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Kibum Kim,1– 3 Jacob Crook,3,4 Chao-Chin Lu,3 Heather Nyman,2 Abdullah Abdelaziz,1 Joanne LaFleur2,3 1Department of Pharmacy Systems, Outcomes and Policy, University of Illinois Chicago, Chicago, IL, USA; 2Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, USA; 3VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 4Division of Epidemiology, University of Utah, Salt Lake City, UT, USACorrespondence: Kibum Kim, Department of Pharmacy Systems, Outcomes and Policy, University of Illinois Chicago, 833 S. Wood Street | Rm 256 PHARM, MC 871, Chicago, IL, 60616, USA, Tel +1 (312) 413-0152, Email kkim204@uic.eduRationale: With a large number of patients and high mortality, diabetic kidney disease (DKD) imposes a significant burden on US health care. Although diabetes is the leading cause of chronic kidney disease and complications, the epidemiology of DKD in the contemporary US veteran population is generally unknown.Objective: We aimed to estimate the rate of DKD progression and to measure the general epidemiology of DKD in the United States veteran population.Study Design: We performed a retrospective observational research using electronic health-care records and administrative databases.Setting: The DKD patient cohort was abstracted from the Veterans Health Administration health-record data from January 2016 to March 2022.Participants: We defined DKD patients using the laboratory test data based on Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guidelines.Analytic Approach: Summary statistics include the five-year cumulative incidence of progression to an advanced stage from the DKD stage at the cohort entry date and prevalence at a series of single time points.Results: A total of 685,288 patients (male [96%], mean age 62 years, Caucasian [64%], non-Hispanic [87%]) met our eligibility criteria. The 5-year cumulative incidence of progression to an advanced DKD stage or all-cause death from DKD stages G1 A2/A3, G2 A2/A3, G3a, and G3b were 52.0%, 47.4%, 50.5%, and 60.9%, respectively. In sum, 594,082 patients were classified as moderate or high risk as per KDIGO guidelines in 2021, and stages G3a and G3b accounted for 51.2% and 25.3%, respectively, of cases.Conclusion: More than half of DKD patients underwent a stage progression or death within 5 years. A substantial number of DKD patients at an earlier stage might be left undetermined. The study findings warrant a revision of DKD patient identification and management in US veterans.Keywords: diabetic kidney disease, prevalence, US veterans

The literature suggests that the commonly used augmentation method of reject inference achieves no appreciable benefit in the context of logistic and probit regression models. Ranking is not improved and the ability to discern a correct cut-off is undermined. This paper considers the application of augmentation to profit scoring applicants by means of survival analysis and by the Cox proportional hazard model, in particular. This new context involves more elaborate models answering more specific questions such as when will default occur and what will be its precise financial implication. Also considered in this paper is the extent to which the rejection rate is critical in the potential usefulness of reject inference and how augmentation meets that potential. The conclusion is essentially that augmentation achieves negative benefits only and that the scope for reject inference in this context pertains mainly to circumstances where a high proportion of applicants have been rejected. Journal of the Operational Research Society (2010) 61, 473-485. doi: 10.1057/jors.2008.180 Published online 4 March 2009 Keywords: credit scoring; reject inference; augmentation; survival analysis; proportional hazard

We use response data collected by a lender to estimate the probabilities of loan offers being accepted by the applicants and the survival probabilities of default and of paying back early. Combining all those together we estimated the expected profit surface for the lender at the time of application before making an offer to an applicant. The results show how a lender could find the optimal interest rate to increase the expected profit or its market share. We also consider how different optimal decision policies could be applied to different market segments. Journal of the Operational Research Society (2010) 61, 430-442. doi: 10.1057/jors.2009.33 Published online 1 April 2009 Keywords: profitability; acceptance; credit; take-up

Survival analysis can be applied to build models for time to default on debt. In this paper, we report an application of survival analysis to model default on a large data set of credit card accounts. We explore the hypothesis that probability of default (PD) is affected by general conditions in the economy over time. These macroeconomic variables (MVs) cannot readily be included in logistic regression models. However, survival analysis provides a framework for their inclusion as time-varying covariates. Various MVs, such as interest rate and unemployment rate, are included in the analysis. We show that inclusion of these indicators improves model fit and affects PD yielding a modest improvement in predictions of default on an independent test set. doi:10.1057/jors.2008.130 Keywords: credit scoring; survival analysis; time-varying covariates; risk; banking: macroeconomic variables

If a credit scoring model is built using only applicants who have been previously accepted for credit such a non-random sample selection may produce bias in the estimated model parameters and accordingly the model's predictions of repayment performance may not be optimal. Previous empirical research suggests that omission of rejected applicants has a detrimental impact on model estimation and prediction. This paper explores the extent to which, given the previous cutoff score applied to decide on accepted applicants, the number of included variables influences the efficacy of a commonly used reject inference technique, reweighting. The analysis benefits from the availability of a rare sample, where virtually no applicant was denied credit. The general indication is that the efficacy of reject inference is little influenced by either model leanness or interaction between model leanness and the rejection rate that determined the sample. However, there remains some hint that very lean models may benefit from reject inference where modelling is conducted on data characterized by a very high rate of applicant rejection. Journal of the Operational Research Society (2005) 56, 1072-1081. doi:10.1057/palgrave.jors.2602017 Published online 8 June 2005 Keywords: reject inference; credit scoring; augmentation

One of the aims of credit scoring models is to predict the probability of repayment of any applicant and yet such models are usually parameterised using a sample of accepted applicants only. This may lead to biased estimates of the parameters. In this paper we examine two issues. First, we compare the classification accuracy of a model based only on accepted applicants, relative to one based on a sample of all applicants. We find only a minimal difference, given the cutoff scores for the old model used by the data supplier. Using a simulated model we examine the predictive performance of models estimated from bands of applicants, ranked by predicted creditworthiness. We find that the lower the risk band of the training sample, the less accurate the predictions for all applicants. We also find that the lower the risk band of the training sample, the greater the overestimate of the true performance of the model, when tested on a sample of applicants within the same risk band--as a financial institution would do. The overestimation may be very large. Second, we examine the predictive accuracy of a bivariate probit model with selection (BVP). This parameterises the accept-reject model allowing for (unknown) omitted variables to be correlated with those of the original good-bad model. The BVP model may improve accuracy if the loan officer has overridden a scoring rule. We find that a small improvement when using the BVP model is sometimes possible. Keywords: credit scoring; reject inference; sample selection