Logistic regression models with missing covariate values for complex survey data

Prediction models and associated factors on the fertility behaviors of the floating population in China

The floating population has been growing rapidly in China, and their fertility behaviors do affect urban management and development. Based on the data set of the China Migrants Dynamic Survey in 2016, the logistic regression model and multiple linear regression model were used to explore the related factors of fertility behaviors among the floating populace. The artificial neural network model, the naive Bayes model, and the logistic regression model were used for prediction. The findings showed that age, gender, ethnic, household registration, education level, occupation, duration of residence, scope of migration, housing, economic conditions, and health services all affected the reproductive behavior of the floating population. Among them, the improvement duration of post-migration residence and family economic conditions positively impacted their fertility behavior. Non-agricultural new industry workers with college degrees or above living in first-tier cities were less likely to have children and more likely to delay childbearing. Among the prediction models, both the artificial neural network model and logistic regression model had better prediction effects. Improving the employment and income of new industry workers, and introducing preferential housing policies might improve their probability of bearing children. The artificial neural network and logistic regression model could predict individual fertility behavior and provide a scientific basis for the urban population management.

How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method

The authors analyze the efficiency of six missing data techniques for categorical item nonresponse under the assumption that data are missing at random or missing completely at random. By efficiency, the authors mean a procedure that produces an unbiased estimate of true sample properties that is also easy to implement. The investigated techniques include listwise deletion, mode substitution, random imputation, two regression imputations, and a Bayesian model-based procedure. The authors analyze efficiency under six experimental conditions for a survey-based data set. They find that listwise deletion is efficient for the data analyzed. If data loss due to listwise deletion is an issue, the analysis points to the Bayesian method. Regression imputation is also efficient, but the result is conditioned on the specific data structure and may not hold in general. Additional problems arise when using regression imputation, making it less appropriate.

Missing covariates in logistic regression, estimation and distribution selection

We derive explicit formulae for estimation in logistic regression models where some of the covariates are missing. Our approach allows for modelling the distribution of the missing covariates either as a multivariate normal or as a multivariate t-distribution. A main advantage of this method is that it is fast and does not require the use of iterative procedures. A model selection method is derived which allows to choose among these distributions. In addition, we consider versions of Akaike’s information criterion that are based on the expectation–maximization algorithm and multiple imputation methods that have a wide applicability to model selection in likelihood models in general.

Self-report of high cholesterol: determinants of validity in U.S. adults

BACKGROUND

Hypercholesterolemia is a major cardiovascular risk factor, and cholesterol awareness is important in both clinical practice and in public health. We evaluated the validity of self-reported hypercholesterolemia and identified determinants of validity.

METHODS

The study design was a cross-sectional survey, from 1988 to 1994, of adult participants (N=8236) from the Third National Health and Nutrition Examination Survey for whom self-report of hypercholesterolemia and serum measurement were available. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for self-reported hypercholesterolemia were calculated using total cholesterol > or =5.17 mmol/L (200 mg/dL) and/or taking cholesterol-lowering medication as the criterion standard.

RESULTS

Overall test characteristics for self-report were sensitivity, 51%; specificity, 89%; PPV, 87%; and NPV, 55%. Sensitivity of self-report was higher among older subjects and non-Hispanic whites, specificity was higher among subjects with >12 years of education, PPV was higher in older subjects, and NPV was higher in younger subjects and in those with >12 years of education. Using higher cholesterol thresholds to define hypercholesterolemia led to higher sensitivity, lower specificity, lower PPV, and higher NPV. Sociodemographic and anthropometric predictors of validity were identified by logistic regression.

CONCLUSIONS

Due to low sensitivity, self-reported hypercholesterolemia should be used with caution, both during the patient encounter and for surveillance of trends in hypercholesterolemia in the absence of measured cholesterol levels. Specificity is consistently much higher than sensitivity. The high PPV may be of use in certain clinical situations. Such validation studies should form the foundation for future research based on self-report.