A Sip of Cool Water: Pregnancy Accommodation After the ADA Amendments Act

When April Roller began making repeated trips to the restroom because of morning sickness and pregnancy-related dizziness, her supervisor told her that her employer did not pay [her] to pee. Rather than accommodating her need for more frequent restroom visits, the supervisor offered to buy her a larger wastebasket so that she could take care of vomiting without having to visit the bathroom or leave her seat. Similarly, a sales associate was denied permission to carry a water bottle, which she needed for her pregnancy-related urinary tract and bladder infections. A cashier was denied permission to use a stool, which she needed because her doctor had forbidden her to stand for more than six hours at a time because of pregnancy-related circulation problems. A nursing home activity director was denied accommodation when her doctor imposed lifting restrictions to prevent miscarriage, despite the fact that lifting was a minor part of her job with which her co-workers were willing to help. All four women eventually lost their jobs

A Sip of Cool Water: Pregnancy Accommodation After the ADA Amendments Act

When April Roller began making repeated trips to the restroom because of morning sickness and pregnancy-related dizziness, her supervisor told her that her employer did not pay [her] to pee. Rather than accommodating her need for more frequent restroom visits, the supervisor offered to buy her a larger wastebasket so that she could take care of vomiting without having to visit the bathroom or leave her seat. Similarly, a sales associate was denied permission to carry a water bottle, which she needed for her pregnancy-related urinary tract and bladder infections. A cashier was denied permission to use a stool, which she needed because her doctor had forbidden her to stand for more than six hours at a time because of pregnancy-related circulation problems. A nursing home activity director was denied accommodation when her doctor imposed lifting restrictions to prevent miscarriage, despite the fact that lifting was a minor part of her job with which her co-workers were willing to help. All four women eventually lost their jobs

OpenCases: case studies on openness in education

OpenCases is a study which is part of the OpenEdu Project. It is a qualitative study consisting of a review of literature on open education and nine in-depth case studies of higher education institutions, a consortium of universities, a private organisation and a national initiative. It analysed the rationale and enabling conditions for involvement in open education, open education activities, strategies, impact, challenges and prospects. The main outcome of this study is evidence that a large number of OER have reached a large group of learners. However, completion rates of MOOCs are low. Accreditation is not formalised and in general its impact on employability is not measure

Random Forests for time-fixed and time-dependent predictors: The DynForest R package

The R package DynForest implements random forests for predicting a continuous, a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent predictors. The main originality of DynForest is that it handles time-dependent predictors that can be endogeneous (i.e., impacted by the outcome process), measured with error and measured at subject-specific times. At each recursive step of the tree building process, the time-dependent predictors are internally summarized into individual features on which the split can be done. This is achieved using flexible linear mixed models (thanks to the R package lcmm) which specification is pre-specified by the user. DynForest returns the mean for continuous outcome, the category with a majority vote for categorical outcome or the cumulative incidence function over time for survival outcome. DynForest also computes variable importance and minimal depth to inform on the most predictive variables or groups of variables. This paper aims to guide the user with step-by-step examples for fitting random forests using DynForest

Random survival forests with multivariate longitudinal endogenous covariates

Predicting the individual risk of a clinical event using the complete patient history is still a major challenge for personalized medicine. Among the methods developed to compute individual dynamic predictions, the joint models have the assets of using all the available information while accounting for dropout. However, they are restricted to a very small number of longitudinal predictors. Our objective was to propose an innovative alternative solution to predict an event probability using a possibly large number of longitudinal predictors. We developed DynForest, an extension of competing-risk random survival forests that handles endogenous longitudinal predictors. At each node of the tree, the time-dependent predictors are translated into time-fixed features (using mixed models) to be used as candidates for splitting the subjects into two subgroups. The individual event probability is estimated in each tree by the Aalen-Johansen estimator of the leaf in which the subject is classified according to his/her history of predictors. The final individual prediction is given by the average of the tree-specific individual event probabilities. We carried out a simulation study to demonstrate the performances of DynForest both in a small dimensional context (in comparison with joint models) and in a large dimensional context (in comparison with a regression calibration method that ignores informative dropout). We also applied DynForest to (i) predict the individual probability of dementia in the elderly according to repeated measures of cognitive, functional, vascular and neuro-degeneration markers, and (ii) quantify the importance of each type of markers for the prediction of dementia. Implemented in the R package DynForest, our methodology provides a novel and appropriate solution for the prediction of events from any number of longitudinal endogenous predictors

Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach

The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic strategy. Joint models and landmark models have been proposed to compute individual dynamic predictions from repeated measures to one or two markers. However, they hardly extend to the case where the complete patient history includes much more repeated markers possibly. Our objective was thus to propose a solution for the dynamic prediction of a health event that may exploit repeated measures of a possibly large number of markers. We combined a landmark approach extended to endogenous markers history with machine learning methods adapted to survival data. Each marker trajectory is modeled using the information collected up to landmark time, and summary variables that best capture the individual trajectories are derived. These summaries and additional covariates are then included in different prediction methods. To handle a possibly large dimensional history, we rely on machine learning methods adapted to survival data, namely regularized regressions and random survival forests, to predict the event from the landmark time, and we show how they can be combined into a superlearner. Then, the performances are evaluated by cross-validation using estimators of Brier Score and the area under the Receiver Operating Characteristic curve adapted to censored data. We demonstrate in a simulation study the benefits of machine learning survival methods over standard survival models, especially in the case of numerous and/or nonlinear relationships between the predictors and the event. We then applied the methodology in two prediction contexts: a clinical context with the prediction of death for patients with primary biliary cholangitis, and a public health context with the prediction of death in the general elderly population at different ages. Our methodology, implemented in R, enables the prediction of an event using the entire longitudinal patient history, even when the number of repeated markers is large. Although introduced with mixed models for the repeated markers and methods for a single right censored time-to-event, our method can be used with any other appropriate modeling technique for the markers and can be easily extended to competing risks setting

Stat Methods Med Res

Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. In this work, we extended the competing-risk random survival forests to handle such endogenous longitudinal predictors when predicting event probabilities. The method, implemented in the R package DynForest, internally transforms the time-dependent predictors at each node of each tree into time-fixed features (using mixed models) that can then be used as splitting candidates. The final individual event probability is computed as the average of leaf-specific Aalen-Johansen estimators over the trees. Using simulations, we compared the performances of DynForest to accurately predict an event with (i) a joint modeling alternative when considering two longitudinal predictors only, and with (ii) a regression calibration method that ignores the informative truncation by the event when dealing with a large number of longitudinal predictors. Through an application in dementia research, we also illustrated how DynForest can be used to develop a dynamic prediction tool for dementia from multimodal repeated markers, and quantify the importance of each marker

Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach

The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic strategy. Joint models and landmark models have been proposed to compute individual dynamic predictions from repeated measures to one or two markers. However, they hardly extend to the case where the complete patient history includes much more repeated markers possibly. Our objective was thus to propose a solution for the dynamic prediction of a health event that may exploit repeated measures of a possibly large number of markers. We combined a landmark approach extended to endogenous markers history with machine learning methods adapted to survival data. Each marker trajectory is modeled using the information collected up to landmark time, and summary variables that best capture the individual trajectories are derived. These summaries and additional covariates are then included in different prediction methods. To handle a possibly large dimensional history, we rely on machine learning methods adapted to survival data, namely regularized regressions and random survival forests, to predict the event from the landmark time, and we show how they can be combined into a superlearner. Then, the performances are evaluated by cross-validation using estimators of Brier Score and the area under the Receiver Operating Characteristic curve adapted to censored data. We demonstrate in a simulation study the benefits of machine learning survival methods over standard survival models, especially in the case of numerous and/or nonlinear relationships between the predictors and the event. We then applied the methodology in two prediction contexts: a clinical context with the prediction of death for patients with primary biliary cholangitis, and a public health context with the prediction of death in the general elderly population at different ages. Our methodology, implemented in R, enables the prediction of an event using the entire longitudinal patient history, even when the number of repeated markers is large. Although introduced with mixed models for the repeated markers and methods for a single right censored time-to-event, our method can be used with any other appropriate modeling technique for the markers and can be easily extended to competing risks setting