Firth-corrected joint model via modified EM algorithm reduces bias from separation in categorical covariates for longitudinal-survival data.
Biostatistics , volume =
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npde methods extend to joint longitudinal-TTE models via censored data imputation and a combined test that maintains ~5% type I error while detecting misspecifications in prostate cancer simulations.
citing papers explorer
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Adressing Separation: A Firth-corrected Joint Model for Longitudinal and Time-to-event Data with an Application on Dropout from Vocational Training
Firth-corrected joint model via modified EM algorithm reduces bias from separation in categorical covariates for longitudinal-survival data.
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Evaluation of the npde performance for the evaluation of joint model with longitudinal and TTE data: an application in metastatic hormono-resistant prostate cancer
npde methods extend to joint longitudinal-TTE models via censored data imputation and a combined test that maintains ~5% type I error while detecting misspecifications in prostate cancer simulations.