Primary graft dysfunction and health-related quality of life after transplantation: The good, the bad, and the missing.
Missing data in longitudinal studies: Comparison of multiple imputation methods in a real clinical setting.
Published in Journal of evaluation in clinical practice
Missing data represent a challenge in longitudinal studies. The aim of the study is to compare the performance of the multivariate normal imputation and the fully conditional specification methods, using real data set with missing data partially completed 2 years later. The data used came from an ongoing randomized controlled trial with 5-year foll...
Imputation of Ordinal Outcomes: A Comparison of Approaches in Traumatic Brain Injury.
Published in Journal of neurotrauma
Loss to follow-up and missing outcomes data are important issues for longitudinal observational studies and clinical trials in traumatic brain injury. One popular solution to missing 6-month outcomes has been to use the last observation carried forward (LOCF). The purpose of the current study was to compare the performance of model-based single-imp...
Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health
Published in Frontiers in Public Health
Assessment of the air quality in metropolitan areas is a major challenge in environmental sciences. Issues related include the distribution of monitoring stations, their spatial range, or missing information. In Mexico City, stations have been located spanning the entire Metropolitan zone for pollutants, such as CO, NO2, O3, SO2, PM2.5, PM10, NO, N...
Contribution to model-based clustering of heterogeneous data
A random shuffle method to expand a narrow dataset and overcome the associated challenges in a clinical study: a heart f...
Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times, increasing the problem of missing data. To overcom...
Optimising an FFQ Using a Machine Learning Pipeline to teach an Efficient Nutrient Intake Predictive Model
Published in Nutrients
Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from nutritional and/or medical fields and can be validated by using other dietary monitoring techniques...
Debiasing Stochastic Gradient Descent to handle missing values
A major caveat of large scale data is their incom-pleteness. We propose an averaged stochastic gradient algorithm handling missing values in linear models. This approach has the merit to be free from the need of any data distribution modeling and to account for heterogeneous missing proportion. In both streaming and finite-sample settings, we prove...
On Modeling Missing Data of an Incomplete Design in the CFA Framework
Published in Frontiers in Psychology
The paper reports an investigation on whether valid results can be achieved in analyzing the structure of datasets although a large percentage of data is missing without replacement. Two types of confirmatory factor analysis (CFA) models were employed for this purpose: the missing data CFA model with an additional latent variable for representing t...
Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation...
Published in Journal of the American Medical Informatics Association : JAMIA
Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision sup...
