The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk

A new generalized ridge regression shrinkage path is proposed that is as short as possible under the restriction that it must pass through the vector of regression coefficient estimators that make the overall Optimal Variance-Bias Trade-Off under Normal distribution-theory. Five distinct types of ridge TRACE displays plus other graphics for this efficient path are motivated and illustrated here. These visualizations provide invaluable data-analytic insights and improved self-confidence to researchers and data scientists fitting linear models to ill-conditioned (confounded) data.Comment: 21 pages, 9 figures. arXiv admin note: substantial text overlap with withdrawn arXiv:2005.1429

Another Example of Nonparametric Generalized Ridge Regression

The Two-Stage approach to optimal \textit{non-linear} predictions via Generalized Ridge Regression is again illustrated. This time, we use a model with six $x-$predictors and more than $2,500$ observations. Unbiased estimates and predictions are then compared with their corresponding ``optimally biased'' estimates and predictions most likely to have minimal MSE risk under Normal distribution theory. Again, we find that lower residual standard errors and lower MSE risks relative to those lower errors result.Comment: 9 pages, 4 Figures, 3 Tables, 11 References. arXiv admin note: substantial text overlap with arXiv:2305.0823

LocalControl: An R Package for Comparative Safety and Effectiveness Research

The LocalControl R package implements novel approaches to address biases and confounding when comparing treatments or exposures in observational studies of outcomes. While designed and appropriate for use in comparative safety and effectiveness research involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. LocalControl is an open-source tool for researchers whose aim is to generate high quality evidence using observational data. The package implements a family of methods for non-parametric bias correction when comparing treatments in observational studies, including survival analysis settings, where competing risks and/or censoring may be present. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups

Antidepressent Treatment for Depression: Total Charges and Therapy Duration

Background: The economic costs of depression are significant, both the direct medical costs of care and the indirect costs of lost productivity. Empirical studies of antidepressant costeffectiveness suggest that the use of selective serotonin reuptake inhibitors (SSRI) may be no more costly than tricyclic antidepressants (TCA), will improve tolerability, and is associated with longer therapy duration. However the success of depression care usually involves multiple factors, including source of care, type of care, and patient characteristics, in addition to drug choice. The cost-effective mix of antidepressant therapy components is unclear. Aims of the Study: Our study evaluates cost and antidepressant-continuity outcomes for depressed patients receiving antidepressant therapy. Specifically, we determined the impact of provider choice for initial care, concurrent psychotherapy, and choice of SSRI versus TCA-based pharmacotherapies on the joint outcome of low treatment cost and continuous antidepressant therapy. Methods: A database of private health insurance claims identifies 2,678 patients who received both a diagnosis of depression and a prescription for an antidepressant during 1990–1994. Patients each fall into one of four groups according to whether their health care charges are high versus low (using the median value as the break point) and by whether their antidepressant usage pattern is continuous versus they discontinued pharmacotherapy early (filling fewer than six prescriptions). A bivariate probit model controlling for patient characteristics, co-morbidities, type of depression and concurrent treatment is the primary multivariate statistical vehicle for cost-effective treatment situation. Results: SSRIs substantially reduce the incidence of patients discontinuing pharmacotherapy while leaving charges largely unchanged. The relative effectiveness of SSRIs in depression treatment is independent of the patient’s personal characteristics and dominates the consequences of other treatment dimensions such as seeing a mental health specialist and receiving concurrent psychotherapy. Initial provider specialty is irrelevant to the continuity of pharmacotherapy, and concurrent psychotherapy creates a tradeoff through reduced pharmacotherapy interruption with higher costs. Discussion: Longer therapy duration is associated with SSRI-based pharmacotherapy (relative to TCA-based pharmacotherapy) and with concurrent psychotherapy. High cost is associated with concurrent psychotherapy and choice of a specialty provider for initial care. In our study cost-effective care includes SSRI-based pharmacotherapy initiated with a non-specialty provider. Previous treatment history and other unobserved factors that might affect antidepressant choice are not included in our model. Implications for Health Care Provision: The decision to use an SSRI-based pharmacotherapy need not consider carefully the patient’s personal characteristics. Shifting depressed patients’ pharmacotherapy away from TCAs to SSRIs has the effect of improving outcomes by lowering the incidence of discontinuation of pharmacotherapy while leaving largely unchanged the likelihood of having high overall health care charges. Targeted use of concurrent psychotherapy may be additionally cost-effective. Implications for Health Policies: The interaction of various components of depression care can alter the cost-effectiveness of antidepressant therapy. Our results demonstrate a role for the non-specialty provider in initiating care and support increased use of SSRIs as first-line therapy for depression as a way of providing cost-effective care that is consistent with APA guidelines for continuous antidepressant treatment. Implications for Further Research: Further research that improves our understanding of how decisions regarding provider choice, concurrent psychotherapy, and drug choice are made will improve our understanding of the effects treatment choices on the cost-effectiveness of depression care. We have suggested that targeted concurrent psychotherapy may prove to be cost-effective; research to determine groups most likely to benefit from the additional treatment would further enable clinicians and healthcare policy makers to form a consensus regarding a model for treating depression

Item response analysis of the Positive and Negative Syndrome Scale

<p>Abstract</p> <p>Background</p> <p>Statistical models based on item response theory were used to examine (a) the performance of individual Positive and Negative Syndrome Scale (PANSS) items and their options, (b) the effectiveness of various subscales to discriminate among individual differences in symptom severity, and (c) the appropriateness of cutoff scores recently recommended by Andreasen and her colleagues (2005) to establish symptom remission.</p> <p>Methods</p> <p>Option characteristic curves were estimated using a nonparametric item response model to examine the probability of endorsing each of 7 options within each of 30 PANSS items as a function of standardized, overall symptom severity. Our data were baseline PANSS scores from 9205 patients with schizophrenia or schizoaffective disorder who were enrolled between 1995 and 2003 in either a large, naturalistic, observational study or else in 1 of 12 randomized, double-blind, clinical trials comparing olanzapine to other antipsychotic drugs.</p> <p>Results</p> <p>Our analyses show that the majority of items forming the Positive and Negative subscales of the PANSS perform very well. We also identified key areas for improvement or revision in items and options within the General Psychopathology subscale. The Positive and Negative subscale scores are not only more discriminating of individual differences in symptom severity than the General Psychopathology subscale score, but are also more efficient on average than the 30-item total score. Of the 8 items recently recommended to establish symptom remission, 1 performed markedly different from the 7 others and should either be deleted or rescored requiring that patients achieve a lower score of 2 (rather than 3) to signal remission.</p> <p>Conclusion</p> <p>This first item response analysis of the PANSS supports its sound psychometric properties; most PANSS items were either very good or good at assessing overall severity of illness. These analyses did identify some items which might be further improved for measuring individual severity differences or for defining remission thresholds. Findings also suggest that the Positive and Negative subscales are more sensitive to change than the PANSS total score and, thus, may constitute a "mini PANSS" that may be more reliable, require shorter administration and training time, and possibly reduce sample sizes needed for future research.</p

Nonparametric Generalized Ridge Regression

A Two-Stage approach enables researchers to make optimal non-linear predictions via Generalized Ridge Regression using models that contain two or more x-predictor variables and make only realistic minimal assumptions. The optimal regression coefficient estimates that result are either unbiased or most likely to have mininal MSE risk under Normal distribution theory. All necessary calculations and graphical displays are generated using current versions of CRAN R-packages. A numerical example using the "corrected" USArrests data.frame introduces and illustrates this new robust statistical methodology. While applying this strategy to regression models with several hundred observations is straight-forward, the computations required in such cases can be extensive.Comment: 9 pages, 3 figures, 3 table

Pope NEJM 2009

air quality, longevity and demographic variables for 211 US citie