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Find video protocols related to scientific articles indexed in Pubmed.
Characterizing expected benefits of biomarkers in treatment selection.
Biostatistics
PUBLISHED: 09-03-2014
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Biomarkers associated with heterogeneity in subject responses to treatment hold potential for treatment selection. In practice, the decision regarding whether to adopt a treatment-selection marker depends on the effect of using the marker on the rate of targeted disease and on the cost associated with treatment. We propose an expected benefit measure that incorporates both effects to quantify a marker's treatment-selection capacity. This measure builds upon an existing decision-theoretic framework, but is expanded to account for the fact that optimal treatment absent marker information varies with the cost of treatment. In addition, we establish upper and lower bounds on the expected benefit for a perfect treatment-selection rule which provides the basis for a standardized expected benefit measure. We develop model-based estimators for these measures in a randomized trial setting and evaluate their asymptotic properties. An adaptive bootstrap confidence interval is proposed for inference in the presence of non-regularity. Alternative estimators robust to risk model misspecification are also investigated. We illustrate our methods using the Diabetes Control and Complications Trial where we evaluate the expected benefit of baseline hemoglobin A1C in selecting diabetes treatment.
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FCGR2C polymorphisms associate with HIV-1 vaccine protection in RV144 trial.
J. Clin. Invest.
PUBLISHED: 08-08-2014
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The phase III RV144 HIV-1 vaccine trial estimated vaccine efficacy (VE) to be 31.2%. This trial demonstrated that the presence of HIV-1-specific IgG-binding Abs to envelope (Env) V1V2 inversely correlated with infection risk, while the presence of Env-specific plasma IgA Abs directly correlated with risk of HIV-1 infection. Moreover, Ab-dependent cellular cytotoxicity responses inversely correlated with risk of infection in vaccine recipients with low IgA; therefore, we hypothesized that vaccine-induced Fc receptor-mediated (FcR-mediated) Ab function is indicative of vaccine protection. We sequenced exons and surrounding areas of FcR-encoding genes and found one FCGR2C tag SNP (rs114945036) that associated with VE against HIV-1 subtype CRF01_AE, with lysine at position 169 (169K) in the V2 loop (CRF01_AE 169K). Individuals carrying CC in this SNP had an estimated VE of 15%, while individuals carrying CT or TT exhibited a VE of 91%. Furthermore, the rs114945036 SNP was highly associated with 3 other FCGR2C SNPs (rs138747765, rs78603008, and rs373013207). Env-specific IgG and IgG3 Abs, IgG avidity, and neutralizing Abs inversely correlated with CRF01_AE 169K HIV-1 infection risk in the CT- or TT-carrying vaccine recipients only. These data suggest a potent role of Fc-? receptors and Fc-mediated Ab function in conferring protection from transmission risk in the RV144 VE trial.
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Net reclassification indices for evaluating risk prediction instruments: a critical review.
Epidemiology
PUBLISHED: 07-31-2014
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Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For predefined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true- and false-positive rates. We advocate the use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid predefined risk categories. However, it experiences many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.
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Combining biomarkers to optimize patient treatment recommendations.
Biometrics
PUBLISHED: 06-04-2014
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Markers that predict treatment effect have the potential to improve patient outcomes. For example, the OncotypeDX® RecurrenceScore® has some ability to predict the benefit of adjuvant chemotherapy over and above hormone therapy for the treatment of estrogen-receptor-positive breast cancer, facilitating the provision of chemotherapy to women most likely to benefit from it. Given that the score was originally developed for predicting outcome given hormone therapy alone, it is of interest to develop alternative combinations of the genes comprising the score that are optimized for treatment selection. However, most methodology for combining markers is useful when predicting outcome under a single treatment. We propose a method for combining markers for treatment selection which requires modeling the treatment effect as a function of markers. Multiple models of treatment effect are fit iteratively by upweighting or "boosting" subjects potentially misclassified according to treatment benefit at the previous stage. The boosting approach is compared to existing methods in a simulation study based on the change in expected outcome under marker-based treatment. The approach improves upon methods in some settings and has comparable performance in others. Our simulation study also provides insights as to the relative merits of the existing methods. Application of the boosting approach to the breast cancer data, using scaled versions of the original markers, produces marker combinations that may have improved performance for treatment selection.
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Analysis of HLA A*02 association with vaccine efficacy in the RV144 HIV-1 vaccine trial.
J. Virol.
PUBLISHED: 05-14-2014
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The RV144 HIV-1 vaccine trial demonstrated partial efficacy of 31% against HIV-1 infection. Studies into possible correlates of protection found that antibodies specific to the V1 and V2 (V1/V2) region of envelope correlated inversely with infection risk and that viruses isolated from trial participants contained genetic signatures of vaccine-induced pressure in the V1/V2 region. We explored the hypothesis that the genetic signatures in V1 and V2 could be partly attributed to selection by vaccine-primed T cells. We performed a T-cell-based sieve analysis of breakthrough viruses in the RV144 trial and found evidence of predicted HLA binding escape that was greater in vaccine versus placebo recipients. The predicted escape depended on class I HLA A*02- and A*11-restricted epitopes in the MN strain rgp120 vaccine immunogen. Though we hypothesized that this was indicative of postacquisition selection pressure, we also found that vaccine efficacy (VE) was greater in A*02-positive (A*02(+)) participants than in A*02(-) participants (VE = 54% versus 3%, P = 0.05). Vaccine efficacy against viruses with a lysine residue at site 169, important to antibody binding and implicated in vaccine-induced immune pressure, was also greater in A*02(+) participants (VE = 74% versus 15%, P = 0.02). Additionally, a reanalysis of vaccine-induced immune responses that focused on those that were shown to correlate with infection risk suggested that the humoral responses may have differed in A*02(+) participants. These exploratory and hypothesis-generating analyses indicate there may be an association between a class I HLA allele and vaccine efficacy, highlighting the importance of considering HLA alleles and host immune genetics in HIV vaccine trials.
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An approach to evaluating and comparing biomarkers for patient treatment selection.
Int J Biostat
PUBLISHED: 04-04-2014
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Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit from adjuvant chemotherapy and can therefore avoid its toxicity.
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Net risk reclassification p values: valid or misleading?
J. Natl. Cancer Inst.
PUBLISHED: 03-28-2014
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The Net Reclassification Index (NRI) and its P value are used to make conclusions about improvements in prediction performance gained by adding a set of biomarkers to an existing risk prediction model. Although proposed only 5 years ago, the NRI has gained enormous traction in the risk prediction literature. Concerns have recently been raised about the statistical validity of the NRI.
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Have the explosive HIV epidemics in sub-Saharan Africa been driven by higher community viral load?
AIDS
PUBLISHED: 10-25-2013
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The HIV epidemic has carved contrasting trajectories around the world with sub-Saharan Africa (SSA) being most affected. We hypothesized that mean HIV-1 plasma RNA viral loads are higher in SSA than other areas, and that these elevated levels may contribute to the scale of epidemics in this region.
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Efficacy trial of a DNA/rAd5 HIV-1 preventive vaccine.
N. Engl. J. Med.
PUBLISHED: 10-07-2013
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A safe and effective vaccine for the prevention of human immunodeficiency virus type 1 (HIV-1) infection is a global priority. We tested the efficacy of a DNA prime-recombinant adenovirus type 5 boost (DNA/rAd5) vaccine regimen in persons at increased risk for HIV-1 infection in the United States.
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New clinical trial designs for HIV vaccine evaluation.
Curr Opin HIV AIDS
PUBLISHED: 07-23-2013
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With multiple HIV vaccine candidates suitable for efficacy evaluation in a rapidly changing HIV prevention landscape, innovative HIV vaccine trial design research is much needed to optimally utilize resources by building on lessons learned from past HIV vaccine efficacy trials.
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Vaccine-induced gag-specific T cells are associated with reduced viremia after HIV-1 infection.
J. Infect. Dis.
PUBLISHED: 07-21-2013
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The contribution of host T-cell immunity and HLA class I alleles to the control of human immunodeficiency virus (HIV-1) replication in natural infection is widely recognized. We assessed whether vaccine-induced T-cell immunity, or expression of certain HLA alleles, impacted HIV-1 control after infection in the Step MRKAd5/HIV-1 gag/pol/nef study. Vaccine-induced T cells were associated with reduced plasma viremia, with subjects targeting ?3 gag peptides presenting with half-log lower mean viral loads than subjects without Gag responses. This effect was stronger in participants infected proximal to vaccination and was independent of our observed association of HLA-B*27, -B*57 and -B*58:01 alleles with lower HIV-1 viremia. These findings support the ability of vaccine-induced T-cell responses to influence postinfection outcome and provide a rationale for the generation of T-cell responses by vaccination to reduce viremia if protection from acquisition is not achieved. Clinical trials identifier: NCT00095576.
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A Framework for Evaluating Markers Used to Select Patient Treatment.
Med Decis Making
PUBLISHED: 06-27-2013
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There is growing interest in markers that can be used to identify which patients are most likely to benefit from a treatment. For example, the Gail breast cancer risk prediction model may be useful for identifying a subset of older women for whom the benefit of tamoxifen for breast cancer prevention is likely to outweigh the harm. Two general classes of approaches to evaluating treatment selection markers have been developed. The first uses data on a cohort of untreated subjects to develop a risk prediction model, such as the Gail model, which is used to identify a high-risk subset of subjects. This model is paired with a measure of treatment effect to assess the impact of identifying and treating the high-risk subset. The second approach uses data from a randomized trial to model the treatment effect on a composite outcome that includes all effects of treatment (positive and negative). The treatment effect model is used to identify a subset of subjects with positive treatment effects and to assess the impact of identifying and treating this subset. We describe a framework that includes both existing approaches as special cases. In doing so, we review the existing approaches, clarify their underlying assumptions, and facilitate the evaluation of markers under less restrictive assumptions.
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In pursuit of an HIV vaccine: designing efficacy trials in the context of partially effective nonvaccine prevention modalities.
AIDS Res. Hum. Retroviruses
PUBLISHED: 06-25-2013
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The HIV prevention landscape is evolving rapidly, and future efficacy trials of candidate vaccines, which remain the best long-term option for stemming the HIV epidemic, will be conducted in the context of partially effective nonvaccine prevention modalities. It is essential that these trials provide for valid and efficient evaluation of vaccine efficacy and immune correlates. The availability of partially effective prevention modalities presents opportunities to study their interactions with vaccines to maximally reduce HIV incidence. This article proposes an approach for conducting future vaccine efficacy trials in the context of background use of partially effective nonvaccine prevention modalities, and for conducting future vaccine efficacy trials that provide nonvaccine prevention modalities in one or more of the randomized study groups. Strategies are discussed for responding to emerging evidence on nonvaccine prevention modalities during ongoing vaccine trials. Next-generation HIV vaccine efficacy trials will almost certainly be more complex in their design and implementation but may become more relevant to at-risk populations and better suited to the ultimate goal of reducing HIV incidence at the population level.
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Impact of herpes simplex virus type 2 on HIV-1 acquisition and progression in an HIV vaccine trial (the Step study).
J. Acquir. Immune Defic. Syndr.
PUBLISHED: 08-24-2011
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Extensive observational data suggest that herpes simplex virus type 2 (HSV-2) infection may facilitate HIV acquisition, increase HIV viral load, and accelerate HIV progression and onward transmission. To explore these relationships, we examined the impact of preexisting HSV-2 infection in an international HIV vaccine trial.
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Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration.
Eur. J. Epidemiol.
PUBLISHED: 03-23-2011
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The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
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Strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS): explanation and elaboration.
J Clin Epidemiol
PUBLISHED: 03-16-2011
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The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
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Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration.
Eur. J. Hum. Genet.
PUBLISHED: 03-16-2011
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The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
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Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration.
Eur. J. Clin. Invest.
PUBLISHED: 03-15-2011
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• The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. • The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. • Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. • A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. • These recommendations aim to enhance the transparency, quality and completeness of study reporting and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
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Measuring the performance of markers for guiding treatment decisions.
Ann. Intern. Med.
PUBLISHED: 02-16-2011
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Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers.
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On quantifying the magnitude of confounding.
Biostatistics
PUBLISHED: 03-04-2010
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When estimating the association between an exposure and outcome, a simple approach to quantifying the amount of confounding by a factor, Z, is to compare estimates of the exposure-outcome association with and without adjustment for Z. This approach is widely believed to be problematic due to the nonlinearity of some exposure-effect measures. When the expected value of the outcome is modeled as a nonlinear function of the exposure, the adjusted and unadjusted exposure effects can differ even in the absence of confounding (Greenland , Robins, and Pearl, 1999); we call this the nonlinearity effect. In this paper, we propose a corrected measure of confounding that does not include the nonlinearity effect. The performances of the simple and corrected estimates of confounding are assessed in simulations and illustrated using a study of risk factors for low birth-weight infants. We conclude that the simple estimate of confounding is adequate or even preferred in settings where the nonlinearity effect is very small. In settings with a sizable nonlinearity effect, the corrected estimate of confounding has improved performance.
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Tiered categorization of a diverse panel of HIV-1 Env pseudoviruses for assessment of neutralizing antibodies.
J. Virol.
PUBLISHED: 11-25-2009
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The restricted neutralization breadth of vaccine-elicited antibodies is a major limitation of current human immunodeficiency virus-1 (HIV-1) candidate vaccines. In order to permit the efficient identification of vaccines with enhanced capacity for eliciting cross-reactive neutralizing antibodies (NAbs) and to assess the overall breadth and potency of vaccine-elicited NAb reactivity, we assembled a panel of 109 molecularly cloned HIV-1 Env pseudoviruses representing a broad range of genetic and geographic diversity. Viral isolates from all major circulating genetic subtypes were included, as were viruses derived shortly after transmission and during the early and chronic stages of infection. We assembled a panel of genetically diverse HIV-1-positive (HIV-1(+)) plasma pools to assess the neutralization sensitivities of the entire virus panel. When the viruses were rank ordered according to the average sensitivity to neutralization by the HIV-1(+) plasmas, a continuum of average sensitivity was observed. Clustering analysis of the patterns of sensitivity defined four subgroups of viruses: those having very high (tier 1A), above-average (tier 1B), moderate (tier 2), or low (tier 3) sensitivity to antibody-mediated neutralization. We also investigated potential associations between characteristics of the viral isolates (clade, stage of infection, and source of virus) and sensitivity to NAb. In particular, higher levels of NAb activity were observed when the virus and plasma pool were matched in clade. These data provide the first systematic assessment of the overall neutralization sensitivities of a genetically and geographically diverse panel of circulating HIV-1 strains. These reference viruses can facilitate the systematic characterization of NAb responses elicited by candidate vaccine immunogens.
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Estimation and Comparison of Receiver Operating Characteristic Curves.
Stata J
PUBLISHED: 08-14-2009
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The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under the ROC curve. Alternatively pointwise comparisons between ROC curves or inverse ROC curves can be made. Options to adjust these analyses for covariates, and to perform ROC regression are described in a companion article. We use a unified framework by representing the ROC curve as the distribution of the marker in cases after standardizing it to the control reference distribution.
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Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve.
Biometrika
PUBLISHED: 04-01-2009
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Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.
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Accommodating Covariates in ROC Analysis.
Stata J
PUBLISHED: 01-06-2009
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Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three different ways of using covariate information. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e. the ROC curve), we describe methods for modelling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and ask how much discriminatory accuracy improves with the addition of the marker to the covariates (incremental value). These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution.
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MRKAd5 HIV-1 Gag/Pol/Nef vaccine-induced T-cell responses inadequately predict distance of breakthrough HIV-1 sequences to the vaccine or viral load.
PLoS ONE
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The sieve analysis for the Step trial found evidence that breakthrough HIV-1 sequences for MRKAd5/HIV-1 Gag/Pol/Nef vaccine recipients were more divergent from the vaccine insert than placebo sequences in regions with predicted epitopes. We linked the viral sequence data with immune response and acute viral load data to explore mechanisms for and consequences of the observed sieve effect.
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Assessing treatment-selection markers using a potential outcomes framework.
Biometrics
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Treatment-selection markers are biological molecules or patient characteristics associated with ones response to treatment. They can be used to predict treatment effects for individual subjects and subsequently help deliver treatment to those most likely to benefit from it. Statistical tools are needed to evaluate a markers capacity to help with treatment selection. The commonly adopted criterion for a good treatment-selection marker has been the interaction between marker and treatment. While a strong interaction is important, it is, however, not sufficient for good marker performance. In this article, we develop novel measures for assessing a continuous treatment-selection marker, based on a potential outcomes framework. Under a set of assumptions, we derive the optimal decision rule based on the marker to classify individuals according to treatment benefit, and characterize the markers performance using the corresponding classification accuracy as well as the overall distribution of the classifier. We develop a constrained maximum-likelihood method for estimation and testing in a randomized trial setting. Simulation studies are conducted to demonstrate the performance of our methods. Finally, we illustrate the methods using an HIV vaccine trial where we explore the value of the level of preexisting immunity to adenovirus serotype 5 for predicting a vaccine-induced increase in the risk of HIV acquisition.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.