Clinical psychology researchers studying adolescents and young adults long have been interested in characterizing the latent categorical (classes/profiles) versus continuous (factors) nature of psychological syndromes. To inform this debate, researchers sometimes compare the fit of finite mixture versus factor analysis models to symptom data. This study explains and evaluates how missing data h...

Multiple imputation is becoming increasingly established as the leading practical approach to modelling partially observed data, under the assumption that the data are missing at random. However, many medical and social datasets are multilevel, and this structure should be reflected not only in the model of interest, but also in the imputation model. In particular, the imputation model should r...

Genotype imputation from low-density to high-density (SNP) chips is an important step before applying genomic selection, because denser chips can provide more reliable genomic predictions. In the current research, the accuracy of genotype imputation from low and moderate-density panels (5K and 50K) to high-density panels in the purebred and crossbred populations was assessed. The simulated popu...

This paper presents a simple way to handle missing values in categorical covariates, namely conditional probability imputation . Properties of this technique are given for various patterns of missing data in regression studies . An example shows its use in the proportional hazards model . The probability imputation technique is furthermore compared with multiple imputation and model-based appro...

Multiple imputation is particularly well suited to deal with missing data in large epidemiologic studies, because typically these studies support a wide range of analyses by many data users. Some of these analyses may involve complex modeling, including interactions and nonlinear relations. Identifying such relations and encoding them in imputation models, for example, in the conditional regres...

Risk models that aim to predict the future course and outcome of disease processes are increasingly used in health research, and it is important that they are accurate and reliable. Most of these risk models are fitted using routinely collected data in hospitals or general practices. Clinical outcomes such as short-term mortality will be near-complete, but many of the predictors may have missin...

The National Health Interview Survey (NHIS) provides a rich source of data for studying relationships between income and health and for monitoring health and health care for persons at different income levels. However, the nonresponse rates are high for two key items, total family income in the previous calendar year and personal earnings from employment in the previous calendar year. To handle...

Multiple imputation is particularly well suited to deal with missing data in large epidemiologic studies, because typically these studies support a wide range of analyses by many data users. Some of these analyses may involve complex modeling, including interactions and nonlinear relations. Identifying such relations and encoding them in imputation models, for example, in the conditional regres...

Hot deck imputation for nonrespondents is often used in surveys. It is a common practice to treat the imputed values as if they are true values, and compute survey estimators and their variance estimators using standard formulas. The variance estimators, however, have seriously negative biases when the rate of nonresponse is appreciable. Methods such as the multiple imputation and the adjusted ...

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missin...