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Alternative Lens Model Equations for Dichotomous Judgments about Dichotomous Criteria.

ABSTRACT: Objective:The Brunswik lens model typically represents a judge's accuracy using parameters derived from linear regression. This is not optimal if the judgment or the ecological criterion is dichotomous. Alternative approaches, modeling dichotomies using logistic regression, or linearizing judgments with confidence ratings, have not been compared with the same data. Method:Four techniques for deriving lens model equation parameters were compared: 1) linear and 2) logistic regression applied to dichotomous patient outcomes and judgments; 3) linear regression with confidence-adjusted judgments but dichotomous patient outcomes; and 4) a hybrid with a linear model of the confidence-adjusted judgments and a logistic model of the patient outcomes. Results:Judgment accuracy (ra) was slightly higher with confidence adjustment of the categorical judgments. The logistic lens model accounted for a higher proportion of ra than the linear lens model; the confident-linear and hybrid lens models were intermediate. For up to a quarter of participants, different methods identified different cues as most important. Display condition differences in achievement ra and in lens model components are similar with all lens model methods. Conclusion:Each of the three alternative lens model equation methods improves on the linear lens model equation's decomposition of the accuracy of dichotomous judgments. Confidence adjustment improves achievement although it requires additional work from the subjects. The logistic lens model equation explains the highest proportion of achievement, but with a small stimulus set it is more vulnerable to cue intercorrelations than either the linear or the confident linear lens model equation.

SUBMITTER: Hamm RM

PROVIDER: S-EPMC5736149 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Alternative Lens Model Equations for Dichotomous Judgments about Dichotomous Criteria.

Hamm Robert M RM Yang Huiqin H

Journal of behavioral decision making 20160531 2

<h4>Objective</h4>The Brunswik lens model typically represents a judge's accuracy using parameters derived from linear regression. This is not optimal if the judgment or the ecological criterion is dichotomous. Alternative approaches, modeling dichotomies using logistic regression, or linearizing judgments with confidence ratings, have not been compared with the same data.<h4>Method</h4>Four techniques for deriving lens model equation parameters were compared: 1) linear and 2) logistic regressio ...[more]

PMID: 29269993

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Project description:BackgroundEarly biomarkers are helpful for predicting clinical endpoints and for evaluating efficacy in clinical trials even if the biomarker cannot replace clinical outcome as a surrogate. The building and evaluation of an association model between biomarkers and clinical outcomes are two equally important concerns regarding the prediction of clinical outcome. This paper is to address both issues in a Bayesian framework.MethodsA Bayesian meta-analytic approach is proposed to build a prediction model between the biomarker and clinical endpoint for dichotomous variables. Compared with other Bayesian methods, the proposed model only requires trial-level summary data of historical trials in model building. By using extensive simulations, we evaluate the link function and the application condition of the proposed Bayesian model under scenario (i) equal positive predictive value (PPV) and negative predictive value (NPV) and (ii) higher NPV and lower PPV. In the simulations, the patient-level data is generated to evaluate the meta-analytic model. PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome. The minimum number of historical trials to be included in building the model is also considered.ResultsIt is seen from the simulations that the logit link function performs better than the odds and cloglog functions under both scenarios. PPV/NPV ?0.5 for equal PPV and NPV, and PPV?+?NPV ?1 for higher NPV and lower PPV are proposed in order to predict clinical outcome accurately and precisely when the proposed model is considered. Twenty historical trials are required to be included in model building when PPV and NPV are equal. For unequal PPV and NPV, the minimum number of historical trials for model building is proposed to be five. A hypothetical example shows an application of the proposed model in global drug development.ConclusionsThe proposed Bayesian model is able to predict well the clinical endpoint from the observed biomarker data for dichotomous variables as long as the conditions are satisfied. It could be applied in drug development. But the practical problems in applications have to be studied in further research.

Dataset Information

Alternative Lens Model Equations for Dichotomous Judgments about Dichotomous Criteria.

Publications

Alternative Lens Model Equations for Dichotomous Judgments about Dichotomous Criteria.

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