However, this problem is independent of the method, our evolutionary learning classifier system is a useful method for incomplete datasets.
That is, the prognostic method is more stable and precise than the traditional method for incomplete trial information.
The meta-analysis results obtained by using prognostic method, interval method, and traditional method for incomplete trials information are provided in Table 1.
In addition, in Example 5, when the SEM value of X1 is missing, we found that the result obtained by the prognostic method is not as accurate as that obtained by the traditional method for incomplete information.
A small caveat of the prognostic and the interval methods is that when the input data are imprecise (as shown in Examples 3 and 4), we may obtain worse results than that from the traditional method for incomplete information.
This is because when some input data are imprecise, the prognostic method will get wrong predictions for missing SEMs, hence obtain worse meta-analysis results whilst the traditional method for incomplete trial information simply abandons trials with incomplete information, avoiding any wrong predictions and therefore may obtain better results.
However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).
The use of new methods for handling incomplete information is of fundamental importance.
This study is first in comparing a broad range of methods for combining incomplete data sets.
The statistical literature is rich with methods for handling incomplete data, including approaches for sensitivity analysis and MNAR data.
