Lung carcinogenesis modeling: Resampling and simulation approach to model fitting, validation, and prediction

Because of serious health implications, lung cancer is the leading cancer killer for both men and women. It is well known that smoking is the major risk factor for lung cancer. I propose to use a two-stage clonal expansion (TSCE) model to evaluate the effects of smoking on initiation and promotion of lung carcinogenesis. The TSCE model is traditionally fit to prospective cohort data. A new method has been developed that allows reconstruction of cohort data from the combination of risk factor data from a case-control study, and tabled incidence/mortality rate data. A simulation study of the method shows that it is accurate in estimating the parameters of the TSCE model. The method is then applied to fit a TSCE model based on smoking history. The fitted model is then validated in two ways. First the model is used to predict lung cancer deaths in the non-asbestos exposed control arm of the CARET study, where the model predicts 366.8 lung cancer deaths while there were 364 observed. Second, the model is used to simulate LC mortality in the US population and reasonably reproduced observed US mortality rates. The model is also applied to a study of CT screening for lung cancer. The study is a single arm CT screening study lacking a control arm for comparison. The model is used to simulate LC mortality in the absence of screening to serve as a surrogate control arm for comparison. Based on the model there is a statistically significant mortality reduction of 36% due to CT screening

Lunar Orbit Mission Risk Analysis Using the Integrated Medical Model

No abstract availabl

Mass and Volume Optimization of Space Flight Medical Kits

Resource allocation is a critical aspect of space mission planning. All resources, including medical resources, are subject to a number of mission constraints such a maximum mass and volume. However, unlike many resources, there is often limited understanding in how to optimize medical resources for a mission. The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulates outcomes and describes the impact of medical events in terms of lost crew time, medical resource usage, and the potential for medically required evacuation. Previously published work describes an approach that uses the IMM to generate optimized medical kits that maximize benefit to the crew subject to mass and volume constraints. We improve upon the results obtained previously and extend our approach to minimize mass and volume while meeting some benefit threshold. METHODS We frame the medical kit optimization problem as a modified knapsack problem and implement an algorithm utilizing dynamic programming. Using this algorithm, optimized medical kits were generated for 3 mission scenarios with the goal of minimizing the medical kit mass and volume for a specified likelihood of evacuation or Crew Health Index (CHI) threshold. The algorithm was expanded to generate medical kits that maximize likelihood of evacuation or CHI subject to mass and volume constraints. RESULTS AND CONCLUSIONS In maximizing benefit to crew health subject to certain constraints, our algorithm generates medical kits that more closely resemble the unlimited-resource scenario than previous approaches which leverage medical risk information generated by the IMM. Our work here demonstrates that this algorithm provides an efficient and effective means to objectively allocate medical resources for spaceflight missions and provides an effective means of addressing tradeoffs in medical resource allocations and crew mission success parameters

Shoulder Injury Incidence Rates in NASA Astronauts

Evaluation of the astronaut shoulder injury rates began with an operational concern at the Neutral Buoyancy Laboratory (NBL) during Extravehicular Activity (EVA) training. An astronaut suffered a shoulder injury during an NBL training run and commented that it was possibly due to a hardware issue. During the subsequent investigation, questions arose regarding the rate of shoulder injuries in recent years and over the entire history of the astronaut corps

Statistics Clinic

Do you have elevated pvalues? Is the data analysis process getting you down? Do you experience anxiety when you need to respond to criticism of statistical methods in your manuscript? You may be suffering from Insufficient Statistical Support Syndrome (ISSS). For symptomatic relief of ISSS, come for a free consultation with JSC biostatisticians at our help desk during the poster sessions at the HRP Investigators Workshop. Get answers to common questions about sample size, missing data, multiple testing, when to trust the results of your analyses and more. Side effects may include sudden loss of statistics anxiety, improved interpretation of your data, and increased confidence in your results

Evidence Based Medicine in Space Flight: Evaluation of Inflight Vision Data for Operational Decision-Making

No abstract availabl