Sentence examples for missing data assumption from inspiring English sources

A sensitivity analysis to this missing data assumption has been undertaken using a pro-rata missing value score for the physical items.

Sensitivity analyses will be conducted to assess the robustness of the missing data assumption made in the primary analysis.

However, the selection model utilizing the ignorable missing data assumption revealed that the previous number of drugs predicted the probability of dropout.

Out of 103 reviewed papers, only 20 studies (19%) provided an explicit statement regarding the missing data assumption used in the primary analysis (e.g. MCAR or MAR).

For the MMSE score, the best fitting model was the selection model utilizing the non-ignorable missing data assumption with additional predictors for dropout.

This was done to assess the impact of the missing data assumption on our findings and the robustness of the adjustment using the high dimensional propensity score.

Finally, two applied examples are used to demonstrate the sensitivity of inferences under different missing data assumptions and how this may change the substantive interpretation.

These advantages include, but are not limited to, the ability to incorporate time-varying predictors, handle dependence among repeated observations in a very flexible manner, and to provide accurate estimates with missing data under fairly unrestrictive missing data assumptions.

Sensitivity of conclusions to missing data assumptions will be evaluated.

In addition, sensitivity analysis considering plausible models and missing data assumptions will be used to check the robustness of results across possible missing data assumptions.

Although regression methods are robust to missing data assumptions, there is still the possibility of bias.