it will provide an appropriate estimate for the comparison of primary regulatory interest in the circumstances of the trial under consideration.. An appropriate analysis would provide a
point estimate that is unlikely to be biased in favour of experimental treatment to an important degree (under reasonable assumptions) and a confidence interval that does not underestimate the variability of the point estimate to an important extent. The type of bias that can critically affect interpretation depends upon the objective of the study (to show superiority, non-inferiority or equivalence). As the choice of primary analysis will be based on assumptions that cannot be verified it will almost always be
necessary to investigate the robustness of trial results through appropriate sensitivity analyses that make different assumptions.

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