Nearly 6000 clinical trial results are currently missing from the European trial registry, despite transparency rules requiring countries to upload results within 12 months of trial completion, a report has found. This was reported in bmj
Researchers from the University of Oxford said the findings show that medicines regulators in the 14 European countries included in the report have failed to ensure that important data on new drugs and vaccines are rapidly and consistently made public.
The report, published 5 July, found that the largest gaps were in Italy (1221 results missing).
The validity of clinical research is potentially threatened by missing data. Any variable measured in a study can have missing values, including the exposure, the outcome, and confounders. When missing values are ignored in the analysis, only those subjects with complete records will be included in the analysis. This may lead to biased results and loss of power. We explain why missing data may lead to bias and discuss a commonly used classification of missing data.
According to a report Missing data can result in bias, although this need not always be the case, depending on the missing data mechanism and the applied statistical approach. In a complete case analysis, already with low percentages of missing values there can be substantial bias and with high percentages there need not be a bias. Nevertheless, the percentage of missing values may be related to the quality of the study in general and specifically the quality of the collected data.
As such, the percentage of missing values may be a proxy for study quality and risk of bias, although not necessarily bias due to missing data. Even randomised trials are not immune to bias due to missing data (8, 9, 10), although the extent of missing data in trials is probably smaller than in observational studies. As default statistical methods ignore subjects with missing values, each reported analysis should be accompanied by the actual number of subjects included in that analysis.
Apart from a possible impact in terms of bias, missing data reduce the precision of effect estimations. Instead of depreciating any missing data bias, because of a study being a randomised trial or because of the low percentage of missing values, researchers should discuss the possible missing data mechanism in relation to the data analysis and consider possible solutions, including imputation techniques.
