Dealing with missing information on covariates for excess mortality hazard regression models – Making the imputation model compatible with the substantive model

Missing data is a common issue in epidemiological databases. Among the different ways of dealing with missing data, multiple imputation has become more available in common statistical software packages. However, the incompatibility between the imputation and substantive model, which can arise when the associations between variables in the substantive model are not taken into account in the imputation models or when the substantive model is itself nonlinear, can lead to invalid inference. Aiming at analysing population-based cancer survival data, we extended the multiple imputation substantive model compatible-fully conditional specification (SMC-FCS) approach, proposed by Bartlett et al. in 2015 to accommodate excess hazard regression models. The proposed approach was compared with the standard fully conditional specification multiple imputation procedure and with the complete-case analysis using a simulation study. The SMC-FCS approach produced unbiased estimates in both scenarios tested, while the fully conditional specification produced biased estimates and poor empirical coverages probabilities. The SMC-FCS algorithm was then used for handling missing data in the evaluation of socioeconomic inequalities in survival from colorectal cancer patients diagnosed in the North Region of Portugal. The analysis using SMC-FCS showed a clearer trend in higher excess hazards for patients coming from more deprived areas. The proposed algorithm was implemented in R software and is presented as Supplementary Material.

Evaluating FIML And Multiple Imputation In Joint Ordinal-Continuous Measurements Models With Missing Data

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the performance of four approaches to handling missing data in these models, a joint ordinal-continuous full information maximum likelihood (JOC-FIML) approach and three multiple imputation approaches (fully conditional specification, fully conditional specification with latent variable formulation, and expectation-maximization with bootstrapping) combined with the weighted least squares with mean and variance adjustment (WLSMV) estimator. In a Monte-Carlo simulation, the JOC-FIML approach produced unbiased estimations of factor loadings and standard errors in almost all conditions. Fully conditional specification combined with WLSMV was second best, producing accurate estimates if the sample size was large. We recommend JOC-FIML across most conditions, except when certain ordinal categories have extremely low frequencies as it was less likely to converge. If the sample is large, fully conditional specification combined with weighted-least-squares is recommended when the FIML approach is not feasible (e.g., non-convergence, variables that predict missingness are not of interest to the analysis).

Medición de la inversión turística nacional en México: antecedentes, conceptos y una propuesta metodológica aplicada

La generación de datos es cada vez más una necesidad prioritaria para abonar a la narrativa de la importancia del turismo en la vida de los mexicanos. En este trabajo se destaca la importancia de medir la inversión turística nacional, ya que se han realizado diversos esfuerzos por reconocer un dato representativo del sector; sin embargo, aún no se ha construido una metodología ni tampoco una serie apropiada y continua de datos para el caso mexicano. El objetivo del artículo es proponer una metodología para la medición de la inversión nacional en el sector turístico que siga las recomendaciones de la Organización Mundial de Turismo, del Instituto Nacional de Estadística y Geografía y las experiencias internacionales. La metodología se propone en dos ámbitos: primero, en la aportación de un “marco conceptual” que define los activos y actividades turísticas donde se registran los montos de inversión; y segundo, la aportación de las “fuentes y métodos” con la creación de una Cédula de Registro, un Sistema de Registro y el método de imputación para la construcción de la serie de inversión con Fully Conditional Specification. La propuesta metodológica se sometió a prueba para conocer su viabilidad con sus fortalezas, oportunidades, debilidades y amenazas. Los resultados indicaron que la propuesta es factible, puesto que existe información no sistematizada a nivel de Secretarías de Turismo de los estados y disposición para implementarla; y, por otro lado, la aplicación del método de imputación se probó con datos obtenidos del estado de Guanajuato, que generó una serie de tiempo continua de forma satisfactoria.

On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures

Large-scale assessments (LSAs) use Mislevy’s “plausible value” (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the properties of methods used in current practice for dealing with missing data in background variables in educational LSAs, which rely on the missing indicator method (MIM), with other methods based on multiple imputation. In this context, we present a fully conditional specification (FCS) approach that allows for a joint treatment of PVs and missing data. Using theoretical arguments and two simulation studies, we illustrate under what conditions the MIM provides biased or unbiased estimates of population parameters and provide evidence that methods such as FCS can provide an effective alternative to the MIM. We discuss the strengths and weaknesses of the approaches and outline potential consequences for operational practice in educational LSAs. An illustration is provided using data from the PISA 2015 study.

Multiple Imputation in Multilevel Models. A Revision of the Current Software and Usage Examples for Researchers

Although modern lines for dealing with missing data are well established from the 1970s, today there is a challenge when researchers encounter this problem in multilevel models. First, there is a variety of existing software to handle missing data based on multiple imputation (MI), currently pointed out by experts as the most promising strategy. Second, the two principal paradigms of MI are joint modelling (JM) and fully conditional specification (FCS), one more complication because they are not equally useful depending on the combination of multilevel model and the estimated parameters affected by missing data. Technical literature do not contribute to ease the number of decisions that researcher has to do. Given these inconveniences, the present paper has three objectives. (1) To present a thorough revision of the most recently developed software and functions about multiple imputation in multilevel models. (2) We derive a set of suggestions, recommendations, and guides for helping researchers to handle missing data. We list a number of key questions to consider when analyzing multilevel models. (3) Finally, based on the previous relevant questions, we present two detailed examples using the recommended R packages to be easy for the researcher applying multiple imputation in multilevel models.