Scientists In 158 Countries Reading Optimal Data Analysis (6/19/2017)

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

Prices Reduced for Maximizing Predictive Accuracy (PDF Version), and for MegaODA and CTA software (6/19/2017)

Please see the Resources page.

New Articles (5/19/2017)

Linden, A., et al. (In Press). Using classification tree analysis to model time-to-event (survival) data. Journal of Evaluation in Clinical Practice.

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

Linden, A., et al. (In Press). Using classification tree analysis to generate propensity score weights. Journal of Evaluation in Clinical Practice.

Lavigne, J.V., et al. (In Press). 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

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.

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

PDF version of Maximizing Predictive Accuracy available in Resources.

ODA Celebrates 74 Person-Years of Full-Time Development (9/5/2016)

Today Paul Yarnold and Robert Soltysik celebrate 37 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).]