alexa Parametric mean survival time analysis in gastric cancer patients.
Mathematics

Mathematics

Journal of Biometrics & Biostatistics

Author(s): Maetani S, Nakajima T, Nishikawa T

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Abstract BACKGROUND: The mean survival time (MS) has acquired increasing importance as an outcome indicator for patient care and technology assessment. The authors use lifelong follow-up data from gastric cancer patients to study whether the MS is predictable from 5-year follow-up information based on 2 parametric models. METHODS: The authors used 3597 gastric cancer patients operated on between 1950 and 1969 to create 50 groups. For each group, the disease-related survival curve (DRSC) was estimated from the 5-year follow-up data using the Boag model. The MS for the group was then estimated by combining the DRSC with the survival curve for the age and sex-matched contemporaries (control group) based on the competing risk model. Alternatively, it was estimated by using the DRSC and the MS for the control group (the survival limit model). These predicted MS values were compared with the full follow-up observations. RESULTS: Although individual prediction errors varied depending on the group size (63 to 3597 patients) and the length of MS (0.3 to 20.2 years), the mean prediction errors were reasonably small; the survival limit model overestimated MS by 4.7\% (95\% confidence interval [CI], 1.6 to 7.8) and the competing risk model by 3.2\% (95\% CI, 0.1 to 6.5). CONCLUSIONS: MS for gastric cancer patients is parametrically predictable from 5-year follow-up data. This analysis should be applicable to other diseases showing log-normal failure time distributions. This article was published in Med Decis Making and referenced in Journal of Biometrics & Biostatistics

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