<p>*: Variable was excluded from modeling. Each row represents one of 617 human cases; each column represents a variable abstracted from the literature. The color of each cell indicates whether the corresponding variable was missing (dark green) or observed (light green) for the given case.</p
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
Additional file 2: Figure S2. Missing data pattern (percentage of missing values in a particular com...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
The bar chart on the left depicts the proportion of data missing for each variable. The graph on the...
<p>A total of 1528 participants were excluded due to missing data, some participants had missing dat...
<p>Note: The exclusion numbers provided are not mutually exclusive; * = missing data includes demogr...
<p>+Missing data.</p><p>A summary of descriptive information ofand study quality by outcome.</p
When exploring missing data techniques in a realistic scenario, the current literature is limited: m...
Many missing data studies have simulated data, randomly deleted values, and investigated the method ...
Missing data is a common issue in research that, if improperly handled, can lead to inaccurate concl...
Comparison of those who were included in the final analysis with those who were excluded for missing...
<p>Colored boxes are non-missing test scores, white boxes are missing test scores.</p
a<p> = 1 missing data point.</p>b<p> = 2 missing data points.</p>c<p> = 3 missing data points.</p><p...
<p>*data may not add to 841 due to missing data</p><p>Description of study population (n = 841).</p
<p>Patterns of missingness in the birth variables and covariates before imputation.</p
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
Additional file 2: Figure S2. Missing data pattern (percentage of missing values in a particular com...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...
The bar chart on the left depicts the proportion of data missing for each variable. The graph on the...
<p>A total of 1528 participants were excluded due to missing data, some participants had missing dat...
<p>Note: The exclusion numbers provided are not mutually exclusive; * = missing data includes demogr...
<p>+Missing data.</p><p>A summary of descriptive information ofand study quality by outcome.</p
When exploring missing data techniques in a realistic scenario, the current literature is limited: m...
Many missing data studies have simulated data, randomly deleted values, and investigated the method ...
Missing data is a common issue in research that, if improperly handled, can lead to inaccurate concl...
Comparison of those who were included in the final analysis with those who were excluded for missing...
<p>Colored boxes are non-missing test scores, white boxes are missing test scores.</p
a<p> = 1 missing data point.</p>b<p> = 2 missing data points.</p>c<p> = 3 missing data points.</p><p...
<p>*data may not add to 841 due to missing data</p><p>Description of study population (n = 841).</p
<p>Patterns of missingness in the birth variables and covariates before imputation.</p
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
Additional file 2: Figure S2. Missing data pattern (percentage of missing values in a particular com...
1<p>Descriptives for variables post imputation were calculated using Rubin’s rules.</p>2<p>NA = miss...