Publications

Peer-Reviewed Journals that Published Articles Using ODA software, with Underscore used to Indicate Multiple Articles

Academic Emergency Medicine

Air Traffic Control Quarterly

Allergy and Asthma Proceedings

American Journal of Orthopsychiatry

American Journal of Perinatology

American Journal of Physical Medicine & Rehabilitation

American Journal of Respiratory and Critical Care Medicine

Annals of Emergency Medicine

Annals of Internal Medicine

Annals of Operations Research

Antimicrobial Agents and Chemotherapy

Applied Psychological Measurement

Archives of Internal Medicine

Behavior Therapy

Bipolar Disorders

BMC Public Health

Cancer

Cancer Medicine

Chest

Child Psychiatry & Human Development

Children and Youth Services Review

Computers and Operations Research

Criminal Justice and Behavior

Decision Sciences

Diabetes & Metabolic Syndrome: Clinical Research & Reviews

Drug Safety

Educational and Psychological Measurement

Evaluation and the Health Professions

Infection Control and Hospital Epidemiology

International Journal of Offender Therapy and Comparative Criminology

International Journal of Psychiatry in Medicine

International Journal of Radiation Oncology * Biology * Physics

JAMA (Journal of the American Medical Association)

JAMA Dermatology

JAMA Internal Medicine

JAMA Oncology

Journal of Acquired Immune Deficiency Syndrome

Journal of Affective Disorders

Journal of Applied Social Psychology

Journal of Behavioral Medicine

Journal of Child and Adolescent Psychology

Journal of Child and Adolescent Trauma

Journal of Child and Family Studies

Journal of Clinical Child & Adolescent Psychology

Journal of Clinical and Experimental Neuropsychology

Journal of Clinical Medicine

Journal of Clinical Oncology

Journal of Consulting and Clinical Psychology

Journal of Evaluation in Clinical Practice

Journal of General Internal Medicine

Journal of Head Trauma Rehabilitation

Journal of Infection

Journal of Laboratory and Clinical Medicine

Journal of Neuropsychology

Journal of Occupational and Environmental Medicine

Journal of Occupational Medicine

Journal of Patient Safety

Journal of Pediatric Psychology

Journal of Personality Assessment

Journal of Research in Personality

Journal of Psychiatric Research

Journal of Stroke and Cerebrovascular Diseases

Journal of the American Academy of Psychiatry

Journal of the American Academy of Child & Adolescent Psychiatry

Journal of the American College of Cardiology

Journal of the International Neuropsychological Society

Journal of Psychiatric Research

Journal of Thoracic Oncology

Kidney International

Lancet

Law & Human Behavior

Law & Society Review

Medical Care

New England Journal of Medicine

North American Journal of Medical Sciences

Optimal Data Analysis

Peer J

Perceptual and Motor Skills

Prevention Science

Primary Dental Care

Professional Psychology: Research and Practice

Psychology, Health, and Medicine

Psychotherapy: Theory, Research, Practice, Training

Rehabilitation Psychology

Respiratory Medicine

Schizophrenia Bulletin

Statistics in Medicine

Stroke

Subtle Energies & Energy Medicine

Supportive Care in Cancer

The Clinical Neurologist

The Clinical Neuropsychologist

The Journal of Pediatrics

The Scientific World Journal

Tijdschrift voor Gerontologie en Geriatrie

Tijdschrift voor neuropsychologie

Value in Health

Victims & offenders: An International Journal of Evidence-based Research, Policy, and Practice

Articles

Introduction

Yarnold PR (2017). What is optimal data analysis? Optimal Data Analysis6, 26-42.

Yarnold PR (2018). Visualizing application and summarizing accuracy of ODA models. Optimal Data Analysis, 7, 85-89.

Yarnold PR, Soltysik RC (2010). Optimal data analysis: A general statistical analysis paradigm. Optimal Data Analysis, 1, 10-22.

Yarnold PR (2014). “A statistical guide for the ethically perplexed” (Chapter 4, Panter & Sterba, Handbook of Ethics in Quantitative Methodology, Routledge, 2011): Clarifying disorientation regarding the etiology and meaning of the term Optimal as used in the Optimal Data Analysis (ODA) paradigm. Optimal Data Analysis, 3, 30-31.

Teaching, Learning

Bryant FB (2010). The Loyola experience (1993-2009): Optimal data analysis in the Department of Psychology. Optimal Data Analysis, 1, 4-9.

Yarnold PR (2018). Learning the ODA paradigm. Optimal Data Analysis, 7, 14-15.

Yarnold PR (2017). How to find articles in the ODA eJournal. Optimal Data Analysis6, 25.

Yarnold PR, Soltysik RC (2016). Maximizing predictive accuracy. Chicago, IL: ODA Books. DOI: 10.13140/RG.2.1.1368.3286

Yarnold PR, Soltysik RC (2005). Optimal data analysis: A guidebook with software for Windows. Washington, DC, APA Books.

Software

Yarnold PR (2018). Obtaining personal copies of ODA, MegaODA and CTA software. Optimal Data Analysis, 7, 42-43.

Soltysik RC, Yarnold PR (2013). MegaODA large sample and BIG DATA time trials: Separating the chaff. Optimal Data Analysis, 2, 194-197.

Soltysik RC, Yarnold PR (2013). MegaODA large sample and BIG DATA time trials: Harvesting the Wheat. Optimal Data Analysis, 2, 202-205.

Yarnold PR, Soltysik RC (2013). MegaODA large sample and BIG DATA time trials: Maximum velocity analysis. Optimal Data Analysis, 2, 220-221.

Soltysik RC, Yarnold PR (2010). Automated CTA software: Fundamental concepts and control commands. Optimal Data Analysis, 1, 144-160.

Bryant FB, Harrison PR (2013). How to create an ASCII input data file for UniODA and CTA software. Optimal Data Analysis, 2, 2-6.

Ebert TA, Yarnold PR (2017). Running ODAÔ software on WindowsÔ: Assigning missing values using ExcelÔ, and using longer file names. Optimal Data Analysis6, 3-4.

Yarnold PR (2018). Creating a data set from a data table. Optimal Data Analysis, 7, 12-13.

Classification Tree Analysis (CTA)

Algorithms, Measures, Methods

Yarnold PR (1996). Discriminating geriatric and non-geriatric patients using functional status information: An example of classification tree analysis via UniODA. Educational and Psychological Measurement, 56, 656-667.

Yarnold PR, Soltysik RC, Bennett CL (1997). Predicting in-hospital mortality of patients with AIDS-related Pneumocystis carinii pneumonia: An example of hierarchically optimal classification tree analysis. Statistics in Medicine, 16, 1451-1463. [See Also: Yarnold PR (2014). “Predicting in-hospital mortality of patients with aids-related Pneumocystis carinii pneumonia: An example of hierarchically optimal classification tree analysis”: Corrected Illustration of CTA Model. Optimal Data Analysis, 3, 28-29.]

Yarnold PR, Soltysik RC (2010). Maximizing the accuracy of classification trees by optimal pruning. Optimal Data Analysis, 1, 23-29.

Yarnold PR (2010). Unconstrained covariates in CTA. Optimal data Analysis, 1, 38-40.

Yarnold PR (2013). Assessing hold-out validity of CTA models using UniODA. Optimal Data Analysis, 2, 31-36.

Yarnold PR, Soltysik RC (2013). Reverse CTA: an optimal analog to analysis of variance. Optimal Data Analysis, 2, 43-47.

Yarnold PR (2015). Selecting the minimum denominator in manual and enumerated CTA. Optimal Data Analysis4, 14-20.

Yarnold PR, Bryant FB (2015). Obtaining a hierarchically optimal CTA model via UniODA software. Optimal Data Analysis4, 36-53.

Yarnold PR, Bryant FB (2015). Obtaining an enumerated CTA model via automated CTA software. Optimal Data Analysis4, 54-60.

Yarnold PR (2015). ESS as an index of decision consistency. Optimal Data Analysis4, 197.

Yarnold PR (2015). Knowing (ESS) and not knowing (D). Optimal Data Analysis4, 198.

Yarnold PR (2016). Using UniODA to determine the ESS of a CTA model in LOO analysis. Optimal Data Analysis5, 3-10.

Yarnold PR (2016). Determining jackknife ESS for a CTA model with chaotic instability. Optimal Data Analysis5, 11-14.

Yarnold PR (2016). Maximizing overall percentage accuracy in classification: Discriminating study groups in the National Pressure Ulcer Long-Term Care Study (NPULS). Optimal Data Analysis5, 29-30.

Yarnold PR (2016). Ascertaining intervention efficacy. Optimal Data Analysis5, 31-36.

Yarnold PR (2016). Pruning CTA models to maximize PAC. Optimal Data Analysis5, 58-61.

Yarnold PR (2016). How many EO-CTA models exist in my sample and which is the best model? Optimal Data Analysis5, 62-64.

Yarnold PR (2016). Restricted vs. unrestricted optimal analysis: Smoking behavior of college undergraduates. Optimal Data Analysis5, 124-128.

Yarnold PR (2018). CTA models and staging tables. Optimal Data Analysis, 7, 19-22.

Yarnold PR (2018). Obtaining LOO p in analysis involving three or more class categories. Optimal Data Analysis, 7, 40-41.

Yarnold PR (2016). Assessing hold-out validity of models of smoking behavior developed for male Anglo-American college undergraduates applied to classify comparable Mexican-American and Indian-American samples. Optimal Data Analysis5, 133-135.

Yarnold PR (2018). Objective functions optimized in ODA. Optimal Data Analysis, 7, 10-11.

Yarnold PR (2019). When to evaluate a nonlinear model. Optimal Data Analysis, 8, 14-20.

Yarnold PR (2019). Growing Classification Tree Models on the Basis of a Priori Performance Criteria. Optimal Data Analysis, 8, 30-32.

Applications

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

Lyons JS (1997). The evolving role of outcomes in managed health care. Journal of Child and Family Studies6, 1-8.

Ostrander R, Weinfurt KP, Yarnold PR, August G (1998).  Diagnosing attention deficit disorders using the BASC and the CBCL: Test and construct validity analyses using optimal discriminant classification trees.  Journal of Consulting and Clinical Psychology, 66, 660-672.

Yarnold PR, Michelson EA, Thompson DA, Adams SL (1998). Predicting patient satisfaction: A study of two emergency departments. Journal of Behavioral Medicine, 21, 545-563.

Feinglass J, Yarnold PR, Martin GJ, McCarthy WJ. (1998). A classification tree analysis of selection for discretionary treatment. Medical Care, 36, 740-747.

Collinge W, Yarnold PR, Raskin E (1998). Use of mind/body self-healing practice predicts positive health transition in chronic fatigue syndrome: A controlled study. Subtle Energies & Energy Medicine, 9, 171-190.

Kanter, AS, Spencer DC, Steinberg MH, Soltysik RC, Yarnold PR, Graham NM (1999).  Supple­mental vitamin B and progression to AIDS and death in black South African patients infected with HIV. Journal of Acquired Immune Deficiency Syndrome, 21, 252-257.

Kucera CM, Greenberger PA, Yarnold PR, Choy AC, Levenson T. (1999). An attempted prospective testing of an asthma severity index and a quality of life survey for 1 year in ambulatory patients with asthma. Allergy and Asthma Proceedings, 20, 29-38.

Mueser KT, Yarnold PR, Rosenberg SD, Drake RE, Swett C, Miles KM, Hill D. (2000). Substance use disorder in hospitalized severely mentally ill psychiatric patients: Prevalence, correlates, and subgroups. Schizophrenia Bulletin, 26, 179-193.

Arozullah AM, Yarnold PR, Weinstein RA, Nwadiaro N, McIlraith TB, Chmiel JS, Sipler AM, Chan C, Goetz MB, Schwartz D, Bennett CL (2000). A new preadmission staging system for predicting in-patient mortality from HIV-associated Pneumocystis carinii pneumonia in the early-HAART era. American Journal of Respiratory and Critical Care Medicine, 161, 1081-1086.

Donenberg GR, Bryant FB, Emerson E, Wilson HW, Pasch KE (2003). Tracing the roots of early sexual debut among adolescents in psychiatric care. Journal of the American Academy of Psychiatry, 42, 594-608.

Green D, Hartwig D, Chen D, Soltysik RC, Yarnold PR (2003). Spinal cord injury risk assessment for thromboembolism (SPIRATE Study). American Journal of Physical Medicine and Rehabilitation, 12, 950-956.

Arozullah AM, Parada J, Bennett CL, Deloria-Knoll M, Chmiel JS, Phan L, Yarnold PR (2003). A rapid staging system for predicting mortality from HIV-associated community-acquired pneumonia. Chest, 123, 1151-1160.

Layden BL, Minadeo N, Suhy J, Abukhdeir AM, Metreger T, Foley K, Borge G, Crayton JW, Bryant FB, Mota de Freitas D. (2004). Biochemical and psychiatric predictors of Li+ response and toxicity in Li+-treated bipolar patients. Bipolar Disorders, 6, 53-61.

Stalans LJ, Yarnold PR, Seng M, Olson DE, Repp M. (2004). Identifying three types of violent offenders and predicting violent recidivism while on probation: A classification tree analysis. Law & Human Behavior, 28, 53-271.

Zakarija A, Bandarenko N, Pandey DK, Auerbach A, Raisch D, Kim B, Kwaan H, McKoy J, Schmitt B, Davidson C, Yarnold PR, Gorelick P, Bennett CL (2004). Clopidogrel-associated thrombotic thrombocytopenic purpura (TTP): An update of pharmacovigilance efforts conducted by independent researchers, the pharmaceutical suppliers, and the Food and Drug Administration. Stroke, 35, 533-538.

Taft CT, Pless AP, Stalans LJ, Koenen KC, King LA, King DW (2005). Risk factors for partner violence among a national sample of combat veterans. Journal of Consulting and Clinical Psychology, 73, 151-159.

Coakley RM, Holmbeck GN, Bryant FB (2006). Constructing a prospective model of psychosocial adaptation in young adolescents with spina bifida: An application of optimal data analysis. Journal of Pediatric Psychology, 31, 1084-1099.

Arozullah AM, Lee SD, Khan T, Kurup S, Ryan J, Bonner M, Soltysik RC, Yarnold PR (2006). The roles of low literacy and social support in predicting the preventability of hospital admission. Journal of General Internal Medicine, 21, 140-145.

Pachman LM, Abbott K, Sinacore JM, Amoruso L, Dyer A, Lipton R, Ilowite N, Hom C, Cawkwell G, White A, Rivas-Chacon R, Kimura Y, Ray L, Ramsey-Goldman R (2006). Duration of illness is an important variable for untreated children with juvenile dermatomyositis. The Journal of Pediatrics, 148, 247-253.

Nebeker JR, Yarnold PR, Soltysik RC, Sauer BC, Sims SA, Samore MH, Rupper RW, Swanson KM, Savitz LA, Shinogle J, Xu W (2007). Developing indicators of inpatient adverse drug events through non-linear analysis using administrative data. Medical Care, 45, S81-S88.

Snowden JA, Leon SC, Bryant FB, Lyons JS (2007). Evaluating psychiatric hospital admission decisions for children in foster care: An optimal classification tree analysis. Journal of Child and Adolescent Psychology, 36, 8-18.

Kyriacou DN, Yarnold PR, Stein AC, Schmitt BP, Soltysik RC, Nelson RR, Frerichs RR, Noskin GA, Belknap SB,  Bennett CL (2007). Discriminating inhalational anthrax from community-acquired pneumonia using chest radiograph findings and a clinical algorithm. Chest, 131, 489-495.

Cromley T, Lavigne JV (2008). Predictors and consequences of early gains in child psychotherapy. Psychotherapy: Theory, Research, Practice, Training, 45, 42-60.

Grobman WA, Terkildsen MF, Soltysik RC, Yarnold PR (2008). Predicting outcome after emergent cerclage using classification tree analysis. American Journal of Perinatology, 25, 443-448.

Kyriacou DM, Yarnold PR, Soltysik RC, Wunderink RG, Schmitt BP, Parada JP, Adams JG (2008). Derivation of a triage algorithm for chest radiography of community-acquired pneumonia in the emergency department. Academic Emergency Medicine, 15, 40-44.

Snowden J, Leon S, Sieracki J (2008). Predictors of children in foster care being adopted: A classification tree analysis. Children and Youth Services Review, 30, 1318-1327.

Belknap SM, Moore H, Lanzotti SA, Yarnold PR, Getz,M, Deitrick DL, Peterson A, Akeson J, Maurer T, Soltysik RC, Storm J (2008). Application of software design principles and debugging methods to an analgesia prescription reduces risk of severe injury from medical use of opioids. Clinical Pharmacology and Therapeutics, 84, 385-392.

Smart CM, Nelson NW, Sweet JJ, Bryant FB, Berry DTR, Granacher RP, Heilbronner RL (2008). Use of MMPI-2 to predict cognitive effort: A hierarchically optimal classification tree analysis. Journal of the International Neuropsychological Society, 14, 842-852.

Han SD, Suzuki H, Drake AI, Jak AJ, Houston WS, Bondi MW (2009). Clinical, cognitive, and genetic predictors of change in job status following traumatic brain injury in a military population. Journal of Head Trauma Rehabilitation, 24, 57-64.

Diesfeldt HFA (2009). Visuographic tests of set shifting and inhibitory control: The contribution of constructional impairments. Journal of Neuropsychology, 3, 93-105.

Lyons AM, Leon SC, Zaddach C, Luboyeski EJ, Richards M (2009). Predictors of Clinically Significant Sexual Concerns in a Child Welfare Population. Journal of Child and Adolescent Trauma, 2, 28-45.

Suzuki H, Bryant FB, Edwards JD (2010). Tracing prospective profiles of juvenile delinquency: An optimal classification tree analysis. Optimal Data Analysis, 1, 125-143.

Greenleaf RG, Flexon JL, Lurigio AJ, Snowden JA (2010). Predicting injuries of women in episodes of intimate partner violence: Individual and composite risk factors. Victims & offenders: An International Journal of Evidence-based Research, Policy, and Practice, 5, 101-119.

Alshekhlee A, Ranawat N, Syed TU, Conway D, Ahmad SA, Zaiday OO (2010). National Institutes of Health Stroke Scale assists in predicting the need for percutaneous endoscopic gastrostomy tube placement in acute ischemic stroke. Journal of Stroke and Cerebrovascular Diseases19, 347-352.

Han SD, Suzuki H, Jak AJ, Chang YL, Salmon DP, Bondi MW (2010). Hierarchical cognitive and psychosocial predictors of amnestic mild cognitive impairment. Journal of the International Neuropsychological Society, 16, 721-729.

Smith JH, Bryant FB, Njus D, Posavac EJ (2010). Here today, gone tomorrow: Understanding freshman attrition using Person-Environment Fit Theory. Optimal Data Analysis, 1, 101-124.

Lavigne JV, LeBailly SA, Gouze KR, Binns HJ, Keller J, Pate L (2010). Predictors and correlates of completing behavioral parent training for the treatment of oppositional defiant disorder in pediatric primary care. Behavior Therapy, 41, 198-211.

Dunleavy AM, Leon SC (2011). Predictors for resolution of antisocial behavior among foster care youth receiving community-based services. Children and Youth Services Review, 33, 2347-2354.

Rupert PA, Miller AO, Tuminello-Hartman ER, Bryant FB (2012). Predictors of career satisfaction among practicing psychologists. Professional Psychology: Research and Practice, 43, 495-502.

Rhode P, Stice E, Gau JM (2012). Effects of three depression prevention interventions on risk for depressive disorder onset in the context of depression risk factors. Prevention Science, 13, 584-593.

Collinge W, Kahn J, Walton T, Kozak L, Bauer-Wu S, Fletcher K, Yarnold PR, Soltysik RC (2013). Touch, Caring, and Cancer: Randomized controlled trial of a multimedia caregiver education program. Supportive Care in Cancer, 21, 1405-1414.

Pape TLB, Guernon A, Lundgren S, Patil V, Herrold AA, Smith B, Blahnik M, Picon LM, Harton B, Peterson M, Mallinson T, Hoffman M. (2013). Predicting levels of independence with expressing needs and ideas 1 year after severe brain injury. Rehabilitation Psychology, 58, 253-262.

Yarnold PR (2013). Initial use of hierarchically optimal classification tree analysis in medical research. Optimal Data Analysis, 2, 7-18.

Frazier TW, Youngstrom EA, Fristad MA, Demeter C, Birmaher B, Kowatch RA, Arnold LE, Axelson D, Gill MK, Horwitz SM, Findling RL (2014). Improving clinical prediction of bipolar spectrum disorders in youth. Journal of Clinical Medicine3, 218-232.

Soltysik RC, Yarnold PR (2014). Hierarchically optimal classification tree analysis of adverse drug reactions secondary to warfarin therapy. Optimal Data Analysis, 3, 23-24.

Hill RM, Pettit JW, Lewinson PM, Seeley JR, Klein DN (2014). Escalation to major depressive disorder among adolescents with subthreshold depressive symptoms: Evidence of distinct subgroups at risk. Journal of Affective Disorders158, 133-138.

Bryant FB, Yarnold PR (2014). Type A behavior, pessimism and optimism among college undergraduates. Optimal Data Analysis3, 32-35.

Bryant FB, Yarnold PR (2014). Finding joy in the past, present, and future: The relationship between Type A behavior and savoring beliefs among college undergraduates. Optimal Data Analysis3, 36-41.

Bryant FB, Yarnold PR (2014). Type A Behavior and savoring among college undergraduates: Enjoy achievements now—not later. Optimal Data Analysis, 3, 25-27.

Lavigne JV, Dahl KP, Gouze KR, LeBailly SA, Hopkins J (2015). Multi-Domain Predictors of Oppositional Defiant Disorder Symptoms in Preschool Children: Cross-Informant Differences. Child Psychiatry & Human Development46, 308-319. 

Sieracki JH, Fuller AK, Leon SC, Jhe Bai G, Bryant FB (2015). The role of race, socioeconomic status, and System of Care services in placement decision-making, Children and Youth Services Review50, 3-11.

Stoner AM, Leon SC, Fuller AK (2015). Predictors of reduction in symptoms of depression for children and adolescents in foster care. Journal of Child and Family Studies, 24, 784-797.

Leon SC, Jhe Bai G, Fuller AK, Busching M (2016).
Emergency shelter care utilization
in child welfare: Who goes to shelter care? How long do they stay? American Journal of Orthopsychiatry86, 49-60.

Oguoma VM, Nwose EU, Ulasi II, Akintunde AA, Chukwukelu EE, Bwititi PT, Richards RS, Skinner TC (2017). Cardiovascular disease risk factors in a Nigerian population with impaired fasting blood glucose level and diabetes mellitus. BMC Public Health, 17:36 (DOI 10.1186/s12889-016-3910-3).

Lavigne JV, Bryant FB, Hopkins J, Gouze KR (2017). Age 4 predictors of Oppositional Defiant Disorder in early grammar school.  Journal of Clinical Child & Adolescent Psychology, 1-15. DOI: 10.1080/15374416.2017.1280806

Kiguradze T, Temps WH, Yarnold PR, Cashy J, Brannigan RE, Nardone B, Micali G, West DP, Belknap SM (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

Grand LA, Hayes MP, Vogt KA, Vogt DJ, Yarnold PR, Richter KO, Anderson CD, Ostergaard EC, Wilhelm JO (2018). Identifica­tion of habitat controls on northern red-legged frog populations: Implications for habitat con­servation on an urbanizing landscape in the Pacific Northwest. Ecological Processes. DOI 10.1186/s13717-017-0111-7

Chi-Square, Sign Test

Yarnold PR (2016). CTA vs. not chi-square: Fear and specific recommendations do synergistically affect behavior. Optimal Data Analysis5, 108-111.

Yarnold PR (2016). CTA vs. not chi-square: Differentiating statistical and ecological signifi­cance. Optimal Data Analysis5, 112-115.

Yarnold PR (2016). CTA vs. chi-square: Differentiating statistical and ecological significance. Optimal Data Analysis5, 116-117.

Yarnold PR (2016). CTA vs. disintegrated chi-square: Integrated vs. piecemeal analysis. Optimal Data Analysis5, 118-120.

Yarnold PR (2016). CTA vs. chi-square: Comparing voter sentiment in political wards. Optimal Data Analysis5, 129-130.

Yarnold PR (2016). CTA vs. non-disentangled omnibus chi-square: Comparing samples (not) selected for study participation. Optimal Data Analysis5, 154-157.

Yarnold PR (2016). Using EO-CTA to disentangle sets of sign-test-based multiple-comparisons. Optimal Data Analysis5, 158-159.

Interrupted Time Series, Single-Case Series

Linden A, Yarnold PR (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, Yarnold PR (2018). Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis. Journal of Evaluation in Clinical Practice, 24, 740-744.

Linden A, Yarnold PR (2018). The Australian Gun Buyback Program and rate of suicide by Firearm. Optimal Data Analysis, 7, 28-35.

Diesfeldt HFA (2010). Theoriegestuurde disgnostiek van spraak- en taalpathologie bij dementie (Theory-driven diagnosis of speech and language pathology in dementia-Adapted from the speech delivered at the presentation of the 1st Betto Deelmanprijs). Tijdschrift voor neuropsychologie5.

Diesfeldt HFA (2011). Lezen als venster op het taalsysteem (Read as a window on the language system). Tijdschrift voor neuropsychologie6.

Diesfeldt HFA (2011). Du fonologische variant van primaire progressieve afasie, een gevalsstudie (The phenomenal variane of primary progressive aphasia-a single case study). Tijdschrift voor Gerontologie en Geriatrie, 42, 79-90.

Yarnold PR (2013). Statistically significant increases in crude mortality rate of North Dakota counties occurring after massive environmental usage of toxic chemicals and biocides began there in1998: An optimal static statistical map. Optimal Data Analysis, 2, 98-105.

Yarnold PR (2013). The most recent, earliest, and Kth significant changes in an ordered series: Traveling backwards in time to assess when annual crude mortality rate most recently began increasing in McLean County, North Dakota. Optimal Data Analysis, 2, 143-147.

Yarnold PR (2013). How to create a data set with SAS™ and compare attributes with UniODA™ in serial single-case designs. Optimal Data Analysis, 2, 157-158.

Yarnold PR (2013). Determining when annual crude mortality rate most recently began increasing in North Dakota counties, I: Backward-stepping little jiffy. Optimal Data Analysis, 2, 217-219.

Manual vs. Automated

Yarnold PR, Soltysik RC (2010). Manual vs. automated CTA: Optimal preadmission staging for inpatient mortality from Pneumocystit cariini pneumonia. Optimal Data Analysis, 1, 50-54.

Coakley RM, Holmbeck GN, Bryant FB, Yarnold PR (2010). Manual vs. automated CTA: Predicting adolescent psychosocial adaptation. Optimal Data Analysis, 1, 55-58.

Yarnold PR, Bryant FB, Smith JH (2013). Manual vs. Automated CTA: Predicting Freshman Attrition. Optimal Data Analysis, 2, 48-53.

Multicategorical Attributes

Yarnold PR, Bryant FB (2013). Analysis involving categorical attributes having many categories. Optimal Data Analysis, 2, 69-70.

Yarnold PR (2013). Analyzing categorical attributes having many response categories. Optimal Data Analysis, 2, 172-176.

Yarnold PR (2013). Univariate and multivariate analysis of categorical attributes with many response categories. Optimal Data Analysis, 2, 177-190.

Propensity Scores

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

Yarnold PR, Linden A (2017). Computing propensity score weights for CTA models involving perfectly predicted endpoints. Optimal Data Analysis6, 43-46.

Linden A, Yarnold PR (2017). Using classifi­cation tree analysis to generate propensity score weights. Journal of Evaluation in Clinical Practice, 23, 703-712.

Randomized Controlled Studies, Matching

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

Linden A, Yarnold PR (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, Yarnold PR (2017). Minimizing imbalances on patient characteristics between treatment groups in randomized trials using classification tree analysis. Journal of Evaluation in Clinical Practice, 23, 1309-1315.

Linden A, Yarnold PR (2018). Using ODA in the evaluation of randomized controlled studies. Optimal Data Analysis, 7, 46-49.

Linden A, Yarnold PR (2018). Using ODA in the evaluation of randomized controlled studies: Application to survival outcomes. Optimal Data Analysis, 7, 50-53.

Survival Analysis

Linden A, Yarnold PR (2017). Modeling time-to-event (survival) data using classification tree analysis. Journal of Evaluation in Clinical Practice, 23, 1299-1308.

Linden A, Yarnold PR (2018). Estimating causal effects for survival (time-to-event) out­comes by combining classification tree analysis and propensity score weighting. Journal of Evaluation in Clinical Practice, 24, 380-387.

Coding, Residuals, Missing Data, Increasing Validity/Reproducibility, Measure Precision

Yarnold PR (2010). Aggregated vs. referenced categorical attributes in UniODA and CTA. Optimal Data Analysis, 1, 46-49.

Yarnold PR (2014). “Breaking-up” an ordinal variable can reduce model classification accuracy. Optimal Data Analysis, 3, 19.

Yarnold PR, Bryant FB (2014). The role of residuals in optimal and suboptimal statistical modeling. Optimal Data Analysis4, 9-11.

Yarnold PR (2014). Triage algorithm for chest radiography for community-acquired pneumonia of Emergency Department patients: Missing data cripples research. Optimal Data Analysis3, 102-106.

Yarnold PR (2014). Increasing the validity and reproducibility of scientific findings. Optimal Data Analysis3, 107-109.

Yarnold PR (2018). Minimize usage of binary measurement scales in rigorous classical research. Optimal Data Analysis, 7, 3-9.

Markov

Yarnold PR (2016). GenODA structural decomposition vs. log-linear model of one-step Markov transition data: Stability and change in male geographic mobility in 1944-1951 and 1951-1953. Optimal Data Analysis5, 213-215.

Yarnold PR (2017). Novometric analysis of transition matrices to ascertain Markovian order. Optimal Data Analysis6, 5-8.

Yarnold PR (2017). Novometric comparison of Markov transition matrices for heterogeneous populations. Optimal Data Analysis6, 9-12.

Yarnold PR (2018). Using ODA to confirm a first order Markov steady state process. Optimal Data Analysis, 7, 72-73.

Yarnold PR (2018). Using ODA to determine if a Markov transition process is second order. Optimal Data Analysis, 7, 74-75.

Yarnold PR (2018). Using ODA to ascertain if stratification yielded different transition matrices. Optimal Data Analysis, 7, 76-77.

Yarnold PR (2018). Initial research using ODA in Markov process modelling. Optimal Data Analysis, 7, 90-91.

Yarnold PR (2019). Maximum-precision Markov transition table: Successive daily change in closing price of a utility stock. Optimal Data Analysis, 8, 3-10.

MultiODA

Yarnold PR, Soltysik RC, Martin GJ (1994). Heart rate variability and susceptibility for sudden cardiac death: An example of multivariable optimal discriminant analysis. Statistics in Medicine, 13, 1015-1021.

Soltysik RC, Yarnold PR (1994). The Warmack-Gonzalez algorithm for linear two-category multivariable optimal discriminant analysis. Computers and Operations Research, 21, 735-745.

Yarnold PR, Soltysik RC, McCormick WC, Burns R, Lin EHB, Bush T, Martin GJ (1995). Application of multivariable optimal discriminant analysis in general internal medicine. Journal of General Internal Medicine, 10, 601-606.

Yarnold PR, Soltysik RC, Lefevre F, Martin GJ (1998). Predicting in-hospital mortality of patients receiving cardiopulmonary resuscitation: Unit-weighted MultiODA for binary data. Statistics in Medicine, 17, 2405-2414.

Bennett CL, Kim B, Zakarija A, Bandarenko N, Pandey DK, Buffie CG, McKoy JM, Tevar AD, Cursio JF, Yarnold PR, Kwaan HC, Masi DD, Sarode R, Raife TJ, Kiss JE, Raisch DW, Davidson C, Sadler JE, Ortel TL, Zheng XL, Kato S, Matsumoto M, Uemura M, Fujimura Y (2007). Two mechanistic pathways for thienopyridine-associated thrombotic thrombocytopenic purpura: A report from the Surveillance, Epidemiology, and Risk Factors for Thrombotic Thrombocytopenic Purpura (SERF-TTP) Research Group and the Research on Adverse Drug Events and Reports (RADAR) Project. Journal of American College of Cardiology, 50, 1138-1143.

Soltysik RC, Yarnold PR (2010). Two-group MultiODA: Mixed-integer linear programming solution with bounded M. Optimal Data Analysis, 1, 31-37.

Novometric Analysis

Algorithms, Measures, Methods

Yarnold PR, Soltysik RC (2014). Globally optimal statistical classification models, I: Binary class variable, one ordered attribute. Optimal Data Analysis3, 55-77.

Yarnold PR, Soltysik RC (2014). Globally optimal statistical classification models, II: Unrestricted class variable, two or more attributes. Optimal Data Analysis3, 78-84.

Yarnold PR (2014). Illustrating how 95% confidence intervals indicate model redundancy. Optimal Data Analysis, 3, 96-97.

Yarnold PR (2018). Comparing exact discrete 95% CIs for model vs. chance ESS to evaluate statistical significance. Optimal Data Analysis, 7, 82-84.

Yarnold PR, Soltysik RC (2014). Discrete 95% confidence intervals for ODA model- and chance-based classifications. Optimal Data Analysis3, 110-112.

Yarnold PR (2015). Distance from a theoretically ideal statistical classification model defined as the number of additional equivalent effects needed to obtain perfect classification for the sample. Optimal Data Analysis4, 81-86.

Yarnold PR (2016). Identifying the descendant family of HO-CTA models by using the minimum denominator selection algorithm: Maximizing ESS versus PAC. Optimal Data Analysis5, 53-57.

Yarnold PR (2016). Novometric theorem generalized to unrestricted class variables. Optimal Data Analysis5, 102-103.

Yarnold PR (2016). Refined definition of the descendant family in novometric theory. Optimal Data Analysis5, 153.

Yarnold PR, Linden A (2016). Theoretical aspects of the D statistic. Optimal Data Analysis5, 171-174.

Yarnold PR (2017). Novometric pairwise comparisons in consolidated temporal series. Optimal Data Analysis6, 18-24.

Yarnold PR (2019). The structure of perfect optimal models with a two-category class variable and four or fewer endpoints. Optimal Data Analysis, 8, 21-25.

Applications

Linden A, Yarnold PR (2018). Identifying causal mechanisms in health care interventions using classification tree analysis. Journal of Evaluation in Clinical Practice, 24, 353-361.

Yarnold PR (2014). What influences patients to recommend an Emergency Department to others? Optimal Data Analysis3, 85-88.

Yarnold PR (2014). Increasing the likelihood of an ambivalent patient recommending the Emergency Department to others, Optimal Data Analysis3, 89-91.

Yarnold PR (2014). What most dissatisfies Emergency Department patients? Optimal Data Analysis3, 92-95.

Yarnold PR (2014). What most satisfies Emergency Department patients? Optimal Data Analysis3, 98-101.

Yarnold PR (2015). Gender and psychology concentration for graduate students. Optimal Data Analysis4, 131-134.

Yarnold PR (2015). Predicting divorce: The role of gender, and of pre- and extra-marital sex. Optimal Data Analysis4, 175-176.

Yarnold PR (2015). Globally-optimal CTA model of World War II recruit training camp location preference. Optimal Data Analysis4, 182-183.

Yarnold PR (2015). Globally-optimal CTA model of voting for US Senators. Optimal Data Analysis4, 186-187.

Yarnold PR (2015). Occupational class, tenure, and voting. Optimal Data Analysis4, 188-189.

Yarnold PR (2016). Parental smoking behavior, ethnicity, gender, and the cigarette smoking behavior of high school students. Optimal Data Analysis5, 136-140.

Yarnold PR (2016). Using gender of an imaginary rated smoker, and subject’s gender, ethnicity, and smoking behavior to identify perceived differences in peer-group smoking standards of American high school students. Optimal Data Analysis5, 141-143.

Yarnold PR (2016). Would one’s best boy- or girl-friend be more upset if one began smoking: An exploratory GenODA model for Anglo-, Mexican-, and Indian-American college undergraduates. Optimal Data Analysis5, 144-145.

Yarnold PR (2016). Novometric models of smoking habits of male and female friends of American college undergraduates: Gender, smoking, and ethnicity. Optimal Data Analysis5, 146-150.

Yarnold PR (2016). Predicting daily television viewing of senior citizens using education, age and marital status. Optimal Data Analysis5, 151-152.

Yarnold PR (2016). Comparing WAIS-R qualitative information for people 75 years and older, with vs. without brain damage. Optimal Data Analysis5, 166-170.

Yarnold PR (2016). Comparing MMPI-2 F-K Index normative data among male and female psychiatric and head-injured patients, individuals seeking disability benefits, police and priest job applicants, and substance abusers. Optimal Data Analysis5, 186-193.

Yarnold PR (2016). Novometric analysis predicting voter turnout: Race, education, and organizational membership status. Optimal Data Analysis5, 194-197.

Yarnold PR (2017). Novometric comparison of patient satisfaction with nurse responsiveness over successive quarters. Optimal Data Analysis6, 13-17.

Versus

Yarnold PR (2018). Optimal analyses in cohort tables. Optimal Data Analysis, 7, 78-81.

Yarnold PR (2016). Novometric statistical analysis and the Pearson-Yule debate. Optimal Data Analysis5, 162-165.

Yarnold PR, Bennett CL (2016). Novometrics vs. correlation: Age and clinical measures of PCP survivors, Optimal Data Analysis5, 74-78.

Yarnold PR (2016). Novometrics vs. regression analysis: Literacy, and age and income, of ambulatory geriatric patients. Optimal Data Analysis5, 83-85.

Yarnold PR (2016). Novometrics vs. regression analysis: Modeling patient satisfaction in the Emergency Room. Optimal Data Analysis5, 86-93.

Yarnold PR (2016). Novometric analysis vs. MANOVA: MMPI codetype, gender, setting, and the MacAndrew Alcoholism scale. Optimal Data Analysis5, 177-178.

Yarnold PR (2016). Novometrics vs. polychoric correlation: Number of lambs born over two years. Optimal Data Analysis5, 184-185.

Yarnold PR (2016). Novometrics vs. Yule’s Q: Voter turnout and organizational membership. Optimal Data Analysis5, 198-199.

Yarnold PR (2016). Novometric vs. recursive causal analysis: The effect of age, education, and region on support of civil liberties. Optimal Data Analysis5, 200-203.

Yarnold PR (2016). Novometric analysis vs. GenODA vs. log-linear model: Temporal stability of the association of presidential vote choice and party identification. Optimal Data Analysis5, 204-207.

Yarnold PR (2016). Novometric vs. log-linear analysis: Church attendance, age and religion. Optimal Data Analysis5, 229-232.

Yarnold PR (2016). Novometric analysis vs. ODA vs. log-linear model in analysis of a two-wave panel design: Assessing temporal stability of Catholic party identification in the 1956-1960 SRC panels. Optimal Data Analysis5, 208-212.

Yarnold PR (2016). Novometric vs. log-linear model: Intergenerational occupational mobility of white American men. Optimal Data Analysis5, 218-222.

Yarnold PR (2016). Novometric vs. logit analysis: Abortion attitude by religion and time. Optimal Data Analysis5, 225-228.

Yarnold PR (2016). Novometric vs. logit vs. probit analysis: Using gender and race to predict if adolescents ever had sexual intercourse. Optimal Data Analysis5, 223-224.

Yarnold PR (2015). GO-CTA vs. marginal structural model: Observed data from a point-treatment study, stratified by known confounder. Optimal Data Analysis4, 104-106.

Yarnold PR, Bennett CL (2016). Novometrics vs. multiple regression analysis: Age and clinical measures of PCP survivors, Optimal Data Analysis5, 79-82.

Yarnold PR (2016). Novometrics vs. ODA: Work shift and raw material production quality. Optimal Data Analysis5, 233-234.

Yarnold PR (2016). Novometrics vs. ODA vs. One-Way ANOVA: Evaluating comparative effectiveness of sales training programs, and the importance of conducting LOO with small samples. Optimal Data Analysis5, 131-132.

Yarnold PR, Linden A. (2016). Novometric analysis with ordered class variables: The optimal alternative to linear regression analysis, Optimal Data Analysis5, 65-73.

Yarnold PR (2016). Using novometrics to disentangle complete sets of sign-test-based multiple-comparison findings. Optimal Data Analysis5, 175-176.

Yarnold PR (2018). Friedman test vs. ODA vs. novometry: Rating violin excellence. Optimal Data Analysis, 7, 16-18.

ODA

Algorithms, Measures, Methods

Yarnold PR, Soltysik RC (1991). Theoretical distributions of optima for univariate discrimination of random data. Decision Sciences, 22, 739-752.

Soltysik RC, Yarnold PR (1994). Univariable optimal discriminant analysis: One-tailed hypotheses. Educational and Psychological Measurement, 54, 646-653.

Carmony L, Yarnold PR, Naeymi-Rad F (1998). One-tailed Type I error rates for balanced two-category UniODA with a random ordered attribute. Annals of Operations Research, 74, 223-238.

Yarnold PR, Soltysik RC (2010). Precision and convergence of Monte Carlo Estimation of two-category UniODA two-tailed p. Optimal Data Analysis, 1, 43-45.

Soltysik RC, Yarnold PR (2013). Statistical power of optimal discrimination with one attribute and two classes: One-tailed hypotheses. Optimal Data Analysis, 2, 26-30.

Yarnold PR, Soltysik RC (2013). Confirmatory analysis for an ordered series of a dichotomous attribute: Airborne radiation and congenital hypothyroidism of California newborns. Optimal Data Analysis, 2, 222-227.

Yarnold PR, Soltysik RC (2013). Exploratory analysis for an ordered series of a dichotomous attribute: Airborne radiation and congenital hypothyroidism of California newborns. Optimal Data Analysis, 2, 228-231.

Yarnold PR (2015). An example of nonlinear UniODA. Optimal Data Analysis4, 124-128.

Applications

Yarnold PR, Martin GJ, Soltysik RC, Nightingale SD (1993). Androgyny predicts empathy for trainees in medicine. Perceptual and Motor Skills, 77, 576-578.

Weinfurt KP, Bryant FB, Yarnold PR (1994). The factor structure of the Affect Intensity Measure: In search of a measurement model. Journal of Research in Personality, 28, 314-331.

Stalans LJ, Finn MA (1995). How novice and experienced officers interpret wife assaults: Normative and efficiency frames. Law & Society Review29, 301-335.

Kabalin CS, Yarnold PR, Grammer LG (1995). Low complication rate of corticosteroid-treated asthmatics undergoing surgical proce­dures. Archives of Internal Medicine155, 1379-1384.

Bryant FB, Yarnold PR (1995). Comparing five alternative factor-models of the Student Jenkins Activity Survey: Separating the wheat from the chaff. Journal of Personality Assessment, 64, 145-158.

Grammer LC, Shaughnessy MA, Hogan MB, Lowenthal M, Yarnold PR, Watkins DM, Berggruen SM (1995). Study of employees with anhydride-induced respiratory disease after re­moval from exposure. Journal of Occupational and Environmental Medicine, 37, 820-825.

Thompson DA, Yarnold PR (1995). Relating patient satisfaction to waiting time perceptions and expectations: The Disconfirmation Para­digm. Academic Emergency Medicine2, 1057-1062.

Grammer LC, Shaughnessy MA, Hogan MB, Berggruen SM, Watkins DM, Yarnold PR (1995). Value of antibody level in diagnosing anhydride-induced immunologic respiratory dis­ease. Journal of Laboratory and Clinical Medicine, 125, 650-653.

Harvey RL, Roth EJ, Yarnold PR, Durham JR, Green D (1996). Deep vein thrombosis in stroke: The use of plasma D-dimer level as a screening test in the rehabilitation setting. Stroke, 27, 1516-1520.

Thompson DA, Yarnold PR, Williams DR, Adams SL (1996). Effects of actual waiting time, perceived waiting time, information deliv­ery and expressive quality on patient satisfaction in the emergency department. Annals of Emergency Medicine, 28, 657-665.

Wyte CD, Adams SL, Cabel JA, Pearlman K, Yarnold PR, Morkin M, Hott KA, Mathews JA (1996). Prospective evaluation of emergency medicine instruction for rotating first-postgraduate-year residents. Academic Emergency Medicine, 3, 72-76.

Grammer LC,  Shaugnnessy MA, Yarnold PR (1996). Risk factors for immunologically mediated disease in workers with respiratory symptoms when exposed to hexahydrophthalic anhydride. Journal of Laboratory and Clinical Medicine, 127, 443-447.

Thompson DA, Yarnold PR, Adams SL, Spacone AB (1996). How accurate are patient’s waiting time perceptions? Annals of Emergency Medicine, 28, 652-656.

Bryant FB, Yarnold PR, Grimm LG (1996). Toward a measurement model of the Affect Intensity Measure: A three-factor structure. Journal of Research in Personality, 30, 223-247.

DeArmon JS, Lacher AR (1997). Aggregate flow directives as a ground delay strategy: Concept analysis using discrete-event simulation. Air Traffic Control Quarterly4, 307-323.

Levenson T, Grammer LC, Yarnold PR, Patterson R (1997). Cost-effective management of malignant potentially fatal asthma. Allergy and Asthma Proceedings, 18, 73-78.

Cohen R, Wiley S, Oswald DP, Eakin KB, Best AM (1999). Applying utilization management principles to a comprehensive service system for children with emotional and behavioral disorders and their families: A feasibility study. Journal of Child and Family Studies8, 463-476.

Grammer LC, Shaughnessy MA, Kenamore BD, Yarnold PR (1999). A clinical and immu­nologic study to assess risk of TMA-induced lung disease as related to exposure. Journal of Occupational and Environmental Medicine, 41, 1048-1051.

Collinge W, Yarnold PR (2001). Transformational breath work in medical illness: Clinical applications and evidence of immunoenhancement. Subtle Energies & Energy Medicine, 12, 139-156.

Grammer LC, Zeiss CR, Yarnold PR, Shaughnessy MA (2002). Human leucocyte antigens (HLA) and trimellitic anhydride (TMA) immunologic lung disease. Respiratory Medicine, 18, 473-477.

Moran JV, Conley DB, Grammer LC, Haines GK, Kern RC, Yarnold PR, Tripathi A, Harris KE, Ditto AM (2003). Specific inflammatory cell types and disease severity as predictors of postsurgical outcomes in patients with chronic sinusitis. Allergy and Asthma Proceedings24, 431-436.

Tanabe P, Gimbel R, Yarnold PR, Kyriacou DN, Adams JG (2004). Reliability and validity of scores on the Emergency Severity Index Version 3. Academic Emergency Medicine11, 59-65.

Van Lancker JA, Yarnold PR, Ditto AM, Tripathi A, Conley DB, Kern RC, Harris KE, Grammer LC (2005). Aeroallergen hypersensi­tivity: Comparing patients with nasal polyps to those with allergic rhinitis. Allergy and Asthma Proceedings, 26, 109-112.

Axelrod BN, Fichtenberg NL, Millis SR, Wertheimer JC (2006). Detecting incomplete effort with digit span from the Wechsler Adult Intelligence Scale—Third Edition. The Clinical Neuropsychologist20, 513-523.

Belmares J, Gerding DN, Parada JP, Miskevics S, Weaver F, Johnson S (2007). Outcome of metronidazole therapy for Clostridium difficile disease and correlation with a scoring system. Journal of Infection, 55, 495-501.

Diesfeldt HFA (2007). Discrepanties tussen de Informantvragenlijst (IQCODE) en cognitieve tests bij deelnemers aan psychogeriatrische dagbehandeling.Tijdschrift voor Gerontologie en Geriatrie, 38, 199-209.

Stalans LJ, Seng M (2006). Identifying subgroups at high risk of dropping out of domestic batterer treatment: The buffering effects of a high school education. International Journal of Offender Therapy and Comparative Criminology, 10, 1-19.

Bennett CL, Nebeker JR, Yarnold PR, Tigue CC, Dorr DA, McKoy JM, Edwards BJ, Hurdle JF, West DP, Lau DT, Angelotta C, Weitzman SA, Belknap SM, Djulbegovic B, Tallman MS, Kuzel TM, Benson AB, Evens A, Trifilio SM, Courtney DM, Raisch DW (2007). Evaluation of serious adverse drug reactions: A proactive pharmacovigilance program (RADAR) versus safety activities conducted by the Food and Drug Administration and pharmaceutical manu­facturers. Archives of Internal Medicine, 167, 1041-1049.

Trifilio SM, Yarnold PR, Scheetz MH, Pi J, Pennick G, Mehta J (2009). Serial plasma voriconazole concentrations after allogeneic hematopoietic stem cell transplantation. Antimicrobial Agents and Chemotherapy, 53, 1793-1796.

McKoy JM, Bennett CL, Scheetz MH, Differding V, Scarsi KK, Yarnold PR, Sutton S, Chandler K, Palella F, Johnson S, Obadina E, Raisch DW, Parada JP (2009). Hepatotoxicity associated with long-course versus short-course nevirapine use as HIV-prophylaxis among non-HIV infected individuals, HIV-infected pregnant women and their offspring: An analysis from the Research on Adverse Drug events And Reports (RADAR) project. Drug Safety, 32, 147-158.

Green D, Sullivan S, Simpson J, Soltysik RC, Yarnold PR (2009). Evolving risk for thrombo­embolism in spinal cord injury (SPIRATE Study). American Journal of Physical Medicine and Rehabilitation, 84, 420-422.

Brocklehurst PR, Baker SR, Speight PM (2010). Factors which determine the referral of potentially malignant disorders by primary care dentists. Journal of Dentistry, 38, 569-578.

Collinge WC, Soltysik RC, Yarnold PR (2010). An internet-based intervention for fibromyalgia self-management: Initial design and alpha test. Optimal Data Analysis, 1, 163-175.

Brocklehurst PR, Baker SR, Speight PM (2010). Primary care clinicians and the detection and referral of potentially malignant disorders of the mouth: A summary of the current evidence. Primary Dental Care17, 65-71.

Stalans LJ, Hacker R, Talbot ME (2010). Comparing nonviolent, other-violent, and domestic batterer sex offenders: Predictive accuracy of risk assessments on sexual recidivism. Criminal Justice and Behavior, 37, 613-628.

Albuquerque K, Giangreco D, Morrison C, Siddiqui M, Sinacore J, Potkul R, Roeske J (2011). Radiation-related predictors of hemato­logic toxicity after concurrent chemoradiation for cervical cancer and implications for bone marrow-sparing pelvic IMRT. International Journal of Radiation Oncology * Biology * Physics, 79, 1043-1047.

Jones A, Ingram MV (2011). A comparison of selected MMPI-2 and MMPI-2-RF validity scales in assessing effort on cognitive tests in a military sample. The Clinical Neurologist7, 1207-1227.

Yung RC, Zeng MY, Stoddard GJ, Garff M,  Callahan K (2012). Transcutaneous computed bioconductance measurement in lung cancer: A treatment enabling technology useful for adjunctive risk stratification in the evaluation of suspicious pulmonary lesions. Journal of Thoracic Oncology7, 681-689.

Yarnold PR, Brofft GC (2013). Comparing knot strength with UniODA. Optimal Data Analysis, 2, 54-59.

Trenery A, Kessler S, Eleasa VS, Lebby A, Bennett CL, Yarnold PR, Ray P, Raisch DW, Rao G, Georgantopoulos P, Norris L, Boyle S, Byrne A, Bialkowski M, Sartor O (2013). Conflicts of interest in pharmaceutical sponsored research on erythropoietin receptors in cancer: An updated analysis. Blood21, 2938.

Collinge W, Yarnold PR, Soltysik RC (2013). Fibromyalgia symptom reduction by online behavioral self-monitoring, longitudinal single subject analysis and automated delivery of individualized guidance. North American Journal of Medical Sciences, 5, 546-553.

Herrold AA, Pape TLB, Guernon A, Mallinson T, Collins E, Jordan N (2014). Prescribing multiple neurostimulatns during rehabilitation for severe brain injury. The Scientific World Journal, Article ID 964578, 1-7. DOI: 10.1155/2014/964578

Yarnold PR, Soltysik RC (2014). Emergency Severity Index (Version 3) score predicts hospital admission. Optimal Data Analysis, 3, 20-22.

Clark-Raymond A, Meresh E, Hoppensteadt D, Fareed J, Sinacore J, Halaris A (2014). Vascular endothelial growth factor: A potential diagnostic biomarker for major depression. Journal of Psychiatric Research, August 19. DOI: http://dx.doi.org/10.1016/j.jpsychires.2014.08.005

Yarnold PR (2015). Is going first an advantage in cribbage? Optimal Data Analysis4, 129-130.

Oguoma VMNwose EUUlasi IIAkintunde AA, Chukwukelu EEAraoye MAEdo AEIjoma CKOnyia ICOgbu IIOnyeanusi JCDigban KAOnodugo OD, Adediran OOpadijo OGBwititi PTRichards RSSkinner TC (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.

Martin J, Benjamin E, Craver C, Kroch E, Nelson E, Bankowitz R (2016). Measuring adverse events in hospitalized patients: An administrative method for measuring harm. Journal of Patient Safety, 12, 125-131.

Linden A, Yarnold PR (2018). Reanalysis of the National Supported Work Experiment using ODA. Optimal Data Analysis, 7, 54-58.

Chi-Square

Yarnold JK (1970). The minimum expectation in c2 goodness of fit tests and the accuracy of approxima­tions for the null distribution. Journal of the American Statistical Association, 65, 864-886. URL: http://www.jstor.org/stable/2284594

Yarnold PR (2010). UniODA vs. chi-square: Ordinal data sometimes feign categorical. Optimal Data Analysis, 1, 62-65.

Yarnold PR (2014). UniODA vs. chi-square: Audience effect on smile production in infants. Optimal Data Analysis, 3, 3-5.

Yarnold PR (2014). UniODA vs. chi-square: Discriminating inhibited and uninhibited infant profiles. Optimal Data Analysis, 3, 9-11.

Yarnold PR (2015). UniODA vs. chi-square: Measures of effect size. Optimal Data Analysis4, 137-138.

Yarnold PR (2016). CTA vs. not chi-square: Differentiating statistical and ecological significance. Optimal Data Analysis5, 112-115.

Yarnold PR (2016). CTA vs. chi-square: Differentiating statistical and ecological significance. Optimal Data Analysis5, 116-117.

Yarnold, PR (2015). UniODA vs. chi-square: Deciphering R x C contingency tables. Optimal Data Analysis4, 156-158.

Yarnold, PR (2015). UniODA vs. not chi-square: Vaccine administration and flu. Optimal Data Analysis4, 159-160.

Yarnold, PR (2015). UniODA vs. chi-square: Voter sentiment and political ward. Optimal Data Analysis4, 161-162.

Yarnold, PR (2015). UniODA vs. not chi-square: Work shift and raw material production quality. Optimal Data Analysis4, 168-170.

Yarnold PR (2015). Chi-square corner cells test: Two wrongs don’t make a right. Optimal Data Analysis4, 171-172.

Yarnold PR (2016). UniODA vs. chi-square: Describing baseline data from the National Pressure Ulcer Long-Term Care Study (NPULS). Optimal Data Analysis5, 24-28.

Yarnold PR (2016). ODA vs. undocumented chi-square: Clarity vs. confusion. Optimal Data Analysis5, 121-123.

Yarnold PR (2019). Value-added by ODA vs. chi-square. Optimal Data Analysis, 8, 10-14.

Dose-Response

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

Yarnold PR, Linden A. (2016). Using machine learning to model dose-response relationships: Eliminating response variable baseline variation by ipsative standardization. Optimal Data Analysis5, 41-52.

Log-Linear Model

Yarnold PR (2010). GenUniODA vs. log-linear model: Modeling discrimination in organizations. Optimal Data Analysis, 1, 59-61.

Yarnold PR (2015). UniODA-based structural decomposition vs. log-linear model: Statics and dynamics of intergenerational class mobility. Optimal Data Analysis4, 179-181.

Yarnold PR (2016). UniODA vs. not log-linear model: The relationship of mental health status and socioeconomic status. Optimal Data Analysis5, 15-18.

Yarnold PR (2016). Pairwise comparisons using UniODA vs. not log-linear model: Ethnic group and schooling in the 1980 Census. Optimal Data Analysis5, 19-23.

Yarnold PR (2016). ODA vs. log-linear model: Gender and surgical operation. Optimal Data Analysis5, 216-217.

Logistic Regression/Fisher’s Discriminant

Yarnold PR (2014). UniODA vs. logistic regression analysis: Serum cholesterol and coronary heart disease and mortality among middle aged diabetic men. Optimal Data Analysis, 3, 17-18.

Yarnold PR (2015). UniODA vs. logistic regression and Fisher’s linear discriminant analysis: Modeling 10-year population change. Optimal Data Analysis4, 139-145.

Multicategorical Class Variable

Yarnold PR, Brofft GC (2013). ODA range test vs. one-way analysis of variance: Comparing strength of alternative line connections. Optimal Data Analysis, 2, 198-201.

Yarnold PR (2013). ODA range test vs. one-way analysis of variance: Patient race and lab results. Optimal Data Analysis, 2, 206-210.

Yarnold PR (2016). Matrix display of pairwise novometric associations for ordered variables. Optimal Data Analysis5, 94-101.

Yarnold PR, Batra M (2016). Matrix display of pairwise novometric associations for mixed-metric variables. Optimal Data Analysis5, 104-107.

Optimizing (Refining) Suboptimal Models

Yarnold PR (2019). Maximizing classification accuracy of CART® recursive partitioning tree models using optimal pruning. Optimal Data Analysis, 8, 26-29.

Yarnold PR (2019). Maximizing the accuracy of a CART tree model predicting missing data. Optimal Data Analysis, 8, 33-37.

Yarnold PR (2019). Optimizing suboptimal classification trees: S-PLUS® propensity score model for adjusted comparison of hospitalized vs. ambulatory patients with community-acquired pneumonia. Optimal Data Analysis, 8, 38-47.

Linden A, Yarnold PR (2018). Identifying maximum-accuracy cut-points for diagnostic indexes via ODA. Optimal Data Analysis, 7, 59-65.

Linden A, Yarnold PR (2018).  Comparative accuracy of a diagnostic index modeled using (optimized) regression vs. novometrics. Optimal Data Analysis, 7, 66-71.

Bryant FB (2010). How to save the binary class variable and predicted probability of group membership from logistic regression analysis to an ASCII space-delimited file in SPSS 17 For WindowsOptimal Data Analysis, 1, 161-162.

Yarnold PR, Soltysik RC (1991). Refining two-group multivariable classification models using univariate optimal discriminant analysis. Decision Sciences, 22, 1158-1164.

Yarnold PR, Hart LA, Soltysik RC (1994). Optimizing the classification performance of logistic regression and Fisher’s discriminant analyses. Educational and Psychological Measurement, 54, 73-85.

Yarnold PR, Bryant FB (1994). A measurement model of the Type A Self-Rating Inventory. Journal of Personality Assessment, 62, 102-115.

Weinfurt KP, Bush PJ (1995). Peer assessment of early adolescents solicited to participate in drug trafficking: A longitudinal model. Journal of Applied Social Psychology25, 2141-2157.

Yarnold PR, Stille FC, Martin GJ (1995). Cross-sectional psychometric assessment of the Functional Status Questionnaire: Use with geriatric versus nongeriatric ambulatory medical patients. International Journal of Psychiatry in Medicine, 25, 305-317.

Yarnold PR, Bryant FB, Nightingale SD, Martin GJ (1996).  Assessing physician empathy using the Interpersonal Reactivity Index: A measurement model and cross-sectional analysis.  Psychology, Health, and Medicine, 1, 207-221.

Yarnold BM, Yarnold PR (2010). Maximizing the accuracy of Probit models via UniODA. Optimal Data Analysis, 1, 41-42.

Yarnold PR, Bryant FB, Soltysik RC (2013). Maximizing the accuracy of multiple regression models via UniODA: Regression away from the mean. Optimal Data Analysis, 2, 19-25.

Yarnold PR (2018). Alternative prediction-interval scaling strategies for regression models. Optimal Data Analysis, 7, 44-45.

Yarnold PR (2013). Maximum-accuracy multiple regression analysis: Influence of registration on overall satisfaction ratings of emergency room patients. Optimal Data Analysis, 2, 72-75.

Yarnold PR (2013). Assessing technician, nurse, and doctor ratings as predictors of overall satisfaction ratings of Emergency Room patients: A maximum-accuracy multiple regression analysis. Optimal Data Analysis, 2, 76-85.

Yarnold PR (2013). Creating a data set with SAS and maximizing ESS of a multiple regression analysis model for a Likert-type dependent variable using UniODAand MegaODAsoftware. Optimal Data Analysis, 2, 191-193.

Yarnold PR (2015). Maximizing ESS of regression models in applications with dependent measures with domains exceeding ten values. Optimal Data Analysis4, 12-13.

Reliability Analysis

Yarnold PR, Feinglass J, Martin GJ, McCarthy WJ (1999). Comparing three pre-processing strategies for longitudinal data for individual patients: An example in functional outcomes research. Evaluation and the Health Professions, 22, 254-277.

Bobb A, Gleason K, Husch M, Feinglass J, Yarnold PR, Noskin GA (2004).  The epidemi­ology of prescribing errors: The potential impact of computerized prescriber order entry.  Archives of Internal Medicine164, 785-792.

Diesfeldt HFA (2007). Measurement of global self-esteem in dementia. Reliability and validity of Brinkman’s self-esteem scale. Tijdschrift voor Gerontologie en Geriatrie, 38, 122-133.

Yarnold PR (2014). UniODA vs. weighted kappa: Evaluating concordance of clinician and patient ratings of the patient’s physical and mental health functioning. Optimal Data Analysis, 3, 12-13.

Yarnold PR (2014). UniODA vs. kappa: Evaluating the long-term (27-year) test-retest reliability of the Type A Behavior Pattern. Optimal Data Analysis, 3, 14-16.

Yarnold PR (2014). How to assess inter-observer reliability of ratings made on ordinal scales: Evaluating and comparing the Emergency Severity Index (Version 3) and Canadian Triage Acuity Scale. Optimal Data Analysis3, 42-49.

Yarnold PR (2014). How to assess the inter-method (parallel-forms) reliability of ratings made on ordinal scales: Evaluating and comparing the Emergency Severity Index (Version 3) and Canadian Triage Acuity Scale. Optimal Data Analysis3, 50-54.

Yarnold PR (2015). Estimating inter-rater reliability using pooled data induces paradoxical confounding: An example involving Emergency Severity Index triage ratings. Optimal Data Analysis4, 21-23.

Yarnold PR (2016) Causality of adverse drug reactions: The upper-bound of arbitrated expert agreement for ratings obtained by WHO and Naranjo algorithms. Optimal Data Analysis5, 37-40.

Yarnold PR (2016). Novometric vs. ODA reliability analysis vs. polychoric correlation with relaxed distributional assumptions: Inter-rater reliability of independent ratings of plant health. Optimal Data Analysis5, 179-183.

Yarnold PR (2018). Psychometric properties of scores on the Naranjo Adverse Drug Reaction Probability Scale, as evaluated by ODA. Optimal Data Analysis, 7, 92.

Reporting Standards

Yarnold PR (2013). Minimum standards for reporting UniODA findings. Optimal Data Analysis, 2, 63-68.

Yarnold PR (2013). Minimum standards for reporting UniODA findings for class variables with three or more response categories. Optimal Data Analysis, 2, 86-93.

Yarnold PR (2013). Standards for reporting UniODA findings expanded to include ESP and all possible aggregated confusion tables. Optimal Data Analysis, 2, 106-119.

Serial and N-of-1 Designs, Simpson’s Paradox

Yarnold PR (1996). Characterizing and circumventing Simpson’s paradox for ordered bivariate data. Educational and Psychological Measurement, 56, 430-442.

Bryant FB, Siegel EKB (2010). Junk science, test validity, and the Uniform Guidelines for Personnel Selection Procedures: The case of Melendez v. Illinois BellOptimal Data Analysis, 1, 176-198.

Soltysik RC, Yarnold PR (2010). The use of unconfounded climatic data improves atmospheric prediction. Optimal Data Analysis, 1, 67-100.

Yarnold PR, Soltysik RC, Collinge W (2013). Modeling individual reactivity in serial designs: An example involving changes in weather and physical symptoms in fibromyalgia. Optimal Data Analysis, 2, 37-42.

Yarnold PR, Soltysik RC (2013). Ipsative transformations are essential in the analysis of serial data. Optimal Data Analysis, 2, 94-97.

Yarnold PR (2013). Comparing attributes measured with “identical” Likert-type scales in single-case designs with UniODA. Optimal Data Analysis, 2, 148-153.

Yarnold PR (2013). Comparing responses to dichotomous attributes in single-case designs. Optimal Data Analysis, 2, 154-156.

Yarnold PR (2013). Ascertaining an individual patient’s symptom dominance hierarchy: Analysis of raw longitudinal data induces Simpson’s Paradox. Optimal Data Analysis, 2, 159-171.

Small Sample

Yarnold PR (2013). Percent oil-based energy consumption and average percent GDP growth: A small sample UniODA analysis. Optimal Data Analysis, 2, 60-61.

Yarnold PR (2013). UniODA and small samples. Optimal Data Analysis, 2, 71.

Structural Decomposition

Yarnold PR (2015). Modeling intended and awarded college degree vis-à-vis UniODA-based structural decomposition. Optimal Data Analysis4, 177-178.

Yarnold PR (2015). Modeling religious mobility by UniODA-based structural decomposition. Optimal Data Analysis4, 192-193.

Yarnold PR (2015). UniODA-based structural decomposition vs. legacy linear models: Statics and dynamics of intergenerational occupational mobility. Optimal Data Analysis4, 194-196.

Versus

Yarnold PR (2015). UniODA vs. doubly incomplete three-factor ANOVA: Production failure attributable to acid corrosion. Optimal Data Analysis4, 165-167.

Yarnold PR (2018). ANOVA with one between-groups factor vs. novometric analysis. Optimal Data Analysis, 7,    23-25.

Yarnold PR (2018). ANOVA with two between-groups factors vs. novometric analysis. Optimal Data Analysis, 7, 26-27.

Yarnold PR (2018). ANOVA with three between-groups factors vs. novometric analysis. Optimal Data Analysis, 7, 36-39.

Yarnold PR (2015). UniODA vs. Bowker’s test for symmetry: Diagnosis before vs. after treatment. Optimal Data Analysis4, 29-31.

Yarnold PR (2014). UniODA vs. Bray-Curtis dissimilarity index for count data. Optimal Data Analysis3, 115-116.

Yarnold PR (2015). UniODA vs. Cochran’s Q test: Comparing success of alternatives. Optimal Data Analysis4, 116-117.

Yarnold PR (2015). UniODA vs. Cochran’s Q test: Evaluating success rate in web usability testing. Optimal Data Analysis, 4, 118-119.

Yarnold PR (2015). UniODA vs. Cochran’s Q test: Pet store reptile display behavior by holiday. Optimal Data Analysis4, 120-121.

Yarnold PR (2015). UniODA vs. Cochran’s Q test for correlated proportions: Measures of effect size. Optimal Data Analysis4, 135-136.

Yarnold PR (2015). Evaluating non-confounded association of an attribute and a class variable using partial UniODA. Optimal Data Analysis4, 32-35.

Yarnold PR (2015). Optimal statistical analysis involving a confounding variable. Optimal Data Analysis4, 87-103.

Yarnold PR (2015). Optimal statistical analysis involving multiple confounding variables. Optimal Data Analysis4, 107-112.

Yarnold PR (2015). UniODA vs. eyeball analysis: Comparing repeated ordinal scores. Optimal Data Analysis4, 154-155.

Yarnold PR (2015). Generalized linear interactive modeling: Four wrongs don’t make a right. Optimal Data Analysis4, 173-174.

Yarnold PR (2014). UniODA vs. Kendall’s Coefficient of Concordance (W): Multiple rankings of multiple movies. Optimal Data Analysis3, 121-123.

Yarnold PR (2015). UniODA vs. Kruskal-Wallis test: Farming method and corn yield. Optimal Data Analysis4, 113-115.

Yarnold PR (2015). UniODA vs. Kruskal-Wallis test: Gender and dominance of free-ranging domestic dogs in the outskirts of Rome. Optimal Data Analysis4, 122-123.

Yarnold PR (2015). UniODA vs. legacy bivariate statistical methodologies. Optimal Data Analysis4, 73-80.

Yarnold PR (2015). UniODA vs. McNemar’s test for correlated proportions: Diagnosis of disease before vs. after treatment. Optimal Data Analysis4, 24-26.

Yarnold PR (2015). UniODA vs. McNemar’s test: A small sample analysis. Optimal Data Analysis4, 27-28.

Yarnold PR (2014). UniODA vs. Mann-Whitney U test: Sunlight and pedal width. Optimal Data Analysis4, 3-5.

Yarnold PR (2014). UniODA vs. Mann-Whitney U test: Comparative effectiveness of laxatives. Optimal Data Analysis4, 6-8.

Yarnold PR (2016). ODA vs. π and κ: Paradoxes of kappa. Optimal Data Analysis5, 160-161.

Yarnold PR (2015). UniODA vs. point-biserial correlation: Marital status and need for achievement Optimal Data Analysis4, 199.

Yarnold PR (2014). UniODA vs. polychoric correlation: Number of lambs born over two years. Optimal Data Analysis3, 113-114.

Yarnold PR (2014). UniODA vs. ROC analysis: Computing the “optimal” cut-point. Optimal Data Analysis3, 117-120.

Yarnold PR (2015). UniODA vs. sign test: Comparing repeated ordinal scores. Optimal Data Analysis4, 151-153.

Yarnold PR (2015). UniODA vs. Spearman rank ρ: Between-raters reliability of scores on the Adverse Drug Reaction Probability Scale. Optimal Data Analysis4, 148-150.

Yarnold PR (2015). UniODA vs. Wilcoxon rank sum test: A small-sample paired experiment. Optimal Data Analysis4, 163-164.

Yarnold PR (2014). UniODA vs. t-Test: Comparing two migraine treatments. Optimal Data Analysis, 3, 6-8.

Yarnold PR (2015). UniODA vs. t-Test: Low brain uptake of L-[11C]5-hydroxytryptophan in major depression. Optimal Data Analysis4, 146-147.

Yarnold PR (2015). UniODA vs. Wilcoxon Rank-Sum Test: Invariance over Monotonic Transformations. Optimal Data Analysis4, 200-201.

Reference Books

Bryant FB, King SP, Mart CM. (2007). Multivariate statistical strategies for construct validation in positive psychology. Oxford handbook of methods in positive psychology: 61-82.

Chinneck, JW (2008). Feasibility and infeasibility in optimization: Algorithms and computational methods. Vol. 118. Springer.

Horton, A.M. Jr., & Hartlage, L.C. (2010). The handbook of forensic neuropsychology, Second Edition. New York: Springer.

Lyons, J.S. (1997). The measurement and management of clinical outcomes in mental health. New York: Wiley.

White, M (2013). Fibromyalgia Syndrome.  eBook: http://www.natrx.co.za/media/file/Fibromyalgiae-Book.pdf