Dataset Information

Adjusting confounders in ranking biomarkers: a model-based ROC approach.

ABSTRACT: High-throughput studies have been extensively conducted in the research of complex human diseases. As a representative example, consider gene-expression studies where thousands of genes are profiled at the same time. An important objective of such studies is to rank the diagnostic accuracy of biomarkers (e.g. gene expressions) for predicting outcome variables while properly adjusting for confounding effects from low-dimensional clinical risk factors and environmental exposures. Existing approaches are often fully based on parametric or semi-parametric models and target evaluating estimation significance as opposed to diagnostic accuracy. Receiver operating characteristic (ROC) approaches can be employed to tackle this problem. However, existing ROC ranking methods focus on biomarkers only and ignore effects of confounders. In this article, we propose a model-based approach which ranks the diagnostic accuracy of biomarkers using ROC measures with a proper adjustment of confounding effects. To this end, three different methods for constructing the underlying regression models are investigated. Simulation study shows that the proposed methods can accurately identify biomarkers with additional diagnostic power beyond confounders. Analysis of two cancer gene-expression studies demonstrates that adjusting for confounders can lead to substantially different rankings of genes.

SUBMITTER: Yu T

PROVIDER: S-EPMC3431720 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adjusting confounders in ranking biomarkers: a model-based ROC approach.

Yu Tao T Li Jialiang J Ma Shuangge S

Briefings in bioinformatics 20120306 5

High-throughput studies have been extensively conducted in the research of complex human diseases. As a representative example, consider gene-expression studies where thousands of genes are profiled at the same time. An important objective of such studies is to rank the diagnostic accuracy of biomarkers (e.g. gene expressions) for predicting outcome variables while properly adjusting for confounding effects from low-dimensional clinical risk factors and environmental exposures. Existing approach ...[more]

PMID: 22396461

Similar Datasets

Project description:BackgroundThe co-occurrence of two or more medical conditions in the same individual is not uncommon. If disability-adjusted life year (DALY) calculations are carried out for each condition separately, multimorbidity may lead to an overestimation of the morbidity component, the Years Lived with Disability (YLD). Adjusting for comorbidity may be straightforward if all symptoms have same onset and duration; however, when the comorbid health states occur at different time points, an analytical solution to the comorbidity problem becomes more complex. The aim of this study was to develop an individual-based modelling (IBM) approach to adjust incidence-based disease burden estimation for multimorbidity that allows simulating hypothetical individuals and tracking their disease history, including possible comorbidities, over time.MethodsWe demonstrated the IBM approach using an example of external comorbidity, i.e., colon cancer comorbid with healthcare-associated pneumonia (HAP) and by assuming an independent multiplicative model. First, each cumulative progression probabilities were converted to a daily transition probabilities. Second, disability weights for simultaneously experienced health states and duration in each health state were determined. Third, YLD, adjusted for comorbidity, was calculated at every time step. We simulated a cohort of 1000 colorectal cancer patients aged 65 years. Ninety-five percent uncertainty intervals around median YLD values were estimated by Monte Carlo methods.ResultsThe median estimated YLD per 1000 cases (due to both cancer and HAP) adjusted for co-morbidity was 545 YLD/1000 (95% interval: 513-585). The impact of not adjusting disability weights for co-existent health states varied from minimal to small; YLD for colorectal cancer would be overestimated only slightly - by 1.6 YLD/1000 - by not adjusting for concurrent HAP. YLD for those HAP patients who have concurrent early-stage colorectal cancer would be overestimated by 2.3 YLD/1000.ConclusionsThe computation of disease burden in the presence of multimorbidity using the incidence-based DALY approach can be handled through IBM. Our approach can be extended to other, more complicated multimorbidity scenarios which are responsible for a high current global disease burden, such as tuberculosis and HIV infection.

Project description:BackgroundNear-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed "harvesting" in the literature.MethodsWe present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a "ball and urn" model.ResultsAmong the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy.ConclusionOur results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement - both in its length and prevalence - by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.

Dataset Information

Adjusting confounders in ranking biomarkers: a model-based ROC approach.

Publications

Adjusting confounders in ranking biomarkers: a model-based ROC approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets