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

Testing the proportional odds assumption in multiply imputed ordinal longitudinal data

A popular choice when analyzing ordinal data is to consider the cumulative proportional odds model to relate the marginal probabilities of the ordinal outcome to a set of covariates. However, application of this model relies on the condition of identical cumulative odds ratios across the cut-offs of the ordinal outcome; the well-known proportional odds assumption. This paper focuses on the assessment of this assumption while accounting for repeated and missing data. In this respect, we develop a statistical method built on multiple imputation (MI) based on generalized estimating equations that allows to test the proportionality assumption under the missing at random setting. The performance of the proposed method is evaluated for two MI algorithms for incomplete longitudinal ordinal data. The impact of both MI methods is compared with respect to the type I error rate and the power for situations covering various numbers of categories of the ordinal outcome, sample sizes, rates of missingness, well-balanced and skewed data. The comparison of both MI methods with the complete-case analysis is also provided.We illustrate the use of the proposed methods on a quality of life data from a cancer clinical trial

Robust and Censored Modeling and Prediction of Progression in Glaucomatous Visual Fields

Citation: Bryan SR, Vermeer KA, Eilers PHC, Lemij HG, Lesaffre EMEH. Robust and censored modeling and prediction of progression in glaucomatous visual fields. Invest Ophthalmol Vis Sci. 2013;54:6694-6700. DOI:10.1167/iovs.12-11185 PURPOSE. Classic regression is based on certain assumptions that conflict with visual field (VF) data. We investigate and evaluate different regression models and their assumptions in order to determine point-wise VF progression in glaucoma and to better predict future field loss for personalised clinical glaucoma management. METHODS. Standard automated visual fields of 130 patients with primary glaucoma with a minimum of 6 years of follow-up were included. Sensitivity estimates at each VF location were regressed on time with classical linear and exponential regression models, as well as different variants of these models that take into account censoring and allow for robust fits. These models were compared for the best fit and for their predictive ability. The prediction was evaluated at six measurements (approximately 3 years) ahead using varying numbers of measurements. RESULTS. For fitting the data, the classical uncensored linear regression model had the lowest root mean square error and 95th percentile of the absolute errors. These errors were reduced in all models when increasing the number of measurements used for the prediction of future measurements, with the classical uncensored linear regression model having the lowest values for these errors irrespective of how many measurements were included. CONCLUSIONS. All models performed similarly. Despite violation of its assumptions, the classical uncensored linear regression model appeared to provide the best fit for our data. In addition, this model appeared to perform the best when predicting future VFs. However, more advanced regression models exploring any temporal-spatial relationships of glaucomatous progression are needed to reduce prediction errors to clinically meaningful levels

Male and female SMI outpatients and the annual incident rate of crime victimisation relative to that in the general population.

a<p>Comprises burglary, attempted burglary, bicycle theft, car theft, theft from car, pick-pocketing, robbery, theft (other).</p>b<p>Comprises burglary, attempted burglary, bicycle theft, pick-pocketing, robbery, theft (other).</p>c<p>Comprises vandalism of car, vandalism (other).</p>d<p>Comprises sexual harassment or assault, threats of violence, physical assault.</p>e<p>Comprises burglary, attempted burglary, bicycle theft, car theft, theft from car, car vandalism, pick-pocketing, robbery, theft (other), vandalism (other), sexual harassment or assault, threats of violence, physical assault, crime (other).</p>f<p>Comprises burglary, attempted burglary, bicycle theft, pick-pocketing, robbery, theft (other), vandalism (other), sexual harassment or assault, threats of violence, physical assault, crime (other).</p><p>*p<.05.</p>†<p>Male car owners in unweighted sample (N = 144); Female car owners in unweighted sample (N = 116); Male car owners in matched IVM 2011 sample (N = 15,786); Female car owners in matched IVM 2011 sample (N = 18,375).</p>#<p>SMI outpatient sample weighted for age, educational level and ethnicity; IVM 2011 sample matched by region.</p>§<p>As female SMI outpatients reported no incidents of car theft or theft from car, no incidence rates and incidence rate ratios could be calculated.</p>¥<p>Unweighted data.</p

SMI Outpatients and the annual weighted and unweighted prevalences of crime polyvictimisation (%) relative to the prevalences in the general population.

a<p>Comprises burglary, attempted burglary, bicycle theft, pick-pocketing, robbery, theft (other), vandalism (other), sexual harassment or assault, threats of violence, physical assault, crime (other).</p>b<p>Comprises burglary, attempted burglary, bicycle theft, pick-pocketing, robbery, theft (other).</p>c<p>Comprises sexual harassment or assault, threats of violence, physical assault.</p><p>*p<.05.</p>#<p>SMI outpatient sample weighted for sex, age, ethnicity, and educational level; IVM 2011 sample matched by region. The general population serves as a reference category.</p>‡<p>Weighted for sex, age, ethnicity and educational level.</p

Male and female SMI outpatients and the prevalences of annual crime victimisation (%) relative to the prevalences in the general population.

a<p>Comprises burglary, attempted burglary, bicycle theft, car theft, theft from car, pick-pocketing, robbery, theft (other).</p>b<p>Comprises burglary, attempted burglary, bicycle theft, pick-pocketing, robbery, theft (other).</p>c<p>Comprises vandalism of car, vandalism (other).</p>d<p>Comprises sexual harassment or assault, threats of violence, physical assault.</p>e<p>Comprises burglary, attempted burglary, bicycle theft, car theft, theft from car, car vandalism, pick-pocketing, robbery, theft (other), vandalism (other), sexual harassment or assault, threats of violence, physical assault, crime (other).</p>f<p>Comprises burglary, attempted burglary, bicycle theft, pick-pocketing, robbery, theft (other), vandalism (other), sexual harassment or assault, threats of violence, physical assault, crime (other).</p><p>*p<.05.</p>†<p>Male car owners in unweighted sample (N = 144); Female car owners in unweighted sample (N = 116); Male car owners in matched IVM 2011 sample (N = 15,786); Female car owners in matched IVM 2011 sample (N = 18,375).</p>#<p>SMI outpatient sample weighted for age, educational level and ethnicity; IVM 2011 sample matched by region.</p>§<p>As female SMI outpatients reported no incidents of car theft or theft from car, prevalence rates and relative rate ratios could not be calculated.</p>¥<p>Unweighted data.</p

Data-acquisition flowchart.

<p>Data-acquisition flowchart.</p

Respondents’ sociodemographic characteristics.

<p>Respondents’ sociodemographic characteristics.</p