Original ArticleA Perspective on Standardizing the Predictive Power of Noninvasive Cardiovascular Tests by Likelihood Ratio Computation: 2. Clinical Applications
Section snippets
Estimating The Predictive Power Of Noninvasive Test Results For Diagnosis Of Cad
A comparison of the relative predictive power of noninvasive tests is most ideally made when the tests to be compared are performed in parallel in the same index population. The index population must be representative of the population to which the tests are to be applied and should be of sufficient size to permit meaningful statistical analysis of the observed differences in test power. Such optimum circumstances for the comparison of noninvasive cardiovascular tests are rarely achieved. The
Estimating Predictive Independence For Combined Test Results
The calculation of the predicted and observed effective likelihood ratio for disease for multiple positive test results, (+)ELRd, and the predicted and observed effective likelihood ratio for no disease for multiple negative test results, (-)ELRn, provides a unique method for verifying the presence or absence of predictive independence among combined test results. This method is based on the premise that predictive independence among test results of like sign can be verified by the finding that
Nterpreting Multiple Threshold Tests Using The Roc Curveand The Likelihood Ratio
A common approach to assessing and comparing test results that can be interpreted at multiple thresholds (ie, tests reported in continuous quantitative terms or at multiple qualitative end points) is the ROC curve. The ROC curve is usually exhibited as a graphic plot of Se vs (l - Sp). In the context of the ROC curve, the Se is termed the true-positive rate plotted on the vertical axis, while the (1 - Sp) is termed the false-positive rate plotted on the horizontal axis. This ROC form is in fact
Estimating The Power Of A Multivariable Predictive Model
The potential error introduced by the use of pretest assumptions and test redundancy when applying Bayesian predictive formulations for 2 or more variables has led to increasing interest in the use of alternative multivariable probability models. In using such methods as multiple linear and logistic regression, discriminant function, and proportional hazards analysis, the predictive variables are adjusted or corrected by introduction of regression coefficients for each cofactor in the
Evaluating The Influence Of Verification Bias On The Diagnostic Power Of Noninvasive Cardiovascular Tests
Verification bias is a form of population selection bias that occurs when the sensitivity and specificity of a noninvasive test are derived from a population referred for definitive diagnostic evaluation on the basis of prior noninvasive testing. Verification bias includes 2 forms of selection bias: (1) knowledge ledge of previous test results that biases referral for verification in favor of those with positive test results and (2) pretest patient characteristics that influence a physician's
Evaluating The Predictive Power Of Cardiovascular Risk Factors
The likelihood ratio principle is readily adapted to the analysis of the predictive power of cardiovascular risk factors. Such risk factor analysis includes 2 forms: (1) epidemiological analysis, wherein the likelihood ratios are computed for population subsets with a given risk factor, irrespective of an association with other risk factors and (2) clinical analysis, wherein the likelihood ratios are computed for population subsets with specific risk factor profiles. Epidemiological analysis
Evaluating The Diagnostic Power Of Chest Pain Syndromes
Likelihood ratio analysis is uniquely applicable to an evaluation of the predictive power of clinical symptoms. Such an analysis is exemplified in a study of the prevalence of CAD in chest pain syndrome subsets reported by Chaitman et al53 for patients enrolled in the Coronary Arteriography Surgery Study (CASS). In this study, 5334 men and 2806 women older than 30 years were referred for coronary arteriography; they were subdivided by 3 symptom complexes into populations with definite angina,
Limitations In Deriving And Applying Likelihood Ratio Estimates Of Test Power
Variables that limit the estimation and application of the likelihood ratio as a measure of the predictive power of a noninvasive test include the following: (1) index population size, (2) index population composition, (3) methodologic variation in testing, and (4) variance between testing and verification modalities. The limitation of index population size is especially pertinent to the likelihood ratio computation, while the remaining limitations apply to all measures of test power.
Acknowledgments
The author expresses his appreciation to Julienne M. Montgomery for her able assistance in preparing the manuscript.
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Cited by (21)
Optimized electrocardiographic criteria for prior inferior and anterior myocardial infarction
2012, Journal of ElectrocardiologyCitation Excerpt :The positive likelihood ratio expresses a diagnostic test's ability to detect the disease being tested for. A positive likelihood ratio greater than 10 exhibited by a diagnostic test is considered to indicate an excellent rule-in power for the test.10 Unlike the positive predictive value, positive likelihood ratios permit diagnostic inferences that are independent of the prevalence of the abnormality in the population being tested.10
Noninvasive Detection of Left Ventricular Systolic Dysfunction by Acoustic Cardiography in Cardiac Failure Patients
2008, Journal of Cardiac FailureCitation Excerpt :We also generated receiver operator characteristic curves to determine the diagnostic sensitivities and specificities for LVSD and used them to calculate positive and negative likelihood ratios. Unlike positive and negative predictive values, positive and negative likelihood ratios are independent of the prevalence of the abnormality in the population being tested.21 To avoid dividing by zero, we set the positive likelihood ratio equal to sensitivity in the cases in which specificity was 100%.
Usefulness of Acoustic Cardiography to Resolve Ambiguous Values of B-Type Natriuretic Peptide Levels in Patients With Suspected Heart Failure
2007, American Journal of CardiologyCitation Excerpt :A positive or a negative likelihood ratio >10 shown by a diagnostic test was considered to have excellent rule-in or rule-out power for the condition being tested, respectively. Unlike positive and negative predictive values, diagnostic inferences based on positive and negative likelihood ratios were independent of the prevalence of the abnormality in the population being tested.12 Statistical analyses were performed using SPSS, version 13.0 (SPSS, Inc., Chicago, Illinois).
Assessment of patients with low-risk chest pain in the emergency department: Head-to-head comparison of exercise stress echocardiography and exercise myocardial SPECT
2005, American Heart JournalCitation Excerpt :To estimate the predictive power of single tests for predicting the presence or absence of disease, sensitivity, specificity, positive (+) and negative (−) predictive values, accuracy (defined as percentage of true test results, ie, (true positives + true negatives)/total number tests performed), and likelihood ratios [LR] ((+)LR = sensitivity/(1−specificity); (−)LR = specificity/(1−sensitivity)) were calculated considering follow-up data. Standard deviations, 95% confidence limits,30 and odds LR (posttest odds of disease for a positive test = (+)PTOD; posttest odds of no disease for a negative test = (−)PTON)31,32 were also calculated. The same LRs were adopted for estimating the power of combinations of tests, applied in sequence, for predicting the presence or absence of disease (see Appendix A).
Use of Likelihood Ratio Computation to Standardize the Predictive Power of Noninvasive Cardiovascular Tests
2000, Mayo Clinic Proceedings