Quantifying bias due to allele misclassification in case-control studies of haplotypes

OBJECTIVES: Genotyping errors can induce biases in frequency estimates for haplotypes of single nucleotide polymorphisms (SNPs). Here, we considered the impact of SNP allele misclassification on haplotype odds ratio estimates from case-control studies of unrelated individuals. METHODS: We calculated bias analytically, using the haplotype counts expected in cases and controls under genotype misclassification. We evaluated the bias due to allele misclassification across a range of haplotype distributions using empirical haplotype frequencies within blocks of limited haplotype diversity. We also considered simple two- and three-locus haplotype distributions to understand the impact of haplotype frequency and number of SNPs on misclassification bias. RESULTS: We found that for common haplotypes (>5% frequency), realistic genotyping error rates (0.1-1% chance of miscalling an allele), and moderate relative risks (2-4), the bias was always towards the null and increases in magnitude with increasing error rate, increasing odds ratio. For common haplotypes, bias generally increased with increasing haplotype frequency, while for rare haplotypes, bias generally increased with decreasing frequency. When the chance of miscalling an allele is 0.5%, the median bias in haplotype-specific odds ratios for common haplotypes was generally small (<4% on the log odds ratio scale), but the bias for some individual haplotypes was larger (10-20%). Bias towards the null leads to a loss in power; the relative efficiency using a test statistic based upon misclassified haplotype data compared to a test based on the unobserved true haplotypes ranged from roughly 60% to 80%, and worsened with increasing haplotype frequency. CONCLUSIONS: The cumulative effect of small allele-calling errors across multiple loci can induce noticeable bias and reduce power in realistic scenarios. This has implications for the design of candidate gene association studies that utilize multi-marker haplotypes.     

Quantifying misclassification bias in cohort studies of vaccine efficacy

A simple mathematical model is presented to quantify the bias due to misclassification in prospective cohort studies of vaccine efficacy. Limitations of methods based on quantifying misclassification in 2 x 2 tables are discussed. The model is applied to data from three examples of the design and analysis of studies of pertussis vaccine efficacy.     

Ascertainment bias in family-based case-control studies

In a family-matched case-control study, a population-based sample of cases is selected from a well-defined geographic region over a fixed period of time. For diseases of adult onset, the control is generally a sibling or cousin who is matched on sex and age without regard to location of residence. Such a design can lead to biased estimates of environmental relative risk if the prevalence of an environmental risk factor varies by the geographic region from which the cases and controls are drawn. However, assuming the independence of genotype and environmental exposure, the estimators for the gene and gene-environment interaction effects are consistent. This suggests that we must use caution in interpreting parameters that estimate environmental main effects from a family-based case-control study if controls are selected from outside the case-ascertainment region.     

Reporting and selection bias in case-control studies of congenital malformations.

Retrospective studies of congenital malformations frequently rely on exposures reported by study subjects. Differential error in exposure reporting by cases and controls, which has alternatively been referred to as "recall bias" and "reporting bias," may result in a biased effect measure. Some authors have attempted to avoid reporting bias by comparing exposures between two malformed groups, rather than between cases and nonmalformed controls. This approach, however, may introduce its own bias, which we call selection bias. Both reporting bias and selection bias are shown to be algebraically equivalent to bias arising from exposure misclassification. The magnitudes of these biases are compared for a range of plausible parametric values. The case-control design is sensitive to both differential reporting and selection bias, and the choice of study design involves balancing these two sources of bias.     

Experimental quantiles of epidemiological indices in case-control studies with non-differential misclassification

The formulae for some typical epidemiological indices in case-control studies with non-differential misclassification are expressed in terms of two groups (α, β) and (γ, δ) of misclassification probabilities of exposure E and confounder C, respectively, and the initially estimated frequencies. The parameters α and β denote the probability that subjects exposed to E are classified as non-exposed and the probability that non-exposed ones will be classified as exposed, respectively. Similarly, δ and γ stand for the probability that those who have been exposed to C will be classified as non-exposed and the probability that non-exposed subjects are classified as exposed, respectively. The non-negativeness of the expressions for the ‘true’ frequencies in terms of the measured ones and the misclassification probabilities leads to the construction of feasibility regions for α, β, γ and δ. For a number of ‘acceptable’ 4-tuples (α, β, γ, δ), all of which lie inside these feasibility regions, a sequence of feasible values for an epidemiological index is determined, after employing a systematic procedure by means of a ‘searching net’ with increments Δα, Δβ, Δγ, Δδ. The procedure serves to determine the characteristics of the (experimental) cumulative distribution function for any selected epidemiological index. The final stage in exploiting the structure of feasibility regions for α, β, γ and δ is to use the cumulative distribution function to calculate quantiles for the index associated with prescribed probabilities.     

Full-likelihood approaches to misclassification of a binary exposure in matched case-control studies

We consider analysis of matched case-control studies where a binary exposure is potentially misclassified, and there may be a variety of matching ratios. The parameter of interest is the ratio of odds of case exposure to control exposure. By extending the conditional model for perfectly classified data via a random effects or Bayesian formulation, we obtain estimates and confidence intervals for the misclassified case which reduce back to standard analytic forms as the error probabilities reduce to zero. Several examples are given, highlighting different analytic phenomena. In a simulation study, using mixed matching ratios, the coverage of the intervals are found to be good, although point estimates are slightly biased on the log scale. Extensions of the basic model are given allowing for uncertainty in the knowledge of misclassification rates, and the inclusion of prior information about the parameter of interest.     

Adjusting for differential misclassification in matched case-control studies utilizing health administrative data

In epidemiological studies of secondary data sources, lack of accurate disease classifications often requires investigators to rely on diagnostic codes generated by physicians or hospital systems to identify case and control groups, resulting in a less-than-perfect assessment of the disease under investigation. Moreover, because of differences in coding practices by physicians, it is hard to determine the factors that affect the chance of an incorrectly assigned disease status. What results is a dilemma where assumptions of non-differential misclassification are questionable but, at the same time, necessary to proceed with statistical analyses. This paper develops an approach to adjust exposure-disease association estimates for disease misclassification, without the need of simplifying non-differentiality assumptions, or prior information about a complicated classification mechanism. We propose to leverage rich temporal information on disease-specific healthcare utilization to estimate each participant's probability of being a true case and to use these estimates as weights in a Bayesian analysis of matched case-control data. The approach is applied to data from a recent observational study into the early symptoms of multiple sclerosis (MS), where MS cases were identified from Canadian health administrative databases and matched to population controls that are assumed to be correctly classified. A comparison of our results with those from non-differentially adjusted analyses reveals conflicting inferences and highlights that ill-suited assumptions of non-differential misclassification can exacerbate biases in association estimates.     

Time-window bias in case-control studies: statins and lung cancer

Time-related biases in cohort studies can produce illusory "beneficial" effects of medications due entirely to an artifact of the analytic design. We describe "time-window bias" in the context of a case-control study, reporting that statin use was associated with a 45% reduction in the incidence of lung cancer. This bias results from the use of time-windows of different lengths between cases and controls to define time-dependent exposures. We illustrate the bias using a population of 365,467 patients from the United Kingdom's General Practice Research Database, including 1786 incident cases of lung cancer during 1998-2004. The case-control approach used in the published study yielded a rate ratio of lung cancer incidence of 0.62 with statin use (95% confidence interval = 0.55-0.71). A case-control approach that properly accounts for time produces a rate ratio of 0.99 (0.85-1.16)-suggesting no benefit of statins on lung cancer risk. We show analytically that the magnitude of the bias is proportional to the ratio of the unequal time-window lengths.     

An empirical investigation of exposure measurement bias and its components in case-control studies.

This study investigated bias due to misclassification of exposure in case control studies. Using a metaanalysis of data from a number of case control studies, in which a possibly misclassified exposure was validated against a more reliable one, estimates of misclassification indices and bias were obtained. The estimates were used to investigate whether misclassification indices, in particular, sensitivity and specificity and so-called quality indices, are typically nondifferential with respect to cases and controls. It is concluded that quality indices do not show any tendency to be either lower or higher in cases than controls. On the other hand, sensitivity tends to be higher in cases than controls and specificity lower. Estimates of misclassification bias may be either positive or negative and are consistent with random variation; there is little to suggest that bias is present in the studies analyzed.     

A novel form of ascertainment bias in case-control studies of cancer screening.

In case-control studies of cancer screening, some have generally admonished investigators against case definitions based on diagnosis dates because of lead-time bias. However, perhaps partly due to vagueness, the admonitions have been frequently ignored. A recurrence-time model simulates case ascertainment when diagnosis must occur within a specific calendar period. The model depends on screening test sensitivity and rate, age-specific preclinical incidence rates, and preclinical duration time and survival time distributions. For one study of sigmoidoscopic screening for colorectal cancer, when the true odds ratio is 1, its estimate is 0.50 to 0.75 under plausible assumptions. This bias can affect any observational study wherein case definition depends on diagnosis times (e.g., health-plan enrollment data). To avoid bias in observational investigations of cancer screening wherein the case definition depends on the diagnosis date, one must ensure that both screening and preclinical incidence do not occur before the case definition period.     

Accounting for error due to misclassification of exposures in case-control studies of gene-environment interaction

We consider analysis of data from an unmatched case-control study design with a binary genetic factor and a binary environmental exposure when both genetic and environmental exposures could be potentially misclassified. We devise an estimation strategy that corrects for misclassification errors and also exploits the gene-environment independence assumption. The proposed corrected point estimates and confidence intervals for misclassified data reduce back to standard analytical forms as the misclassification error rates go to zero. We illustrate the methods by simulating unmatched case-control data sets under varying levels of disease-exposure association and with different degrees of misclassification. A real data set on a case-control study of colorectal cancer where a validation subsample is available for assessing genotyping error is used to illustrate our methods.     

Confounding bias due to indication and severity in observational studies

Observational studies are subject to biases that may lead to misinterpretation of the results. This study aimed to determine the influence of omeprazole treatment on the duration of sick leave in patients with ankle sprains treated with non-steroidal anti-inflammatory drugs. We used the Ibermutuamur database. Contrary to our expectations, sick leave was longer in patients who received omeprazole than in those who did not. These findings were probably due to the influence of a bias due to confounding by severity, given that patients who received omeprazole had a worse kind of ankle sprain; however, a bias due to confounding by indication cannot be excluded. To avoid the influence of these systematic errors, biases should be monitored from the design stage to the data analysis stage.     

Selection bias in the assessment of gene-environment interaction in case-control studies

Selection bias is a common concern in epidemiologic studies, particularly case-control studies. Selection bias in the odds ratio occurs when participation depends jointly on exposure and disease status. General results for understanding when selection bias may affect studies involving gene-environment interactions have not yet been developed. In this paper, the authors show that the assessment of gene-environment interactions will not be subject to selection bias under the assumption that genotype does not influence participation conditional on exposure and disease status. This is true even when selection, including self-selection of subjects, is jointly influenced by exposure and disease and regardless of whether the genotype is related to exposure, disease, or both. The authors present an example to illustrate this concept.     

Selection bias in case-control studies using relatives as the controls

Investigators have suggested using relatives of cases as the control group when studying complex diseases thought to have a major genetic component. However, there is a concern about possible bias and we developed a model to examine the possibility of bias in the selection of relatives as the control group. Assuming the exposure-specific risks of disease remain constant over time, the results indicate that even when there is a correlation in the exposure status among relatives, selection of controls from relatives of cases does not, of itself, introduce bias in the estimate of effect.     

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