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Nevertheless, tree-based algorithms can handle complete data as well as missing data usually assuming Missing Completely At Random (MCAR).
Missing data usually involved 1% or less of observations, however, our results were stable given the large size of our dataset.
Except multilocus and haplotype-based tests, analyses used the full cyt b database (sometimes divided among groups); iteratively adding/removing samples with missing data (usually <5 bp) had no qualitative effect on results.
In smaller cohorts and in case control studies, risk estimates based on classic frequentist modeling are likely to produce unreliable estimates in small strata, and any likelihood-based analysis for small data, or even worse, missing data, usually involves computationally intensive methods or ad hoc adjustments.
However, these methods are applicable only to data with "missing at random" (MAR) pattern of missingness, while errors of omission (interpreted as missing data) usually do not satisfy the MAR requirements.
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1% of missing data is usually considered trivial, 1 5% as manageable.
In some cases, a Delphi panel will be required to obtain missing data, which usually relates to health care utilisation.
Unfortunately, because the observed data do not give us any information about the missing data, there is usually no way of knowing if any given analysis with missing data is correct.
Analytical methods that account for administrative censoring and missing data, which are usually present in randomised trials, are not explicitly reviewed but are briefly summarised in a web appendix (http://www.herc.ox.ac.uk/downloads/support_pub).ac.uk/downloads/support_pub
Usually missing data cases are known as "available cases" and the possible solution is either to omit the missing data or to use an imputation method, although controversies remain.
Bearing in mind, the LS test is only applicable for monotone missing data, the two methods usually provided the same conclusion; that is, there was evidence against MCAR suggesting missingness was MAR or possibly MNAR.
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