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On two novel parameters for validation of predictive QSAR models.

ABSTRACT: Validation is a crucial aspect of quantitative structure-activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q(2) for internal validation and predictive R(2) for external validation) may be supplemented with two novel parameters r(m)(2) and R(p)(2) for a stricter test of validation. The parameter r(m)(2)((overall)) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter R(p)(2) penalizes model R(2) for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of r(m)(2) parameter, r(m)(2)((LOO)) and r(m)(2)((test)), penalize a model more strictly than Q(2) and R(2)(pred) respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q(2) and R(2)(pred)) but fail to achieve the required values for the novel parameters r(m)(2) and R(p)(2). Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

SUBMITTER: Pratim Roy P

PROVIDER: S-EPMC6254296 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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On two novel parameters for validation of predictive QSAR models.

Pratim Roy Partha P Paul Somnath S Mitra Indrani I Roy Kunal K Roy Kunal K

Molecules (Basel, Switzerland) 20090429 5

Validation is a crucial aspect of quantitative structure-activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q(2) for internal validation and predictive R(2) for external validation) may be supplemented with two novel parameters r(m)(2) and R(p)(2) for a stricter test of validation. The parameter r(m)(2)((overall)) penalizes a model for large differences between observed and predicted values of the compounds of the whole set ...[more]

PMID: 19471190

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Project description:IntroductionEarly discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI) data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS) to assist ICU discharge planning.MethodsRetrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011.Exclusion criteriaDNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2?1) to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC) in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge.Results469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission) differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups.ConclusionsOur models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although prospective validation is warranted, we speculate that these models may have value in assisting clinicians with ICU discharge planning.

Project description:BackgroundExcessive adipose visceral tissue (AVT) represents an independent risk factor for cardiometabolic alterations. The search continues for a highly valid marker for estimating visceral adiposity that is a simple and low cost tool able to screen individuals who are highly at risk of being viscerally obese. The aim of this study was to develop a predictive model for estimating AVT volume using anthropometric parameters.ObjectiveExcessive adipose visceral tissue (AVT) represents an independent risk factor for cardiometabolic alterations. The search continues for a highly valid marker for estimating visceral adiposity that is a simple and low cost tool able to screen individuals who are highly at risk of being viscerally obese. The aim of this study was to develop a predictive model for estimating AVT volume using anthropometric parameters.MethodsA cross-sectional study involving overweight individuals whose AVT was evaluated (using computed tomography-CT), along with the following anthropometric parameters: body mass index (BMI), abdominal circumference (AC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR), sagittal diameter (SD), conicity index (CI), neck circumference (NC), neck-to-thigh ratio (NTR), waist-to-thigh ratio (WTR), and body adiposity index (BAI).Results109 individuals with an average age of 50.3±12.2 were evaluated. The predictive equation developed to estimate AVT in men was AVT = -1647.75 +2.43(AC) +594.74(WHpR) +883.40(CI) (R2 adjusted: 64.1%). For women, the model chosen was: AVT = -634.73 +1.49(Age) +8.34(SD) + 291.51(CI) + 6.92(NC) (R2 adjusted: 40.4%). The predictive ability of the equations developed in relation to AVT volume determined by CT was 66.9% and 46.2% for males and females, respectively (p<0.001).ConclusionsA quick and precise AVT estimate, especially for men, can be obtained using only AC, WHpR, and CI for men, and age, SD, CI, and NC for women. These equations can be used as a clinical and epidemiological tool for overweight individuals.

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On two novel parameters for validation of predictive QSAR models.

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On two novel parameters for validation of predictive QSAR models.

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