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. (TIF)</p
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Calibration plots (bottom panel) and receiver operating characteristic curves (top panel) for test c...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
Imputation results of E[p*] under the null for different missing data combinations, with imputation ...
Imputation results of E[p*] under the null for different missing data combinations with initial valu...
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...
Simulation results of under the null for different missing data combinations. We illustrate the res...
Imputation is the process of replacing missing data with substituted values. Missing data can create...
Existence of missing values creates a big problem in real world data. Unless those values are missi...
<p>Patterns of missingness in the birth variables and covariates before imputation.</p
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Calibration plots (bottom panel) and receiver operating characteristic curves (top panel) for test c...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
Imputation results of E[p*] under the null for different missing data combinations, with imputation ...
Imputation results of E[p*] under the null for different missing data combinations with initial valu...
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...
Simulation results of under the null for different missing data combinations. We illustrate the res...
Imputation is the process of replacing missing data with substituted values. Missing data can create...
Existence of missing values creates a big problem in real world data. Unless those values are missi...
<p>Patterns of missingness in the birth variables and covariates before imputation.</p
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Calibration plots (bottom panel) and receiver operating characteristic curves (top panel) for test c...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...