Survival Analysis & Maximum Likelihood Techniques as Applied to Physiological Model.

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Survival Analysis & Maximum Likelihood Techniques as Applied to Physiological Model.

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dc.contributor.author Weathersby, PK
dc.contributor.author Gerth, WA
dc.date.accessioned 2009-04-26T23:17:17Z
dc.date.available 2009-04-26T23:17:17Z
dc.date.issued 2002
dc.identifier.citation Weathersby PK, Gerth WA (eds). Survival Analysis & Maximum Likelihood Techniques as Applied to Physiological Model. 51st Undersea and Hyperbaric Medical Society Workshop. UHMS Publication Number WD650. Kensington: Undersea and Hyperbaric Medical Society; 2002; 176 pages. en
dc.identifier.isbn 0-930406-19-2
dc.identifier.other WD650
dc.identifier.uri http://archive.rubicon-foundation.org/8027
dc.description Copyright Undersea and Hyperbaric Medical Society. en
dc.description.abstract Preface - Wayne A. Gerth: In 1984, Dr. Paul Weathersby, Dr. Lou Homer and Dr. Edward Flynn published a seminal paper in which they introduced survival analysis into the study of decompression sickness (DCS). The approach they outlined, and continued to develop with colleagues Shalini Survanshi, Erich Parker and others at the Naval Medical Research Institute (NMRI) in a subsequent series of published papers and reports, gave new direction to the way we reconcile theory with experience in this field. First, it explicitly recognizes that a given physiological outcome is not an inevitable result of a particular environmental history, but instead is only a probabilistic function of that history. Second, the approach includes rigorous means to make one or more candidate expressions of that probabilistic function each provide its best possible, or optimum, correlation of observed outcomes in actual experience. The optimized models that emerge from such work are consequently quantitative generalizations of that experience, which renew the analyst's focus on the data he or she has in hand. Finally, the approach allows quantitative assessments to be made of how well a given model accounts for observed behavior in specific sets of data, so that the best of a collection of candidate models can be selected. This selection process allows models that are more complex than the data warrants to be identified and deselected, helping to separate necessary theoretical complexity from speculation. Workers interested in other undersea and aerospace physiological problems soon recognized the analytic advantages of survival modeling. Adoption of the techniques in these areas has lead to development of application-specific functions describing responses to ever more complex patterns in the independent variables, and to use of meta-analytic approaches to build data sets with analytically tractable numbers of occurrences of the adverse events of interest. As these applications have ventured farther from those described in standard statistical tests, there has been a growing need to pause and distill their underlying principles, critically evaluate their merits, and outline directions for further development and application. The present Proceedings of the Workshop on Survival Analysis and Maximum Likelihood Techniques as Applied to Physiological Modeling is both an attempt to meet that need, and a salute to the NMRI workers who originally introduced us to this promising line of inquiry. TABLE OF CONTENTS: Preface - Wayne A. Gerth; Welcome - Paul K. Weathersby; Workshop Origin - Edward D. Thalmann; Overview of Survival Functions and Methodology - Wayne A. Gerth; NMRl Models of CNS Oxygen Toxicity Modeling - Paul K. Weathersby; Diver Tolerance to Breathing Resistance - John Clarke; A Log-Logistic Survival Model Applied to Hypobaric Decompression Sickness - Johnny Conkin; Testing of Hypotheses About Basic Mechanisms with Risk Functions - Hugh D. Van Liew; Survival Models for Altitude Decompression Sickness - Nandini Kannan; Multinomial Bubble Score Model - Peter Tikuisis, Keith A. Gault; Probabilistic Models of DCS During Flying After Diving: Motivation for Mechanism - Wayne A. Gerth; Improving on a "Good" Model - Erich C. Parker, Shalini S. Survanshi, Paul K. Weathersby; Meta Analysis of Diver Decompression Data - Paul K. Weathersby, Diana A. Temple, Erich C. Parker; Cold Exposure Survival Model - Peter Tikuisis; Critique of Methodology - Frank E. Harrell, Louis D. Homer; Promising Approaches to Experimental Design - Louis D. Homer; Directions in Statistical Methodology for Multivariable Predictive Modeling - Frank E. Harrell, Jr; General Discussion; Close - Wayne A. Gerth; List of Participants en
dc.description.sponsorship UHMS, DAN, US NAVY, US Air Force, NOAA en
dc.format.extent 83159395 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Undersea and Hyperbaric Medical Society en
dc.subject diving en
dc.subject model en
dc.subject Survival Analysis en
dc.subject Maximum Likelihood Techniques en
dc.subject Physiological Model en
dc.subject decompression sickness en
dc.subject oxygen toxicity en
dc.subject probabilistic model en
dc.subject statistics en
dc.subject Breathing Resistance en
dc.subject Altitude en
dc.subject Log-Logistic Survival Model en
dc.subject Flying After Diving en
dc.subject Cold Exposure Survival Model en
dc.subject Methodology en
dc.subject Predictive Modeling en
dc.subject Experimental Design en
dc.title Survival Analysis & Maximum Likelihood Techniques as Applied to Physiological Model. en
dc.type Article en

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    This is a collection of the published workshops held by the Undersea and Hyperbaric Medical Society (UHMS).

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