Imputing Partial Status and Estimating Incidence Rate in an Illness-death Model with Application to a Phase IV Cancer Trial
Year: 2020
Type: preprint
Abstract: BackgroundPhase IV clinical trials are designed to monitor the long-term toxic effects of drugs in cancer survivors. Evaluations to study the long-term effects of the cancer treatment are often made with cross-sectional surveys. This leads to interval censored data since the exact time of the onset of toxicity is not known. In addition to finding prognostic factors for log-term survival outcome, estimating and comparing the cumulative incidence rates for adverse outcomes of interest for interval censored data is also desired. However, the analysis of such data is further complicated by many issues, such as incomplete data, competing risks and selection bias. For example, one such study was designed by Hudson et al. to study the effect of anthracyclines exposure, received as part of treatment for childhood cancer, to cardiotoxicity. Rai et al. had utilized a parametric approach for assessing the effect of anthracycline on the cumulative incidence of cardiotoxicity but excluded the patients with missing information on the parameters used for assessing cardiotoxicity.MethodsIn this paper our focus is on imputing the missing data and then using the current status regression methods, previously described in Rai et al. for estimating and comparing cumulative incidence rates in an illness-death/failure model.ResultsWe undertook a comprehensive simulation study to evaluate the performance of our imputation approach and applied it to a Phase IV clinical trial to evaluate the effect of anthracycline exposure on long-term cardiotoxicity in childhood cancer survivors, which had missing cardiotoxicity information. ConclusionsOur simulations suggest that the results obtained by imputing the missing values using regression methods are significantly more efficient than those obtained without imputation. The proposed approach is easy to implement, and we demonstrate its usefulness by applying it to the data reported in Rai et al. and compare the results reported there to our approach that utilizes imputation.
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Funders: University of Louisville, American Lebanese Syrian Associated Charities, Kentucky Council on Postsecondary Education, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences
Awards: G101836233, Functional Microbiomics, Inflammation and Pathogenicity, Center for Excellence in Diabetes and Obesity Research, G4029480128, G5673835866, G7890109843, G865138149, G9000020041, G9000086855, G9000218919, G9000225631, G9000334059, Center for Cancer Immunology and Immunotherapy (CCII), Mechanism for arsenic induced carcinogenesis, Hepatobiology and Toxicology COBRE, American Heart Association Tobacco Center for Regulatory Science (A-TRAC) 2.0, G989631422
OpenAlex ID: W3118960125