An Information System Approach for Handling Missing Data in Collaborative Medical Research.

As more and more data are shared, the possible distance between data collection and data analysis has increased. This makes missing data handling more difficult, because of the possible loss of information between collection and analysis. We wondered how information about missing data could be shared in order to improve missing data handling. No answer could be found in the literature. Therefore, we conducted an empirical study over three large medical datasets. We observed a diversity of practices and opportunities to improve them. We designed a way of transmitting information about missing data, easy to implement, based on what we empirically learned. Our propositions have been implemented in a large scale medical research project, giving the opportunity of a second empirical study for future works

Logistics Projects: How to Assess the Right System? The Case of RFID Solutions in Healthcare

RFID is very promising for the healthcare industry. However, RFID solutions and contexts of use vary a lot from one place to another and as a consequence its impact on performance can vary as well. Moreover, it can be complex to assess its potential benefits relatively to other solutions before its implementation. We analyze a real project which gathers 18 partners working together to implement RFID solutions and/or datamatrix in health related processes in hospitals. We show the complexity of assessing such a project: diversity of the domains involved, interdependencies between them and impact of representation of the project on its assessment. Instead of providing an evaluation tool ready to use, we suggest a meta-evaluation tool which determines what the appropriate scope and abstraction level are to represent and grasp what has to be assessed. Practitioners could then use it to design their own customized evaluation tool on the field

Du morcellement de l'espace de recherche en planification d'actions

Diverses approches en plani cation d actions consistent à produire un plan solution après avoir construit à partir du problème de plani cation, puis parcouru, un espace de recherche plus ou moins morcelé en sous-espaces. En formalisant cette notion de morcellement de l'espace de recherche, nous montrons que des approches en apparence très différentes ne se distinguent en fait que du morcellement qu elles mettent en œuvre. Nous montrons aussi que la diversité des morcellements est largement sous-exploitée par les plani cateurs actuels, ouvrant ainsi la voie à la conception de nouvelles approches, en créant de nouveaux morcellements de l'espace de recherche. Pour valider expérimentalement notre propos, nous avons développé un plani cateur : TokenPlan. Nous avons aussi conçu et expérimenté un nouveau morcellement de l espace de recherche qui permet d obtenir une approche hybride entre un algorithme Branch and Bound et un plani cateur disjonctif.TOULOUSE-ISAE (315552318) / SudocSudocFranceF

Radio frequency identification: a case for health care

The use of RFID tags in healthcare applications has been gaining momentum over the past decade. This is partly due to recent advances in information technology and the need to reduce errors while simultaneously improving the efficiency of the system. We, at the RFID European Lab, have been studying various aspects of RFID implementations in healthcare environment over the past several years. The potential for RFID implementations in healthcare environment is enormous. We consider several such opportunities and identify possible extensions

Pooling individual participant data from randomized controlled trials: Exploring potential loss of information

Background: Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results. Methods: Data were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those 1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss. Results: The R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1. Conclusions: Large, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results

Pooling individual participant data from randomized controlled trials: Exploring potential loss of information.

BACKGROUND: Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results. METHODS: Data were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those 1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss. RESULTS: The R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1. CONCLUSIONS: Large, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results