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Construct a logistic model for missing occgp regressing on variables distot, age, sex, and status and excluding individuals with missing values imposed by stage 1.
Construct a logistic model for missing occgp regressing on variables onset, distot, age, sex, and status and excluding individuals with missing values imposed by stages 1 & 2. Impose missing data in cprs0 for individuals with fitted probabilities falling in the top eighth of probabilities.
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b: 31 missing values imputed.
For MCAR, missing values were randomly imposed for approximately 33% of values in the GHQ at wave 8.
OR represents the odds of having two variables with missing observations, and was calculated using the proportion of missing values for each variable and the degree of overlap between variables for each of the five overall amounts of missingness to be imposed.
Before pregnancy: 13 missing values; Week 17: 13 missing values; Week 32: 3 missing values; 8 weeks after delivery: 5 missing values.
There were no missing values.
*Number varies due to missing values.
Missing values correspond to concentrations below detection.
The missing values amounted to 3.3%%.
c): Excluding 56 observations with missing values.
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