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Relative risk estimated from the ratio of two median unbiased estimates.

ABSTRACT: Clinical trials often include binary endpoints. In some cases, no successes are observed and the usual large-sample estimates of relative risk are undefined. This paper proposes an estimator for relative risk based on the median unbiased estimator. The proposed relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. To facilitate the use of the estimator, a deterministic bootstrap confidence interval is also proposed, and a SAS MACRO is available to perform the necessary calculations. An ongoing randomized clinical trial motivated the development of the estimator and is used to illustrate the approach.

SUBMITTER: Carter RE

PROVIDER: S-EPMC2918902 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Relative risk estimated from the ratio of two median unbiased estimates.

Carter Rickey E RE Lin Yan Y Lipsitz Stuart R SR Newcombe Robert G RG Hermayer Kathie L KL

Journal of the Royal Statistical Society. Series C, Applied statistics 20100801 4

Clinical trials often include binary endpoints. In some cases, no successes are observed and the usual large-sample estimates of relative risk are undefined. This paper proposes an estimator for relative risk based on the median unbiased estimator. The proposed relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. To facilitate the use of the estimator, a deterministic bootstrap confidence interval is also proposed, and a SAS MACRO is availa ...[more]

PMID: 20711511

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Project description:BackgroundThe 10th revision of the International Classification of Diseases (ICD) represents a major change in the ICD system. This paper investigates the impact on relative risk estimates of inconsistencies in outcome classification between ICD-9 and ICD-10, including scenarios in which occupational exposure levels are correlated with year of death (and therefore with the ICD revision in effect at death). The setting of interest is a cohort mortality study in which follow up spans the periods during which ICD-9 and ICD-10 were in effect. The relative risk estimate obtained when death certificates are coded to the ICD revision in effect at time of death is compared to the relative risk estimate that would be obtained if all death certificates were coded to a consistent ICD revision (that is, ICD-10). The ratio of these relative risks is referred to as the coefficient of bias.MethodsSimple equations relate the coefficient of bias to the sensitivity and specificity of the classification of decedents into categories of cause of death via ICD-9 (treating classifications based upon ICD-10 as the standard). Bridge coded mortality data for 2,296,922 decedents (that is, death certificates coded to ICD-9 and ICD-10) are used to derive estimates of sensitivity and specificity by category of cause of death. Numerical examples illustrate the application of these equations.ResultsEstimates of the sensitivity of classification of decedents into categories of death defined by ICD-9 ranged from 0.26-1.00. Specificity was above 0.98 for all categories of cause of death. Numerical examples illustrate that inconsistencies in outcome classification between ICD-9 and ICD-10 may have substantial impact on relative risk estimates if there is a strong relation between exposure status and the proportion of deaths coded to a given ICD revision.ConclusionsFor analyses of mortality outcomes that exhibit poor comparability between ICD-9 and -10, it may be prudent to recode cause of death information to a standard ICD revision in order to avoid bias that can occur when exposures are correlated with the proportion of deaths coded to a given ICD revision.

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Relative risk estimated from the ratio of two median unbiased estimates.

Publications

Relative risk estimated from the ratio of two median unbiased estimates.

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