Estimating propensity scores with missing covariate data using general location mixture models

In many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on children’s cognitive abilities

Estimating propensity scores with missing covariate data using general location mixture models

In many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on children’s cognitive abilities

JointAI: Joint Analysis and Imputation of Incomplete Data in R

Missing data occur in many types of studies and typically complicate the analysis. Multiple imputation, either using joint modelling or the more flexible fully conditional specification approach, are popular and work well in standard settings. In settings involving non-linear associations or interactions, however, incompatibility of the imputation model with the analysis model is an issue often resulting in bias. Similarly, complex outcomes such as longitudinal or survival outcomes cannot be adequately handled by standard implementations. In this paper, we introduce the R package JointAI, which utilizes the Bayesian framework to perform simultaneous analysis and imputation in regression models with incomplete covariates. Using a fully Bayesian joint modelling approach it overcomes the issue of uncongeniality while retaining the attractive flexibility of fully conditional specification multiple imputation by specifying the joint distribution of analysis and imputation models as a sequence of univariate models that can be adapted to the type of variable. JointAI provides functions for Bayesian inference with generalized linear and generalized linear mixed models and extensions thereof as well as survival models and joint models for longitudinal and survival data, that take arguments analogous to corresponding well known functions for the analysis of complete data from base R and other packages. Usage and features of JointAI are described and illustrated using various examples and the theoretical background is outlined.Comment: imputation, Bayesian, missing covariates, non-linear, interaction, multi-level, survival, joint model R, JAG

The use of weights to account for non-response and drop-out

Background: Empirical studies in psychiatric research and other fields often show substantially high refusal and drop-out rates. Non-participation and drop-out may introduce a bias whose magnitude depends on how strongly its determinants are related to the respective parameter of interest. Methods: When most information is missing, the standard approach is to estimate each respondent’s probability of participating and assign each respondent a weight that is inversely proportional to this probability. This paper contains a review of the major ideas and principles regarding the computation of statistical weights and the analysis of weighted data. Results: A short software review for weighted data is provided and the use of statistical weights is illustrated through data from the EDSP (Early Developmental Stages of Psychopathology) Study. The results show that disregarding different sampling and response probabilities can have a major impact on estimated odds ratios. Conclusions: The benefit of using statistical weights in reducing sampling bias should be balanced against increased variances in the weighted parameter estimates

Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality

"To protect the cofidentiality of survey respondents' identities and sensitive attributes, statistical agencies can release data in which cofidential values are replaced with multiple imputations. These are called synthetic data. We propose a two-stage approach to generating synthetic data that enables agencies to release different numbers of imputations for different variables. Generation in two stages can reduce computational burdens, decrease disclosure risk, and increase inferential accuracy relative to generation in one stage. We present methods for obtaining inferences from such data. We describe the application of two stage synthesis to creating a public use file for a German business database." (Author's abstract, IAB-Doku) ((en))IAB-Betriebspanel, Datenaufbereitung, Datenanonymisierung, Datenschutz, angewandte Statistik, statistische Methode, Arbeitsmarktforschung, Imputationsverfahren

Comprehensive Education, Social Attitudes and Civic Engagement

The claims made for comprehensive secondary schooling in Britain have tended to invoke three kinds of rationale – relating to attainment, social mobility and the creation of an integrated or harmonious society. Much research attention has been given to the first of these, and in particular to whether comprehensive schooling reduces social inequalities of attainment and progression. Some attention, notably very recently (Boliver & Swift, 2011), has been given to the second, following from the work on attainment. The third has been somewhat neglected, and is the topic of this paper. Attempts are made to distinguish between general effects of education on civic-mindedness – in the sense that, for example, on the whole, better-educated people tend to be more liberal, respectful of diversity, and so on – and the effects specifically associated with having attended a non-selective school or non-selective system. As with the recent research on comprehensive education and social mobility, long-term effects are of greater relevance to the claims made for the consequences of comprehensive schooling than the effects in late adolescence or early adulthood. The data source is the British National Child Development Study