Engineering topics
Publications and source records attributed to Minard, Charles G..
We propose using meta-analytic methods to combine summary measures across space agencies: (1) Non-attributable data (2) Avoids problems with sharing health related data (3) Unpublished data
The Integrated Medical Model (IMM) uses Monte Carlo methodologies to predict the occurrence of medical events, their mitigation, and the resources required during space flight. The model includes two modules that utilize output from a single model simulation to identify an optimized medical kit for a specified mission scenario. This poster describes two flexible optimization routines built into SAS 9.1. The first routine utilizes a systematic process of elimination to maximize (or minimize) outcomes subject to attribute constraints. The second routine uses a search and mutate approach to minimize medical kit attributes given a set of outcome constraints. There are currently 273 unique resources identified that are used to treat at least one of 83 medical conditions currently in the model.
The Integrated Medical Model (IMM) utilizes Monte Carlo methodologies to predict the occurrence of medical events, utilization of resources, and clinical outcomes during space flight. Real-world data may be used to demonstrate the accuracy of the model. For this analysis, IMM predictions were compared to data from historical shuttle missions, not yet included as model source input. Initial goodness of fit test-ing on International Space Station data suggests that the IMM may overestimate the number of occurrences for three of the 83 medical conditions in the model. The IMM did not underestimate the occurrence of any medical condition. Initial comparisons with shuttle data demonstrate the importance of understanding crew preference (i.e., preferred analgesic) for accurately predicting the utilization of re-sources. The initial analysis demonstrates the validity of the IMM for its intended use and highlights areas for improvement.
The Integrated Medical Model (IMM) is a decision support tool that is useful to space flight mission planners and medical system designers in assessing risks and optimizing medical systems. The IMM employs an evidence-based, probabilistic risk assessment (PRA) approach within the operational constraints of space flight.
Space flight is a very unique occupational exposure with potential hazards that are not fully understood. A limited number of individuals have experienced the exposures incurred during space flight, and epidemiologic research would benefit from shared information across space agencies. However, data sharing can be problematic due to agency protection policies for personally identifiable information as well as medical records. Compliance with these protocols in the astronaut population is particularly difficult given the small, high-profile population under study. Creativity in combining data is necessary in order to overcome these difficulties and improve statistical power in research. This study presents methods in meta-analysis that may be used to combine non-attributable data across space agencies so that meaningful conclusions may be drawn about study interests. Methods for combining epidemiologic data across space agencies are presented, and the processes are demonstrated using life-time mortality data in U.S. astronauts and Russian cosmonauts. This proof of concept was found to be an acceptable way of sharing data across agencies, and will be used in the future as more relevant research interests are identified.