Kumar K V, Powell M R
Environmental Physiology Laboratory, KRUG Life Sciences, Houston, TX 77058.
Aviat Space Environ Med. 1994 Jul;65(7):661-5.
Several approaches have been used for modeling the incidence of decompression sickness (DCS) such as Hill's dose-response and logistic regression. Most of these methods do not include the time-to-onset information in the model. Survival analysis (failure time analysis) is appropriate when the time to onset of an event is of interest. The applicability of survival analysis for modeling the risk of DCS is illustrated by using data obtained from hypobaric chamber exposures simulating extravehicular activities (n = 426). Univariate analysis of incidence-free survival proportions were obtained for Doppler-detectable circulating microbubbles (CMB), symptoms of DCS and test aborts. A log-linear failure time regression model with 360-min half-time tissue ratio (TR) as covariate was constructed, and estimated probabilities for various TR values were calculated. Further regression analysis by including CMB status in this model showed significant improvement (p < 0.05) in the estimation of DCS over the previous model. Since DCS is dependent on the exposure pressure as well as the duration of exposure, we recommend the use of survival analysis for modeling the risk of DCS.
已经采用了几种方法来对减压病（DCS）的发病率进行建模，例如希尔剂量反应模型和逻辑回归。这些方法中的大多数在模型中未纳入发病时间信息。当关注事件的发病时间时，生存分析（失效时间分析）是合适的。通过使用从模拟舱外活动的低压舱暴露获得的数据（n = 426），说明了生存分析在对DCS风险进行建模中的适用性。对多普勒可检测到的循环微泡（CMB）、DCS症状和试验中止进行了无发病率生存比例的单变量分析。构建了一个以360分钟半衰期组织比（TR）作为协变量的对数线性失效时间回归模型，并计算了各种TR值的估计概率。在该模型中纳入CMB状态进行的进一步回归分析表明，与之前的模型相比，DCS估计有显著改善（p < 0.05）。由于DCS取决于暴露压力以及暴露持续时间，我们建议使用生存分析来对DCS风险进行建模。
