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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.

Project description:BACKGROUND:Generally, populations differ in terms of environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in two populations is known as the additive genetic correlation between populations and thus, can differ from 1. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect the bias of the estimated genetic correlation. We simulated two populations that were separated by 50 generations and differed in LD pattern between markers and causal loci, as measured by the LD-statistic r. We used a high marker density to represent a high consistency of LD between populations, and lower marker densities to represent situations with a lower consistency of LD between populations. Markers and causal loci were selected to have either similar or different allele frequencies in the two populations. RESULTS:Our results show that genetic correlations were underestimated only slightly when the difference in allele frequencies between the two populations was similar for the markers and the causal loci. A lower marker density, representing a lower consistency of LD between populations, had only a minor effect on the underestimation of the genetic correlation. When the difference in allele frequencies between the two populations was not similar for markers and causal loci, genetic correlations were severely underestimated. This bias occurred because the markers did not predict accurately the relationships at causal loci. CONCLUSIONS:For an unbiased estimation of the genetic correlation between populations, the markers should accurately predict the relationships at the causal loci. To achieve this, it is essential that the difference in allele frequencies between populations is similar for markers and causal loci. Our results show that differences in LD phase between causal loci and markers across populations have little effect on the estimated genetic correlation.

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

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

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