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. 2015 Jul 28:15:54.
doi: 10.1186/s12874-015-0048-4.

Prostate cancer: net survival and cause-specific survival rates after multiple imputation

Adeline Morisot  1 , Faïza Bessaoud  2 , Paul Landais  3 , Xavier Rébillard  4 , Brigitte Trétarre  2 , Jean-Pierre Daurès  3
Affiliations

Prostate cancer: net survival and cause-specific survival rates after multiple imputation

Adeline Morisot et al. BMC Med Res Methodol. .

Abstract

Background: Estimations of survival rates are diverse and the choice of the appropriate method depends on the context. Given the increasing interest in multiple imputation methods, we explored the interest of a multiple imputation approach in the estimation of cause-specific survival, when a subset of causes of death was observed.

Methods: By using European Randomized Study of Screening for Prostate Cancer (ERSPC), 20 multiply imputed datasets were created and analyzed with a Multivariate Imputation by Chained Equation (MICE) algorithm. Then, cause-specific survival was estimated on each dataset with two methods: Kaplan-Meier and competing risks. The two pooled cause-specific survival and confidence intervals were obtained using Rubin's rules after complementary log-log transformation. Net survival was estimated using Pohar-Perme's estimator and was compared to pooled cause-specific survival. Finally, a sensitivity analysis was performed to test the robustness of our constructed multiple imputation model.

Results: Cause-specific survival performed better than net survival, since this latter exceeded 100 % for almost the first 2 years of follow-up and after 9 years whereas the cause-specific survival decreased slowly and than stabilized at around 94 % at 9 years. Sensibility study results were satisfactory.

Conclusions: On our basis of prostate cancer data, the results obtained by cause-specific survival after multiple imputation appeared to be better and more realistic than those obtained using net survival.

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Figures

Fig. 1
Fig. 1
Overall, net, and pooled cause-specific survival curves and their confidence intervals
Fig. 2
Fig. 2
True cause-specific, pooled cause specific survival rates and their confidence intervals using the first sensibility method
Fig. 3
Fig. 3
True cause-specific, pooled cause-specific survival rates and their confidence intervals using the second sensibility method
Fig. 4
Fig. 4
True cause-specific and pooled cause-specific survival rates and their confidence intervals using the third sensibility method
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