Rationale: Idiopathic pulmonary fibrosis (IPF) is characterized by progressive dyspnea, impaired gas exchange, and ultimate mortality. Objectives: To test the hypothesis that maximal oxygen uptake during cardiopulmonary exercise testing at baseline and with short-term longitudinal measures would predict mortality in patients with idiopathic pulmonary fibrosis. Methods: Data from 117 patients with IPF and longitudinal cardiopulmonary exercise tests were examined retrospectively. Survival was calculated from the date of the first cardiopulmonary exercise test. Measurements and Main Results: Patients with baseline maximal oxygen uptake less than 8.3 ml/kg/min had an increased risk of death (n 5 8; hazard ratio, 3.24; 95% confidence interval, 1.10-9.56; P 5 0.03) after adjusting for age, gender, smoking status, baseline forced vital capacity, and baseline diffusion capacity for carbon monoxide. We were unable to define a unit change in maximal oxygen uptake that predicted survival in our cohort. Conclusions: We conclude that a threshold maximal oxygen uptake of 8.3 ml/kg/min during cardiopulmonary exercise testing at baseline adds prognostic information for patients with IPF.
Most multiple imputation (MI) methods for censored survival data either ignore patient characteristics when imputing a likely event time, or place quite restrictive modeling assumptions on the survival distributions used for imputation. In this research, we propose a robust MI approach that directly imputes restricted lifetimes over the study period based on a model of the mean restricted life as a linear function of covariates. This method has the advantages of retaining patient characteristics when making imputation choices through the restricted mean parameters and does not make assumptions on the shapes of hazards or survival functions. Simulation results show that our method outperforms its closest competitor for modeling restricted mean lifetimes in terms of bias and efficiency in both independent censoring and dependent censoring scenarios. Survival estimates of restricted lifetime model parameters and marginal survival estimates regain much of the precision lost due to censoring. The proposed method is also much less subject to dependent censoring bias captured by covariates in the restricted mean model. This particular feature is observed in a full statistical analysis conducted in the context of the International Breast Cancer Study Group Ludwig Trial V using the proposed methodology.
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