Methodology to correct for differential misclassification.

W D Flanders; C D Drews; A S Kosinski

doi:10.1097/00001648-199503000-00011

Home/Archive/PubMed

PubMed · 7742401

Methodology to correct for differential misclassification.

W D Flanders·C D Drews·A S Kosinski

View original source ↗Export metadata ↓

Abstract

Misclassification of exposure is a serious problem in epidemiology. Methods for addressing misclassification are available, but most are based on limiting assumptions such as availability of a "gold standard" measure of true exposure, or availability of two tests of exposure whose performance is nondifferential. In this paper, we discuss a method that allows the investigator to correct for differential misclassification in case-control studies. Our method only requires two potentially imperfect tests for measuring exposure. Importantly, the sensitivity and specificity of each test when applied to cases may differ from the sensitivity and specificity when applied to controls. The approach does require two subgroups of cases, such that each test's sensitivity and specificity is the same across these subgroups and requires analogous subgroups for controls. We exemplify our approach in several ways, using hypothetical data, using data from a case-control study of birth defects and service in Vietnam, and by a small Monte Carlo study. Finally, we discuss limitations of the method.

Explore related subjects

Bias Case-Control Studies Congenital Abnormalities Environmental Exposure Humans Military Personnel Models, Statistical Monte Carlo Method Odds Ratio Sensitivity and Specificity Vietnam Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

W D Flanders, C D Drews, A S Kosinski. 1995. Methodology to correct for differential misclassification.. https://doi.org/10.1097/00001648-199503000-00011

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Assessment of blinding in pharmacotherapy and noninvasive neuromodulation randomized controlled trials for neuropathic pain in adults.

In randomized controlled trials (RCTs), study participants and research personnel are often blinded to minimize biases related to knowing treatment allocation. To determine if blinding was effective, participants may be asked which treatment they believe they received ("treatment guess"). This descriptive review characterized blinding assessment (BA) reporting in pharmacotherapy and neuromodulation neuropathic pain RCTs. Of 288 papers, 36 (12.5%) reported a BA. One paper reported the results of 2 studies, so in total 37 studies with a BA were assessed. Of these, 19 were crossover, 17 parallel, and 1 partial crossover in design. All 37 studies assessed participant blinding, and 10 also assessed investigator blinding. Approximately 27% included an "unsure" answer option for treatment guess, and 38% asked the reason for the guess. There were no clear patterns in BA reporting across time nor based on treatment type. Seventeen trials provided sufficient data to calculate Bang Blinding Index (BI) to determine blinding success. Participants remained blinded (BI = 0 ± 0.2) in 10/17 placebo and 10/17 treatment arms, 6 placebo and 5 treatment arms had a BI > 0.2 suggesting possible unblinding, whereas 1 placebo and 2 treatment arms had a BI < -0.2 suggesting misinformed guessing. Overall, we found that BAs are done in a minority of published neuropathic pain trials and with variable methodology. Given the importance of minimizing risk of bias because of treatment unblinding, future studies should consider including BAs, and further consensus building is necessary to determine if and how BAs should be conducted and interpreted in analgesic clinical trials.

Bias↗

PubMed2004

The impact of population heterogeneity on risk estimation in genetic counseling.

BACKGROUND: Genetic counseling has been an important tool for evaluating and communicating disease susceptibility for decades, and it has been applied to predict risks for a wide class of hereditary disorders. Most diseases are complex in nature and are affected by multiple genes and environmental conditions; it is highly likely that DNA tests alone do not define all the genetic factors responsible for a disease, so that persons classified into the same risk group by DNA testing actually could have different disease susceptibilities. Ignorance of population heterogeneity may lead to biased risk estimates, whereas additional information on population heterogeneity may improve the precision of such estimates. METHODS: Although DNA tests are widely used, few studies have investigated the accuracy of the predicted risks. We examined the impact of population heterogeneity on predicted disease risks by simulation of three different heterogeneity scenarios and studied the precision and accuracy of the risks estimated from a logistic regression model that ignored population heterogeneity. Moreover, we also incorporated information about population heterogeneity into our original model and investigated the resulting improvement in the accuracy of risk estimation. RESULTS: We found that heterogeneity in one or more categories could lead to biased estimates not only in the "contaminated" categories but also in other homogeneous categories. Incorporating information about population heterogeneity into the original model greatly improved the accuracy of risk estimation. CONCLUSIONS: Our findings imply that without thorough knowledge about genetic basis of the disease, risks estimated from DNA tests may be misleading. Caution should be taken when evaluating the predicted risks obtained from genetic counseling. On the other hand, the improved accuracy of risk estimates after incorporating population heterogeneity information into the model did point out a promising direction for genetic counseling, since more and more new techniques are being invented and disease etiology is being better understood.

Bias↗

PubMed2004

Effects of duplicate and screening isolates on surveillance of community and hospital antibiotic resistance.

OBJECTIVES: To investigate common contentions that duplicate and screening isolates consistently show marked excess resistance, and that inclusion of such isolates significantly distorts regional resistance estimates. METHODS: Two Welsh surveys of antibiotic resistance for routine diagnostic isolates were analysed, comprising 309,129 isolates of six common community pathogens and 85,061 ward isolates of 11 common hospital pathogens. Duplicate isolates were defined as isolates from the same patient of the same pathogen with an indistinguishable susceptibility pattern, excluding the initial isolate. Significance was assessed from 95% confidence limits of the difference between resistance estimates. RESULTS: Duplicate isolates comprised approximately 20% of total isolates. For the 195 antibiotic-pathogen combinations investigated, differences in resistance between duplicate and non-duplicate isolates were statistically significant for 93. Only 54 combinations showed significantly increased resistance amongst duplicates, and only 30 of these showed a difference >5%. Comparisons of de-duplicated with un-de-duplicated regional resistance estimates showed significant differences for only 18 of 195 antibiotic-pathogen combinations; none were sufficient to alter judgement on clinical use. Screening isolates produced little disturbance of resistance estimates for Staphylococcus aureus, with the exception of flucloxacillin resistance, where inclusion of screening and duplicate isolates resulted in an increase of 4.4% for both community and hospital resistance estimates. CONCLUSIONS: The contentions were incorrect for these regional surveys. However, the proportion (and so effects) of screening and duplicate isolates may be greater in surveys of units with frequent repetitive sampling practice (burns, ITU, cystic fibrosis), or pathogens subjected to unusually intensive infection control sampling.

Bias↗