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An Application of Multivariate Survival Analysis
Published online by Cambridge University Press: 19 December 2002
Objectives: This study was designed to estimate the effects of treatment with linezolid as compared with vancomycin, on the distribution of length of stay (LOS) for hospitalized patients with methicillin-resistant staphylococcal infections. Treatment with intravenous–oral linezolid may allow some patients to be discharged earlier than would treatment with intravenous vancomycin.
Methods: The analysis is based on the intention-to-treat sample from a randomized multinational phase 3 clinical trial of 460 patients showing that the treatments had equal efficacy. Given the nature of the LOS data, some censoring, and some imbalances between treatment groups, multivariate survival analysis was indicated. Cox proportional hazards assumptions were tested and failed, and accelerated failure time models were tested for best fit. The log-logistic model was selected and used as the basis for estimating the overall treatment effect on LOS. Two methods for multivariate corrections to the survivorship functions allowed more thorough description of the treatment effect on the distribution of LOS, including multivariate-adjusted Kaplan-Meier curves.
Results: The average reduction in LOS associated with linezolid treatment, based on the log-logistic model after correction for covariate effects, was 18.1\% (p = .041) or 2.53 days at the median. This was consistent with differences at the medians of the adjusted survivorship functions, which were 2 or 3 days depending on the method used. Treatment-based differences exist at each decile of LOS and consistently favor linezolid. Estimated mean reduction in LOS due to linezolid was 1.62 days in both methods.
Conclusions: In this study sample, linezolid treatment resulted in statistically significantly shorter hospital LOS as compared with vancomycin treatment. Appropriate use of multivariate survival analysis allows better examination of the nature of the treatment effect on LOS, which may be important for economic analysis.