Analytic Methods

We explore novel solutions in data analysis such as Artificial Intelligence. Within the IHR, we collaborate closely with investigators on study design, statistical analysis and research manuscripts. Our primary aim is to provide investigators in the Institute for Health Research with state-of-the-art statistical support.

Methodologic Topic Areas:

Analytic areas diagram

IHR investigators and biostatisticians have experience/expertise in a variety of methods, some of which are particularly useful as we partner with KPCO Operations. A sampling of our methodologic work within a few selected topics can be viewed below by clicking on the topic area.

Methods Used in Partnership with KPCO Operations

  1. Generalized Linear models for Poisson regression, Binomial, and Gamma distributions
    • Bender BG, Wagner NM, Shoup JA, Goodrich GK, Shetterly SM, Cvietusa PJ, Anderson CB, Anderson CB, Anderson CB, Tacinas C, King DK and Raebel MA. Adults with asthma experience no increase in asthma-related exacerbations when digital communication technology tools are employed to offset provider workload: A pragmatic randomized trial. Med Care. 2020, Apr;58(4): 352-359. https://www.ncbi.nlm.nih.gov/pubmed/32197029.
  2. Natural Language Processing
    • Algorithms for identifying bilateral mastectomy, abdominal aortic aneurysms, and kidney mass/lesions – not published, but used in Operations
    • Boggs JM, Quintana LM, Powers JD, Hochberg S and Beck A. Frequency of clinicians' assessments for access to lethal means in persons at risk for suicide. Arch Suicide Res. 2020, May 7:1-10. https://www.ncbi.nlm.nih.gov/pubmed/32379012.
  3. Predictive Models
    • Zeng C, Melberg MW, Tavel HM, Argosino SE, Kiepe DA, Lyons EE, Ford MA and Steiner CA. Development and validation of a model for predicting rehabilitation care location among patients discharged home after total knee arthroplasty. The Journal of Arthroplasty. 2020, Feb 21. https://www.ncbi.nlm.nih.gov/pubmed/32164994.
  4. Stepped Wedge Design
    • Palen TE, Sharpe RE, Jr., Shetterly SM and Steiner JF. Randomized clinical trial of a clinical decision support tool for improving the appropriateness scores for ordering imaging studies in primary and specialty care ambulatory clinics. AJR Am. J. Roentgenol. 2019, Jul 16:1-6. PMID: 31310183.
  5. Time-Series Analyses, Control Charts
    • Daley MF, Arnold Rehring SM, Glenn KA, Reifler LM and Steiner JF. Improving antibiotic prescribing for pediatric urinary tract infections in outpatient settings. Pediatrics. 2020, Apr;145(4). https://www.ncbi.nlm.nih.gov/pubmed/32127361.
    • Raebel MA, Shetterly SM, Bhardwaja B, Sterrett AT, Schroeder EB, Chorny J, Hagen TP, Silverman DJ, Astles R and Lubin IM. Technology-enabled outreach to patients taking high-risk medications reduces a quality gap in completion of clinical laboratory testing. Population health management. 2019, May 20. PMID: 31107176.
  6. Difference in difference models
    • Palen TE, Ross C, Powers JD and Xu S. Association of online patient access to clinicians and medical records with use of clinical services. JAMA. 2012, Nov 21:2012-2019.

Self-Controlled Case Series Used in Vaccine Safety

  1. Daley MF, Clarke CL, Glanz JM, Xu S, Hambidge SJ, Donahue JG, Nordin JD, Klein NP, Jacobsen SJ, Naleway AL, Jackson ML, Lee G, Duffy J and Weintraub E. The safety of live attenuated influenza vaccine in children and adolescents 2 through 17 years of age: A vaccine safety datalink study. Pharmacoepidemiol Drug Saf. 2017, Nov 17 (Epub ahead of print). PMID: 29148124.
  2. Xu S, Clarke CL, Newcomer SR, Daley MF and Glanz JM. Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample. Biometrical journal. Biometrische Zeitschrift. 2018, Jul;60(4):748-760 PMID: 29768667.
  3. Hambidge SJ, Newcomer SR, Narwaney KJ, Glanz JM, Daley MF, Xu S, Shoup JA, Rowhani-Rahbar A, Klein NP, Lee GM, Nelson JC, Lugg M, Naleway AL, Nordin JD, Weintraub E, Destefano F. Timely Versus Delayed Early Childhood Vaccination and Seizures. Pediatrics 2014; 133(6):e1492-e1499. PMID:24843064.

Pattern Identification

  1. (Clustering, Latent Class Analysis) Bayliss EA, Ellis JL, Powers JD, Gozansky W and Zeng C. Using self-reported data to segment older adult populations with complex care needs. EGEMS (Wash DC). 2019, Apr 12;7(1):12. PMID: 31065556.
  2. (Clustering) Bayliss EA, Ellis JL, Powers JD, Gozansky W and Zeng C. Using self-reported data to segment older adult populations with complex care needs. EGEMS (Wash DC). 2019, Apr 12;7(1):12. PMID: 31065556.
  3. (Clustering) Bayliss EA, Powers JD, Ellis JL, Barrow JC, Strobel M, Beck A. Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs. EGEMS (Wash DC). 2016;4(1):1258. PMID: 27563684.
  4. (Clustering) Green AR, Reifler LM, Boyd CM, Weffald LA and Bayliss EA. Medication profiles of patients with cognitive impairment and high anticholinergic burden. Drugs Aging. 2018, Mar;35(3):223-232. PMID: 29404965.

Predictive Modeling and Algorithm Development

  1. Schroeder EB, Xu S, Goodrich GK, Nichols GA, O'Connor PJ and Steiner JF. Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model. J Diabetes Complications. 2017, Jul;31(7):1158-1163. PMID: 28462891.
  2. Allen LA, Matlock DD, Shetterly SM, Xu S, Levy WC, Portalupi LB, McIlvennan CK, Gurwitz JH, Johnson ES, Smith DH and Magid DJ. Use of risk models to predict death in the next year among individual ambulatory patients with heart failure. JAMA Cardiol. 2017, Apr 1;2(4):435-441. PMID: 28002546.
  3. Schroeder EB, Adams JL, Chonchol M, Nichols GA, O'Connor PJ, Powers JD, Schmittdiel JA, Shetterly SM and Steiner JF. Predictors of hyperkalemia and hypokalemia in individuals with diabetes: A classification and regression tree analysis. J Gen Intern Med. 2020, Apr 16. https://www.ncbi.nlm.nih.gov/pubmed/32301044.
  4. Zeng C, Melberg MW, Tavel HM, Argosino SE, Kiepe DA, Lyons EE, Ford MA and Steiner CA. Development and validation of a model for predicting rehabilitation care location among patients discharged home after total knee arthroplasty. The Journal of Arthroplasty. 2020, Feb 21. https://www.ncbi.nlm.nih.gov/pubmed/32164994.
  5. Bayliss EA, Powers JD, Ellis JL, Barrow JC, Strobel M, Beck A. Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs. EGEMS (Wash DC). 2016;4(1):1258. PMID: 27563684.
  6. Carroll NM, Burniece KM, Holzman J, McQuillan DB, Plata A and Ritzwoller DP. Algorithm to identify systemic cancer therapy treatment using structured electronic data. JCO Clin Cancer Inform. 2017;1:1-9. http://ascopubs.org/doi/pdf/10.1200/CCI.17.00002.
  7. Carroll NM, Ritzwoller DP, Banegas MP, O'Keeffe-Rosetti M, Cronin AM, Uno H, Hornbrook MC and Hassett MJ. Performance of cancer recurrence algorithms after coding scheme switch from international classification of diseases 9th revision to international classification of diseases 10th revision. JCO Clin Cancer Inform. 2019, Mar;3:1-9. PMID: 30869998.

Comparative Effectiveness Research Methods Applied to Observational Studies

  1. (Propensity Scores) Carroll NM, Delate T, Menter A, Hornbrook MC, Kushi L, Aiello Bowles EJ, Loggers ET, Ritzwoller DP. Use of Bevacizumab in Community Settings: Toxicity Profile and Risk of Hospitalization in Patients with Advanced Non-Small-Cell Lung Cancer. J Oncol Pract. 2015;11(5):356-362. PMID: 26060223.
  2. (Propensity Scores) Menter AR, Carroll NM, Sakoda LC, Delate T, Hornbrook MC, Jain RK, Kushi LH, Quinn VP, Ritzwoller DP. Effect of Angiotensin System Inhibitors on Survival in Patients Receiving Chemotherapy for Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2016;Aug 20 [epub ahead of print]. PMID: 27637408.
  3. (Propensity Scores, Disease Risk Scores) Xu S, Shetterly S, Cook AJ, Raebel MA, Goonesekera S, Shoaibi A, Roy J, Fireman B. Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring. Pharmacoepidemiol Drug Saf. 2016;25(4):453-461. PMID: 26875591.
  4. (Propensity scores) Tsai TT, Rehring TF, Rogers RK, Shetterly SM, Wagner NM, Gupta R, Jazaeri O, Hedayati N, Jones WS, Patel MR, Ho PM, Go AS, Magid DJ. The Contemporary Safety and Effectiveness of Lower Extremity Bypass Surgery and Peripheral Endovascular Interventions in the Treatment of Symptomatic Peripheral Arterial Disease. Circulation. 2015;132(21):1999-2011. PMID: 26362632.
  5. (Propensity Scores) Peterson PN, Greenlee RT, Go AS, Magid DJ, Cassidy‐Bushrow A, Garcia‐Montilla R, Glenn KA, Gurwitz JH, Hammill SC,Hayes J, Kadish A, Reynolds K, Sharma P, Smith DH, Varolosy PD, Viadaillet H, Zeng C, Normand SLT, Masoudi FA. Comparison of inappropriate shocks and other health outcomes between single‐and dual‐chamber implantable cardioverter‐defibrillators for primary prevention of sudden cardiac death: Results from the cardiovascular research network longitudinal study of implantable cardioverter‐defibrillators. J Am Heart Assoc. 2017, Sept;6(11):e006937. http://jaha.ahajournals.org/content/ahaoa/6/11/e006937.full.pdf.
  6. (Missing Data, Imputation) Daley MF, Barrow JC, Tabano DC, Reifler LM, Kraus EM, Davies SD, Williford DL, White B, Shupe A and Davidson AJ. Estimating childhood obesity prevalence in communities through multi-institutional data sharing. J. Public Health Manag. Pract. 2019, Feb 14. PMID: 30789593.
  7. (Misclassification Bias) Newcomer SR, Kulldorff M, Xu S, Daley MF, Fireman B, Lewis E and Glanz JM. Bias from outcome misclassification in immunization schedule safety research. Pharmacoepidemiol. Drug Saf. 2018, Feb;27(2):221-228. PMID: 29292551.
  8. (Quantitative Bias Analysis) Newcomer SR, Xu S, Kulldorff M, Daley MF, Fireman B, Glanz JM. A primer on quantitative bias analysis with positive predictive values in research using electronic health data. J Am Med Inform Assoc. 2019, Jul 31;26(12):1664-74. PMID: 31365086.