News

Scientists In 166 Countries Reading Optimal Data Analysis (12/11/2017)

The countries are listed at the bottom of the About page.

ODA Laboratory and Business Office Relocating (12/9/2017)

The Laboratory and Business Office are suspending operations beginning Thursday, December 14, 2017, and will resume operations on Monday, February 12, 2018.

University Pricing for Maximizing Predictive Accuracy (PDF Version), and for MegaODA and CTA software (12/9/2017)

Please see the Resources page.

New Articles (12/9/2017)

Wang, W.C., Pestana, M.H., & Moutinho, L. (2018). The effect of emotions on brand recall by gender using voice emotion response with optimal data analysis. In: Moutinho L., Sokele M. (Eds), Innovative Research Methodologies in Management. Palgrave Macmillan, Cham. DOI: 10.1007/978-3-319-64400-4_5

Grand, L.A., et al. (2017). Identification of habitat controls on northern red-legged frog populations: Implications for habitat conservation on an urbanizing landscape in the Pacific Northwest. Ecological Processes.

Linden, A., et al. (2017). Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting. Journal of Evaluation in Clinical Practice.

Linden, A., et al. (2017). Identifying causal mechanisms in health care interventions using classification tree analysis. Journal of Evaluation in Clinical Practice. DOI: 10.1111/jep.12848

Linden, A., et al. (2017). Minimizing imbalances on patient characteristics between treatment groups in randomized trials using classification tree analysis. Journal of Evaluation in Clinical Practice. DOI: 10.1111/jep.12792

Linden, A., et al. (2017). Modeling time-to-event (survival) data using classification tree analysis. Journal of Evaluation in Clinical Practice. DOI: 10.1111/jep.12779

Sabol, R., et al. (2017). Melanoma complicating treatment with natalizumab for multiple sclerosis: A report from the Southern Network on Adverse Reactions. Cancer Medicine.

Linden, A., et al. (2017). Using classification tree analysis to generate propensity score weights. Journal of Evaluation in Clinical Practice. DOI: 10.1111/jep.12744

Lavigne, J.V., et al. (2017). Age 4 predictors of oppositional defiant disorder in early grammar school. Journal of Clinical Child & Adolescent Psychology. DOI: 10.1080/15374416.2017.1280806

Kiguradze. T., et al. (2017). Persistent erectile dysfunction in men exposed to the 5α-reductase inhibitors, finasteride, or dutasteride. PeerJ, 5: e3020 https://doi.org/10.7717/peerj.3020

Oguoma, V.M., et al. (2016). Maximum accuracy obesity indices for screening metabolic syndrome in Nigeria: A consolidated analysis of four cross-sectional studies. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 10, 121-127.

Noxon, V., et al. (2016). The benefit of a “Light Touch” in identifying melanoma in Natalizumab treated patients: A Southern Network On Adverse Reactions (Sonar) analysis based On The Tysabri Outreach Commitment To Health Registry (Touch). Value in Health, 19, A134-A135.

Leon, S.C., et al. (2016). Emergency shelter care utilization in child welfare: Who goes to shelter care? How long do they stay? American Journal of Orthopsychiatry, 86, 49-60.

Bryant, F.B. (2016). Enhancing predictive accuracy and reproducibility in clinical evaluation research. Journal of Evaluation in Clinical Practice, 22, 829-834.

Linden, A. (2016). Maximizing predictive accuracy (Book Review). Journal of Evaluation in Clinical Practice, 22, 835-838.

Linden, A., et al. (2016). Using data mining techniques to characterize participation in observational studies. Journal of Evaluation in Clinical Practice, 22, 839-847.

Linden, A., et al. (2016). Using machine learning to assess covariate balance in matching studies. Journal of Evaluation in Clinical Practice, 22, 848-854.

Linden, A., et al.  (2016). Using machine learning to identify structural breaks in single-group interrupted time series designs. Journal of Evaluation in Clinical Practice, 22, 855-859.

Linden, A., et al. (2016). Using machine learning to model dose-response relationships. Journal of Evaluation in Clinical Practice, 22, 860-867.

Linden, A., et al. (2016). Combining machine learning and matching techniques to improve causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 22, 868-874.

Linden, A., et al. (2016). Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments. Journal of Evaluation in Clinical Practice, 22, 875-885.

ODA Celebrates 75 Person-Years of Full-Time Development (9/5/2017)

Today Paul Yarnold and Robert Soltysik celebrate 38 years of collaborative development of the ODA paradigm. Their collaboration began in 1979, while they were attending an outdoor presentation of Fiddler On The Roof.

“New scientific ideas never spring from a communal body, however organized, but rather from the head of an individually inspired researcher who struggles with his problems in lonely thought and unites all his thought on one single point which is his whole world for the moment.”  [From address by Max Planck, the father of Quantum Mechanics, on the 25th anniversary of the Kaiser-Wilhelm Gesellschaft (January 1936), as quoted in Surviving the Swastika : Scientific Research in Nazi Germany (1993) ISBN 0-19-507010-0.]

“A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”  [Max Planck: Wissenschaftliche Selbstbiographie. Mit einem Bildnis und der von Max von Laue gehaltenen Traueransprache.Johann Ambrosius Barth Verlag (Leipzig 1948), p. 22, as translated in Scientific Autobiography and Other Papers, trans. F. Gaynor (New York, 1949), pp. 33–34 (as cited in T. S. Kuhn, The Structure of Scientific Revolutions).]

PDF Copy of Maximizing Predictive Accuracy now Available (12/11/2016)

PDF version of Maximizing Predictive Accuracy available in Resources.