Imputation results of E[p*] under the null for different missing data combinations with initial value . Grey lines correspond to the case of equal missingness probability in both arms; Blue lines correspond to missingness in the control arm; Red lines correspond to missingness in the experimental arm. Solid lines correspond to the results without mean imputation, while the dashed lines correspond to the results with mean imputation.</p
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Simulation results of under the null for different missing data combinations. We illustrate the res...
Imputation results of E[p*] under the null for different missing data combinations with initial valu...
Imputation results of E[p*] under the null for different missing data combinations, with imputation ...
Imputation results of E[p*] under the alternative for different missing data combinations with initi...
Imputation results of E[p*] under the alternative for different missing data combinations, with impu...
Simulation results of E[p*] under the null for different missing data combinations. Grey lines corre...
Simulation results of E[p*] under the alternative for different missing data combinations. Grey line...
The impact of missing data is similar under different algorithms under the null: the impact due to t...
<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper...
Imputation is the process of replacing missing data with substituted values. Missing data can create...
<p>Patterns of missingness in the birth variables and covariates before imputation.</p
Existence of missing values creates a big problem in real world data. Unless those values are missi...
<p>The amount of missing data was low and was imputed using multiple imputations.</p><p>Missing data...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Simulation results of under the null for different missing data combinations. We illustrate the res...
Imputation results of E[p*] under the null for different missing data combinations with initial valu...
Imputation results of E[p*] under the null for different missing data combinations, with imputation ...
Imputation results of E[p*] under the alternative for different missing data combinations with initi...
Imputation results of E[p*] under the alternative for different missing data combinations, with impu...
Simulation results of E[p*] under the null for different missing data combinations. Grey lines corre...
Simulation results of E[p*] under the alternative for different missing data combinations. Grey line...
The impact of missing data is similar under different algorithms under the null: the impact due to t...
<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper...
Imputation is the process of replacing missing data with substituted values. Missing data can create...
<p>Patterns of missingness in the birth variables and covariates before imputation.</p
Existence of missing values creates a big problem in real world data. Unless those values are missi...
<p>The amount of missing data was low and was imputed using multiple imputations.</p><p>Missing data...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Simulation results of under the null for different missing data combinations. We illustrate the res...