Pragmatic cluster randomized trial to evaluate effectiveness and implementation of enhanced EHR-facilitated cancer symptom control (E2C2)

Lila J Finney Rutten, Kathryn J Ruddy, Linda L Chlan, Joan M Griffin, Jeph Herrin, Aaron L Leppin, Deirdre R Pachman, Jennifer L Ridgeway, Parvez A Rahman, Curtis B Storlie, Patrick M Wilson, Andrea L Cheville, Lila J Finney Rutten, Kathryn J Ruddy, Linda L Chlan, Joan M Griffin, Jeph Herrin, Aaron L Leppin, Deirdre R Pachman, Jennifer L Ridgeway, Parvez A Rahman, Curtis B Storlie, Patrick M Wilson, Andrea L Cheville

Abstract

Background: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools.

Methods: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention. The E2C2 intervention will have two levels of care: level 1 will offer low-touch, automated self-management support for patients reporting moderate sleep disturbance, pain, anxiety, depression, and energy deficit symptoms or limitations in physical function (or both). Level 2 will offer nurse-managed collaborative care for patients reporting more intense (severe) symptoms or functional limitations (or both). By surveying and interviewing clinical staff, we will also evaluate whether the use of a multifaceted, evidence-based implementation strategy to support adoption and use of the E2C2 technologies improves patient and clinical outcomes. Finally, we will conduct a mixed methods evaluation to identify disparities in the adoption and implementation of the E2C2 intervention among elderly and rural-dwelling patients with cancer.

Discussion: The E2C2 intervention offers a pragmatic, scalable approach to delivering guideline-based symptom and function management for cancer patients. Since discrete EHR-imbedded algorithms drive defining aspects of the intervention, the approach can be efficiently disseminated and updated by specifying and modifying these centralized EHR algorithms.

Trial registration: ClinicalTrials.gov, NCT03892967. Registered on 25 March 2019.

Keywords: Electronic health record; Neoplasm; Pain; Palliative care; Patient care team; Patient-reported outcome measure; Quality of life; Self-management; Survivor; Systems integration.

Conflict of interest statement

LJFR, LLC, JMG, JH, ALL, DRP, JLR, PAR, CBS, PMW, and ALC have no competing interests to declare. KJR inherited and then sold (in February 2018) stock in Merck and Co, Inc. and Pfizer, Inc.

Figures

Fig. 1
Fig. 1
Locations of Participating Mayo Clinic Health System Sites in Minnesota and Wisconsin. (Used with permission of Mayo Foundation for Medical Education and Research)
Fig. 2
Fig. 2
Intervention Overview. ePRO indicates electronic patient-reported outcome; E2C2, enhanced, electronic health record–facilitated cancer symptom control; SPADE, sleep disturbance, pain, anxiety, depression, and energy deficit
Fig. 3
Fig. 3
SPIRIT Diagram
Fig. 4
Fig. 4
Stepped Wedge Cluster Design for the Enhanced, Electronic Health Record–Facilitated Cancer Symptom Control (E2C2) Trial
Fig. 5
Fig. 5
Simulated Power in Relation to Effect Size

References

    1. Cheville AL, Shen T, Chang M, Basford JR. Appropriateness of the treatment of fatigued patients with stage IV cancer. Support Care Cancer. 2013;21(1):229–233.
    1. Barsevick AM, Sweeney C, Haney E, Chung E. A systematic qualitative analysis of psychoeducational interventions for depression in patients with cancer. Oncol Nurs Forum. 2002;29(1):73–84.
    1. Laoutidis ZG, Mathiak K. Antidepressants in the treatment of depression/depressive symptoms in cancer patients: a systematic review and meta-analysis. BMC Psychiatry. 2013;13:140.
    1. Boyce MB, Browne JP. Does providing feedback on patient-reported outcomes to healthcare professionals result in better outcomes for patients? A systematic review. Qual Life Res. 2013;22(9):2265–2278.
    1. Mooney KH, Beck SL, Friedman RH, Farzanfar R, Wong B. Automated monitoring of symptoms during ambulatory chemotherapy and oncology providers’ use of the information: a randomized controlled clinical trial. Support Care Cancer. 2014;22(9):2343–2350.
    1. Carpenter JS, Rawl S, Porter J, Schmidt K, Tornatta J, Ojewole F, et al. Oncology outpatient and provider responses to a computerized symptom assessment system. Oncol Nurs Forum. 2008;35(4):661–669.
    1. Yount SE, Rothrock N, Bass M, Beaumont JL, Pach D, Lad T, et al. A randomized trial of weekly symptom telemonitoring in advanced lung cancer. J Pain Symptom Manag. 2014;47(6):973–989.
    1. Barsevick AM. The elusive concept of the symptom cluster. Oncol Nurs Forum. 2007;34(5):971–980.
    1. Barsevick AM, Whitmer K, Nail LM, Beck SL, Dudley WN. Symptom cluster research: conceptual, design, measurement, and analysis issues. J Pain Symptom Manag. 2006;31(1):85–95.
    1. Barsevick AM. The concept of symptom cluster. Semin Oncol Nurs. 2007;23(2):89–98.
    1. Dodd MJ, Miaskowski C, Paul SM. Symptom clusters and their effect on the functional status of patients with cancer. Oncol Nurs Forum. 2001;28(3):465–470.
    1. Fox SW, Lyon DE. Symptom clusters and quality of life in survivors of lung cancer. Oncol Nurs Forum. 2006;33(5):931–936.
    1. Given BA, Given CW, Sikorskii A, Hadar N. Symptom clusters and physical function for patients receiving chemotherapy. Semin Oncol Nurs. 2007;23(2):121–126.
    1. Ferreira KA, Kimura M, Teixeira MJ, Mendoza TR, da Nobrega JC, Graziani SR, et al. Impact of cancer-related symptom synergisms on health-related quality of life and performance status. J Pain Symptom Manag. 2008;35(6):604–616.
    1. Hadi S, Fan G, Hird AE, Kirou-Mauro A, Filipczak LA, Chow E. Symptom clusters in patients with cancer with metastatic bone pain. J Palliat Med. 2008;11(4):591–600.
    1. Carr D, Goudas L, Lawrence D, Pirl W, Lau J, DeVine D, et al. Management of cancer symptoms: pain, depression, and fatigue evidence report /technology assessment [serial on the Internet] 2002.
    1. Donovan KA, Jacobsen PB. Fatigue, depression, and insomnia: evidence for a symptom cluster in cancer. Semin Oncol Nurs. 2007;23(2):127–135.
    1. Fleishman SB. Treatment of symptom clusters: pain, depression, and fatigue. J Natl Cancer Inst Monogr. 2004;(32):119–23.
    1. So WK, Marsh G, Ling WM, Leung FY, Lo JC, Yeung M, et al. The symptom cluster of fatigue, pain, anxiety, and depression and the effect on the quality of life of women receiving treatment for breast cancer: a multicenter study. Oncol Nurs Forum. 2009;36(4):E205–E214.
    1. Yamagishi A, Morita T, Miyashita M, Kimura F. Symptom prevalence and longitudinal follow-up in cancer outpatients receiving chemotherapy. J Pain Symptom Manag. 2009;37(5):823–830.
    1. Brown LF, Kroenke K. Cancer-related fatigue and its associations with depression and anxiety: a systematic review. Psychosomatics. 2009;50(5):440–447.
    1. Katon W, Guico-Pabia CJ. Improving quality of depression care using organized systems of care: a review of the literature. Prim Care Companion CNS Disord. 2011;13(1):PCC.10r01019blu.
    1. Katon W, Unutzer J, Wells K, Jones L. Collaborative depression care: history, evolution and ways to enhance dissemination and sustainability. Gen Hosp Psychiatry. 2010;32(5):456–464.
    1. Coventry PA, Hudson JL, Kontopantelis E, Archer J, Richards DA, Gilbody S, et al. Characteristics of effective collaborative care for treatment of depression: a systematic review and meta-regression of 74 randomised controlled trials. PLoS One. 2014;9(9):e108114.
    1. Sighinolfi C, Nespeca C, Menchetti M, Levantesi P, Belvederi Murri M, Berardi D. Collaborative care for depression in European countries: a systematic review and meta-analysis. J Psychosom Res. 2014;77(4):247–263.
    1. Craven MA, Bland R. Better practices in collaborative mental health care: an analysis of the evidence base. Can J Psychiatr. 2006;51(6 Suppl 1):7S–72S.
    1. Archer J, Bower P, Gilbody S, Lovell K, Richards D, Gask L, et al. Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev. 2012;10:CD006525.
    1. van Straten A, Hill J, Richards DA, Cuijpers P. Stepped care treatment delivery for depression: a systematic review and meta-analysis. Psychol Med. 2015;45(2):231–246.
    1. Hudson JL, Bower P, Archer J, Coventry PA. Does collaborative care improve social functioning in adults with depression? The application of the WHO ICF framework and meta-analysis of outcomes. J Affect Disord. 2016;189:379–391.
    1. Tully PJ, Baumeister H. Collaborative care for comorbid depression and coronary heart disease: a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2015;5(12):e009128.
    1. Panagioti M, Bower P, Kontopantelis E, Lovell K, Gilbody S, Waheed W, et al. Association between chronic physical conditions and the effectiveness of collaborative care for depression: an individual participant data meta-analysis. JAMA Psychiatry. 2016;73(9):978–989.
    1. Kroenke K, Krebs EE, Wu J, Yu Z, Chumbler NR, Bair MJ. Telecare collaborative management of chronic pain in primary care: a randomized clinical trial. JAMA. 2014;312(3):240–248.
    1. Cheville AL, Moynihan T, Basford JR, Nyman JA, Tuma ML, Macken DA, et al. The rationale, design, and methods of a randomized, controlled trial to evaluate the effectiveness of collaborative telecare in preserving function among patients with late stage cancer and hematologic conditions. Contemp Clin Trials. 2018;64:254–264.
    1. Mooney KH, Beck SL, Wong B, Dunson W, Wujcik D, Whisenant M, et al. Automated home monitoring and management of patient-reported symptoms during chemotherapy: results of the symptom care at home RCT. Cancer Med. 2017;6(3):537–546.
    1. Cleeland CS, Wang XS, Shi Q, Mendoza TR, Wright SL, Berry MD, et al. Automated symptom alerts reduce postoperative symptom severity after cancer surgery: a randomized controlled clinical trial. J Clin Oncol. 2011;29(8):994–1000.
    1. National Cancer Institute . Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) 2019.
    1. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–226.
    1. Gilbody S, Sheldon T, House A. Screening and case-finding instruments for depression: a meta-analysis. CMAJ. 2008;178(8):997–1003.
    1. Kroenke K, Unutzer J. Closing the false divide: sustainable approaches to integrating mental health services into primary care. J Gen Intern Med. 2017;32(4):404–410.
    1. Mularski RA, White-Chu F, Overbay D, Miller L, Asch SM, Ganzini L. Measuring pain as the 5th vital sign does not improve quality of pain management. J Gen Intern Med. 2006;21(6):607–612.
    1. Valderas JM, Kotzeva A, Espallargues M, Guyatt G, Ferrans CE, Halyard MY, et al. The impact of measuring patient-reported outcomes in clinical practice: a systematic review of the literature. Qual Life Res. 2008;17(2):179–193.
    1. Chen J, Ou L, Hollis SJ. A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Serv Res. 2013;13:211.
    1. Huang H, Tabb KM, Cerimele JM, Ahmed N, Bhat A, Kester R. Collaborative care for women with depression: a systematic review. Psychosomatics. 2017;58(1):11–18.
    1. Nierenberg AA, McIntyre RS, Sachs GS. Improving outcomes in patients with bipolar depression: a comprehensive review. J Clin Psychiatry. 2015;76(3):e10.
    1. Atlantis E, Fahey P, Foster J. Collaborative care for comorbid depression and diabetes: a systematic review and meta-analysis. BMJ Open. 2014;4(4):e004706.
    1. Jacob V, Chattopadhyay SK, Sipe TA, Thota AB, Byard GJ, Chapman DP, et al. Economics of collaborative care for management of depressive disorders: a community guide systematic review. Am J Prev Med. 2012;42(5):539–549.
    1. Boland L, Bennett K, Connolly D. Self-management interventions for cancer survivors: a systematic review. Support Care Cancer. 2018;26(5):1585–1595.
    1. Bennett S, Pigott A, Beller EM, Haines T, Meredith P, Delaney C. Educational interventions for the management of cancer-related fatigue in adults. Cochrane Database Syst Rev. 2016;11:CD008144.
    1. Kobleder A, Raphaelis S, Glaus A, Fliedner M, Mueller MD, Gafner D, et al. Recommendations for symptom management in women with vulvar neoplasms after surgical treatment: an evidence-based guideline. Eur J Oncol Nurs. 2016;25:68–76.
    1. Hammer MJ, Ercolano EA, Wright F, Dickson VV, Chyun D, Melkus GD. Self-management for adult patients with cancer: an integrative review. Cancer Nurs. 2015;38(2):E10–E26.
    1. Te Boveldt N, Vernooij-Dassen M, Leppink I, Samwel H, Vissers K, Engels Y. Patient empowerment in cancer pain management: an integrative literature review. Psychooncology. 2014;23(11):1203–11.
    1. Howell D, Harth T, Brown J, Bennett C, Boyko S. Self-management education interventions for patients with cancer: a systematic review. Support Care Cancer. 2017;25(4):1323–1355.
    1. Lovell MR, Luckett T, Boyle FM, Phillips J, Agar M, Davidson PM. Patient education, coaching, and self-management for cancer pain. J Clin Oncol. 2014;32(16):1712–1720.
    1. Goldzweig CL, Orshansky G, Paige NM, Miake-Lye IM, Beroes JM, Ewing BA, et al. Electronic health record-based interventions for improving appropriate diagnostic imaging: a systematic review and meta-analysis. Ann Intern Med. 2015;162(8):557–565.
    1. Jones SS, Rudin RS, Perry T, Shekelle PG. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med. 2014;160(1):48–54.
    1. Eastaugh SR. Health economics: efficiency, quality, and equity. Westport: Auburn House; 1992.
    1. Henry J. Kaiser Family Foundation. Medicaid State Fact Sheets. 2018. . Accessed 17 Sept 2019.
    1. American Cancer Society. Cancer facts & figures 2018. . Accessed 15 Dec 2018.
    1. MN Disclosure Of Health Records For External Research, Statute. 144.295. 2018.
    1. Snyder CF, Smith KC, Bantug ET, Tolbert EE, Blackford AL, Brundage MD, et al. What do these scores mean? Presenting patient-reported outcomes data to patients and clinicians to improve interpretability. Cancer. 2017;123(10):1848–1859.
    1. Oncology Nursing Society . Symptom interventions overview. 2019.
    1. National Comprehensive Cancer Network . NCCN guidelines. 2019.
    1. National Comprehensive Cancer Network . NCCN guidelines for supportive care: adult cancer pain. 2019.
    1. Miller K, Mosby D, Capan M, Kowalski R, Ratwani R, Noaiseh Y, et al. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc. 2018;25(5):585–592.
    1. Main C, Moxham T, Wyatt JC, Kay J, Anderson R, Stein K. Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic reviews of the effects and cost-effectiveness of systems. Health Technol Assess. 2010;14(48):1–227.
    1. Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci. 2015;10:21.
    1. Baskerville NB, Liddy C, Hogg W. Systematic review and meta-analysis of practice facilitation within primary care settings. Ann Fam Med. 2012;10(1):63–74.
    1. Taylor EF, Machta RM, Meyers DS, Genevro J, Peikes DN. Enhancing the primary care team to provide redesigned care: the roles of practice facilitators and care managers. Ann Fam Med. 2013;11(1):80–83.
    1. Shojania KG, Jennings A, Mayhew A, Ramsay CR, Eccles MP, Grimshaw J. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev. 2009;8(3):CD001096.
    1. Shojania KG, Jennings A, Mayhew A, Ramsay C, Eccles M, Grimshaw J. Effect of point-of-care computer reminders on physician behaviour: a systematic review. CMAJ. 2010;182(5):E216–E225.
    1. Woertman W, de Hoop E, Moerbeek M, Zuidema SU, Gerritsen DL, Teerenstra S. Stepped wedge designs could reduce the required sample size in cluster randomized trials. J Clin Epidemiol. 2013;66(7):752–758.
    1. Baio G, Copas A, Ambler G, Hargreaves J, Beard E, Omar RZ. Sample size calculation for a stepped wedge trial. Trials. 2015;16:354.
    1. Storlie CB, Fugate ML, Higdon DM, Huzurbazar AV, Francois EG, McHugh DC. Methods for characterizing and comparing populations of shock wave curves. Technometrics. 2013;55(4):436–449.
    1. Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007;28(2):182–191.
    1. Cohen J. Statistical power analysis for the behavioral sciences. 2. Hillsdale: L. Erlbaum Associates; 1988.
    1. Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H, et al. CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines. J Am Med Inform Assoc. 2018;25:331–336.
    1. Kaggal VC, Elayavilli RK, Mehrabi S, Pankratz JJ, Sohn S, Wang Y, et al. Toward a learning health-care system - knowledge delivery at the point of care empowered by big data and NLP. Biomed Inform Insights. 2016;8(Suppl 1):13–22.
    1. Aktas A, Walsh D, Kirkova J. The psychometric properties of cancer multisymptom assessment instruments: a clinical review. Support Care Cancer. 2015;23(7):2189–2202.
    1. Locke DE, Decker PA, Sloan JA, Brown PD, Malec JF, Clark MM, et al. Validation of single-item linear analog scale assessment of quality of life in neuro-oncology patients. J Pain Symptom Manag. 2007;34(6):628–638.
    1. Paice JA, Cohen FL. Validity of a verbally administered numeric rating scale to measure cancer pain intensity. Cancer Nurs. 1997;20(2):88–93.
    1. Singh JA, Satele D, Pattabasavaiah S, Buckner JC, Sloan JA. Normative data and clinically significant effect sizes for single-item numerical linear analogue self-assessment (LASA) scales. Health Qual Life Outcomes. 2014;12:187.
    1. Kumar SP. Utilization of brief pain inventory as an assessment tool for pain in patients with cancer: a focused review. Indian J Palliat Care. 2011;17(2):108–115.
    1. Kroenke K, Theobald D, Wu J, Tu W, Krebs EE. Comparative responsiveness of pain measures in cancer patients. J Pain. 2012;13(8):764–772.
    1. Cleeland CS, Mendoza TR, Wang XS, Chou C, Harle MT, Morrissey M, et al. Assessing symptom distress in cancer patients: the M.D. Anderson Symptom Inventory. Cancer. 2000;89(7):1634–1646.
    1. Richardson LA, Jones GW. A review of the reliability and validity of the Edmonton Symptom Assessment System. Curr Oncol. 2009;16(1):55.
    1. Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, et al. Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. J Pain Symptom Manag. 2011;41(6):1073–1093.
    1. Jeter K, Blackwell S, Burke L, Joyce D, Moran C, Conway EV, et al. Cancer symptom scale preferences: does one size fit all? BMJ Support Palliat Care. 2018;8(2):198–203.
    1. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol. 2010;63(11):1179–1194.
    1. Garcia SF, Cella D, Clauser SB, Flynn KE, Lad T, Lai JS, et al. Standardizing patient-reported outcomes assessment in cancer clinical trials: a patient-reported outcomes measurement information system initiative. J Clin Oncol. 2007;25(32):5106–5112.
    1. Fries JF, Krishnan E, Rose M, Lingala B, Bruce B. Improved responsiveness and reduced sample size requirements of PROMIS physical function scales with item response theory. Arthritis Res Ther. 2011;13(5):R147.
    1. Fries J, Rose M, Krishnan E. The PROMIS of better outcome assessment: responsiveness, floor and ceiling effects, and Internet administration. J Rheumatol. 2011;38(8):1759–1764.
    1. Rose M, Bjorner JB, Gandek B, Bruce B, Fries JF, Ware JE., Jr The PROMIS Physical Function item bank was calibrated to a standardized metric and shown to improve measurement efficiency. J Clin Epidemiol. 2014;67(5):516–526.
    1. Choi SW, Reise SP, Pilkonis PA, Hays RD, Cella D. Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Qual Life Res. 2010;19(1):125–136.
    1. Pilkonis PA, Yu L, Dodds NE, Johnston KL, Maihoefer CC, Lawrence SM. Validation of the depression item bank from the Patient-Reported Outcomes Measurement Information System (PROMIS) in a three-month observational study. J Psychiatr Res. 2014;56:112–119.
    1. Amtmann D, Kim J, Chung H, Bamer AM, Askew RL, Wu S, et al. Comparing CESD-10, PHQ-9, and PROMIS depression instruments in individuals with multiple sclerosis. Rehabil Psychol. 2014;59(2):220–229.
    1. Junghaenel DU, Schneider S, Stone AA, Christodoulou C, Broderick JE. Ecological validity and clinical utility of Patient-Reported Outcomes Measurement Information System (PROMIS(R)) instruments for detecting premenstrual symptoms of depression, anger, and fatigue. J Psychosom Res. 2014;76(4):300–306.
    1. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
    1. May C, Rapley T, Mair FS, Treweek S, Murray E, Ballini L, et al. Normalization process theory on-line users’ manual, toolkit and NoMAD instrument. 2015.
    1. Glasgow RE, McKay HG, Piette JD, Reynolds KD. The RE-AIM framework for evaluating interventions: what can it tell us about approaches to chronic illness management? Patient Educ Couns. 2001;44(2):119–127.
    1. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322–1327.
    1. US Department of Agriculture . Rural-urban continuum codes. 2013.
    1. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299–309.
    1. Storlie CB, Branda ME, Gionfriddo MR, Shah ND, Rank MA. Prediction of individual outcomes for asthma sufferers. Biostatistics. 2018;19(4):579–593.
    1. Cunanan KM, Carlin BP, Peterson KA. A practical Bayesian stepped wedge design for community-based cluster-randomized clinical trials: The British Columbia Telehealth Trial. Clin Trials. 2016;13(6):641–650.
    1. Ma J, Thabane L, Kaczorowski J, Chambers L, Dolovich L, Karwalajtys T, et al. Comparison of Bayesian and classical methods in the analysis of cluster randomized controlled trials with a binary outcome: the Community Hypertension Assessment Trial (CHAT) BMC Med Res Methodol. 2009;9:37.
    1. Belsley DA, Kuh E, Welsch RE. Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley; 1980.
    1. Bradley EH, Curry LA, Spatz ES, Herrin J, Cherlin EJ, Curtis JP, et al. Hospital strategies for reducing risk-standardized mortality rates in acute myocardial infarction. Ann Intern Med. 2012;156(9):618–626.
    1. Luo S, Lawson AB, He B, Elm JJ, Tilley BC. Bayesian multiple imputation for missing multivariate longitudinal data from a Parkinson’s disease clinical trial. Stat Methods Med Res. 2016;25(2):821–837.
    1. Epic: past accomplishments and futures challenges. Annual Epic Users’ Group Meeting; September 25–28, 2017; Verona, WI.

Source: PubMed

3
Iratkozz fel