Statistical Inference on Desirability Function Optimal Points to Evaluate Multi-Objective Response Surfaces

A shortfall of the Derringer and Suich (1980) desirability function is lack of inferential methods to quantify uncertainty. Most articles for addressing uncertainty usually involve robust methods, providing a point estimate that is less affected by variation. Few articles address confidence intervals or bands but not specifically for the Derringer and Suich method. This research provides two valuable contributions to the field of response surface methodology. The first contribution is evaluating the effect of correlation and plane angles on Derringer and Suich optimal solutions. The second contribution proposes and compares 8 inferential methods--both univariate and multivariate--for creating confidence intervals on each desirability function solution for first order and second order models. The effect of the Derringer and Suich method parameters, objective plane angles, and differing correlation between response surfaces are examined through simulation. The 8 proposed methods include a simple best/worst case method, 2 generalized methods, 4 simulated surface methods, and a nonparametric bootstrap method. One of the generalized methods, 2 of the simulated surface methods, and the nonparametric method account for covariance between the response surfaces. Bivariate examples showcase these methods in the first order and second order models. A multivariate real-world case with 3 objectives is also examined. While all 7 novel methods and the best/worst method seem to perform decently on the second order models. The methods which utilize an underlying multivariate-t distribution, Multivariate Generalized (MG) and Multivariate t Simulated Surface (MVtSSig), are recommended methods from this research as they perform well with small samples for both first order and second order models with coverage only becoming unreliable at non-optimal solutions. MG and MVtSSig inference should be used in conjunction with robust methods such as Pareto Front Optimization to help ascertain which solutions are more likely to be optimal before constructing confidence interval

Characterizing Uncertainty in Correlated Response Variables for Pareto Front Optimization

Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in the parameters which can affect the frequency of treatments appearing on the Pareto front resulting in potentially different proposed solution spaces from that of the MVN

National Foreclosure Mitigation Counseling Program Evaluation: Final Report, Rounds 1 and 2

The National Foreclosure Mitigation Counseling (NFMC) program is a special federal appropriation, administered by NeighborWorks (NW) America, to support a rapid expansion of foreclosure intervention counseling in response to the nationwide foreclosure crisis. As this is a federal appropriation, NW America must inform Congress and other entities of the NFMC program's progress. The Urban Institute (UI) was selected by NW America to evaluate the NFMC program. This report presents the final results from UI's evaluation of the first two rounds of the NFMC program (people receiving counseling in 2008 and 2009), including a detailed analysis of program outcomes first described in preliminary reports of November 2009 (Mayer et al.) and December 2010 (Mayer et al.). According to those reports, homeowners receiving NFMC counseling avoided entering foreclosure, successfully cured existing foreclosures, and obtained more favorable loan modifications. This report updates previous analyses and also includes revised models of several homeowner outcomes for NFMC clients counseled in 2008 and 2009. These new models use an improved comparison sample selection design, which addressed potential issues raised by reviewers of earlier analyses, and a better method for controlling for possible selection bias in the NFMC sample. The additional analyses in this report include models of non-modification cures, non-modification redefaults, and foreclosures avoided

National Foreclosure Mitigation Counseling Program Evaluation: Final Report, Rounds 3 Through 5

The Urban Institute completed a four-year evaluation of Rounds 3 through 5 of the National Foreclosure Mitigation Counseling (NFMC) program. Using a representative NFMC sample of 137,000 loans and a comparison non-NFMC sample of 103,000 loans, the Urban Institute was able to employ robust statistical techniques to isolate the impact of NFMC counseling on loan performance through June 2013.The final evaluation of Rounds 3 through 5 conducted by Urban Institute indicates that the NFMC program continues to have positive effects for homeowners participating in the program Counseled homeowners were more likely to cure a serious delinquency or foreclosure with a modification or other type cure, stay current after obtaining a cure, and for NFMC clients who cured a serious delinquency, avoid foreclosure altogether

Evaluation of an Innovative Transitional Care Clinic in an Interprofessional Teaching Practice

During transitions of care, great opportunity exists for miscommunication, poor care coordination, adverse events, medication errors and unnecessary healthcare utilization costing billions of dollars annually. An Interprofessional Transitions of Care (IPTC) clinic was developed utilizing a Family Medicine team that included physicians, nurses, a clinical social worker, and a clinical pharmacist. The purpose of this study was to determine if utilization of an IPTC clinic prevented hospital readmission, and to identify factors that predict most benefit from an interprofessional approach to transitions of care. A retrospective chart review of 1,001 patients was completed. A treatment group (TG) of 501 patients were offered IPTC clinic appointments following hospital discharge. A control group (CG) of 500 patients were hospitalized and received traditional follow-up prior to development of the IPTC clinic. Traditional follow-up typically consisted of an automated appointment reminder and a physician office visit. Outcomes assessed included 30-day hospital readmission of TG versus CG, and whether patient characteristics predisposed specific patient groups to attend IPTC appointments or benefit more from IPTC participation. Compared with CG, patients who completed an IPTC appointment were 48% less likely to be readmitted to the hospital within 30 days. Patients with congestive heart failure and cellulitis particularly benefited from IPTC. Telephone contact within two business days of discharge was the greatest predictor of patients attending an IPTC appointment. These results demonstrate that an interprofessional approach to transitions in care effectively addresses this high risk for error and high cost time in the continuum of care

How We Close the Gaps: Our Interprofessional Team Approach to Meeting Quality Measures

Define the role/function of an interprofessional team in the management of complex outpatients. Identify the types of patients that would benefit most from a team-based approach. Implement elements of our team-based patient care model into individual practices

Constructing Multivariate Survival Trees: The MST Package for R

Multivariate survival trees require few statistical assumptions, are easy to interpret, and provide meaningful diagnosis and prediction rules. Trees can handle a large number of predictors with mixed types and do not require predictor variable transformation or selection. These are useful features in many application fields and are often required in the current era of big data. The aim of this article is to introduce the R package MST. This package constructs multivariate survival trees using marginal model and frailty model based approaches. It allows the user to control and see how the trees are constructed. The package can also simulate high-dimensional, multivariate survival data from marginal and frailty models

Application of Finite-Time and Control Thermodynamics to Biological Processes at Multiple Scales

An overall synthesis of biology and non-equilibrium thermodynamics remains a challenge at the interface between the physical and life sciences. Herein, theorems from finite-time and control thermodynamics are applied to biological processes to indicate which biological strategies will succeed over different time scales. In general, living systems maximize power at the expense of efficiency during the early stages of their development while proceeding at slower rates to maximize efficiency over longer time scales. The exact combination of yield and power depends upon the constraints on the system, the degrees of freedom in question, and the time scales of the processes. It is emphasized that biological processes are not driven by entropy production but, rather, by <i>informed exergy flow</i>. The entropy production is the generalized friction that is minimized insofar as the constraints allow. Theorems concerning thermodynamic path length and entropy production show that there is a direct tradeoff between the efficiency of a process and the process rate. To quantify this tradeoff, the concepts of <i>compensated heat</i> and <i>waste heat</i> are introduced. Compensated heat is the exergy dissipated, which is necessary for a process to satisfy constraints. Conversely, waste heat is exergy that is dissipated as heat, but does not provide a compensatory increase in rate or other improvement. We hypothesize that it is waste heat that is minimized through natural selection. This can be seen in the strategies employed at several temporal and spatial scales, including organismal development, ecological succession, and long-term evolution. Better understanding the roles of compensated heat and waste heat in biological processes will provide novel insight into the underlying thermodynamic mechanisms involved in metabolism, ecology, and evolution