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Semiparametric estimation in the proportional hazard model accounting for a misclassified cause of failure.

ABSTRACT: Misclassified causes of failures are a common phenomenon in competing risks survival data such as cancer mortality. We propose new estimating equations for a semiparametric proportional hazards (PH) model with misattributed causes of failures. Unlike other methods, the estimator does not require any parametric assumptions on baseline cause-specific hazard rates. It is shown that the estimators for regression coefficients are consistent and asymptotically normal. Simulation results support the theoretical analysis in finite samples. The methods are applied to analyze prostate cancer survival.

SUBMITTER: Ha J

PROVIDER: S-EPMC4689683 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Semiparametric estimation in the proportional hazard model accounting for a misclassified cause of failure.

Ha Jinkyung J Tsodikov Alexander A

Biometrics 20150623 4

Misclassified causes of failures are a common phenomenon in competing risks survival data such as cancer mortality. We propose new estimating equations for a semiparametric proportional hazards (PH) model with misattributed causes of failures. Unlike other methods, the estimator does not require any parametric assumptions on baseline cause-specific hazard rates. It is shown that the estimators for regression coefficients are consistent and asymptotically normal. Simulation results support the th ...[more]

PMID: 26102346

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Project description:IntroductionMisclassification of HIV deaths can substantially diminish the usefulness of cause of death data for decision-making. In this study, we describe the methods developed by the Global Burden of Disease Study to account for the misclassified cause of death data from vital registration systems for estimating HIV mortality in 132 countries and territories.MethodsThe cause of death data were obtained from the World Health Organization Mortality Database and official country-specific mortality databases. We implemented two steps to adjust the raw cause of death data: (1) redistributing garbage codes to underlying causes of death, including HIV/AIDS by applying methods, such as analysis of multiple cause data and proportional redistribution, and (2) reassigning HIV deaths misclassified as other causes to HIV/AIDS by examining the age patterns of underlying causes in location and years with and without HIV epidemics.ResultsIn 132 countries, during the period from 1990 to 2018, 1,848,761 deaths were reported as caused by HIV/AIDS. After garbage code redistribution in these 132 countries, this number increased to 4,165,015 deaths. An additional 1,944,291 deaths were added through correction of HIV deaths misclassified as other causes in 44 countries. The proportion of HIV deaths derived from garbage code redistribution decreased over time, from 0.4 in 1990 to 0.1 in 2018. The proportion of deaths derived from HIV misclassification correction peaked at 0.4 in 2006 and declined afterwards to 0.08 in 2018. The greatest contributors to garbage code redistribution were "immunodeficiency antibody" (ICD 9: 279-279.1; ICD 10: D80-D80.9) and "immunodeficiency other" (ICD 9: 279, 279.5-279.9; ICD 10: D83-D84.9, D89, D89.8-D89.9), which together contributed 77% of all redistributed deaths at their peak in 1995. Respiratory tuberculosis (ICD 9: 010-012.9; ICD 10: A10-A14, A15-A16.9) contributed the greatest proportion of all HIV misclassified deaths (25-62% per year) over the most years.ConclusionsCorrecting for miscoding and misclassification of cause of death data can enhance the utility of the data for analyzing trends in HIV mortality and tracking progress toward the Sustainable Development Goal targets.

Dataset Information

Semiparametric estimation in the proportional hazard model accounting for a misclassified cause of failure.

Publications

Semiparametric estimation in the proportional hazard model accounting for a misclassified cause of failure.

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