Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician

Lawrence Blonde1, Kamlesh Khunti2, Stewart B. Harris3, Casey Meizinger4, Neil Skolnik5
1Ochsner Diabetes Clinical Research Unit, Department of Endocrinology, Frank Riddick Diabetes Institute, Ochsner Medical Center, New Orleans, LA, USA
2Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK
3Schulich School of Medicine and Dentistry, Centre for Studies in Family Medicine, Western Centre for Public Health and Family Medicine, Western University, London, ON, Canada
4Department of Family Medicine, Abington Jefferson Health, Abington, PA, 19001, USA
5Abington Family Medicine, Jenkintown, PA, USA

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Luce BR, Drummond M, Jönsson B, et al. EBM, HTA, and CER: clearing the confusion. Milbank Q. 2010;88:256–76.

Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med. 2016;375:2293–7.

Barnish MS, Turner S. The value of pragmatic and observational studies in health care and public health. Pragmat Obs Res. 2017;8:49–55.

Fortin M, Dionne J, Pinho G, Gignac J, Almirall J, Lapointe L. Randomized controlled trials: do they have external validity for patients with multiple comorbidities? Ann Fam Med. 2006;4(2):104–8.

FDA. Developing a framework for regulatory use of real-world evidence; Public Workshop. https://www.gpo.gov/fdsys/pkg/FR-2017-07-31/pdf/2017-16021.pdf . Accessed 08 Sep 2017.

EMA. Update on real world evidence data collection. 10 March 2016. https://ec.europa.eu/health//sites/health/files/files/committee/stamp/2016-03_ stamp4/4_ real_world_evidence_ema_presentation.pdf. Accessed 08 Sep 2017.

Batrouni M, Comet D, Meunier JP. Real world studies, challenges, needs and trends from the industry. Value Health. 2014;17:A587–8.

Goodman CS. National Information Center on Health Services Research and Health Care Technology (NICHSR): HTA 101, 2017. https://www.nlm.nih.gov/nichsr/hta101/ta10103.html . Accessed Feb 2018.

Malone DC, Brown M, Hurwitz JT, Peters L, Graff JS. Real-world evidence: useful in the real world of US payer decision making? How? When? And what studies? Value Health. 2018;21(3):326–33.

Cattell J, Groves P, Hughes B, Savas S. How can pharmacos take advantage of the real-world data opportunity in healthcare? McKinsey and Company, 2011. https://www.mckinsey.com/~/media/mckinsey/dotcom/client_service/Pharma%20and%20Medical%20Products/PMP%20NEW/PDFs/Pharma%20%20RWD%20opportunity%20October%202011.ashx . Accessed Feb 2018.

ABPI. The vision for real world data—harnessing the opportunities in the UK. Demonstrating value with real world data 2017. http://www.abpi.org.uk/media/1378/vision-for-real-world-data.pdf . Accessed 22 Jan 2018.

Schwartz D, Lellouch J. Explanatory and pragmatic attitudes in therapeutical trials. J Clin Epidemiol. 2009;62(5):499–505.

Davies J, Martinex M, Martina R, et al. Retrospective indirect comparison of alectinib phase II data vs ceritinib real-world data in ALK + NSCLC after progression on crizotinib. Ann Oncol. 2017;28(suppl_2): ii28-ii51. 10.

Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375:454–63.

Dang A, Vallish BN. Real world evidence: an Indian perspective. Perspect Clin Res. 2016;7:156–60.

Sox HC, Lewis RJ. Pragmatic trials: practical answers to “real world” questions. JAMA. 2016;316:1205–6.

Tunis SR, Stryer DB, Clancy CM. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA. 2003;290:1624–32.

Dubois RW. Is the real-world evidence or hypothesis: a tale of two retrospective studies. J Comp Eff Res. 2015;4(3):199–201.

Clinicaltrials.gov. Studies for “Diabetes Mellitus, Type 2”. https://clinicaltrials.gov/ct2/results?cond=Diabetes+Mellitus%2C+Type+2&term=&cntry=&state=&city=&dist= . Accessed 21 Aug 2018.

Carls GS, Tuttle E, Tan RD, et al. Understanding the gap between efficacy in randomized controlled trials and effectiveness in real-world use of GLP-1 RA and DPP-4 therapies in patients with type 2 diabetes. Diabetes Care. 2017;40:1469–78.

Edelman SV, Polonsky WH. Type 2 diabetes in the real world: the elusive nature of glycemic control. Diabetes Care. 2017;40:1425–32.

McGovern A, Hinchliffe R, Munro N, de Lusignan S. Basing approval of drugs for type 2 diabetes on real world outcomes. BMJ. 2015;351:h5829.

Zhou FL, Ye F, Gupta V, et al. Older adults with type 2 diabetes (T2D) experience less hypoglycemia when switching to insulin glargine 300 U/mL (Gla-300) vs other basal insulins (DELIVER 3 study). Poster 986-P, American Diabetes Association (ADA) 77th Scientific Sessions, San Diego, CA, US, June 10, 2017.

Blonde L, Merilainen M, Karwe V, Raskin P. Patient-directed titration for achieving glycaemic goals using a once-daily basal insulin analogue: an assessment of two different fasting plasma glucose targets-the TITRATE™ study. Diabetes Obes Metab. 2009;11:623–31.

Gerstein HC, Yale JF, Harris SB, et al. A randomized trial of adding insulin glargine vs. avoidance of insulin in people with type 2 diabetes on either no oral glucose-lowering agents or submaximal doses of metformin and/or sulphonylureas. The Canadian INSIGHT (Implementing New Strategies with Insulin Glargine for Hyperglycaemia Treatment) Study. Diabet Med. 2006;23:736–42.

Meneghini L, Koenen C, Weng W, Selam JL. The usage of a simplified self-titration dosing guideline (303 Algorithm) for insulin detemir in patients with type 2 diabetes—results of the randomized, controlled PREDICTIVE™ 303 study. Diabetes Obes Metab. 2007;9:902–13.

Garrison LP Jr, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health. 2007;10:326–35.

Roche N, Reddel H, Martin R, et al. Quality standards for real-world research. Focus on observational database studies of comparative effectiveness. Ann Am Thorac Soc. 2014;11(Suppl 2):S99–104.

Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N Engl J Med. 2000;22(342):1878–86.

Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med. 2000;342:1887–92.

Golder S, Loke YK, Bland M. Meta-analyses of adverse effects data derived from randomised controlled trials as compared to observational studies: methodological overview. PLoS Med. 2011;8:e1001026.

McMurry TL, Hu Y, Blackstone EH, Kozower BD. Propensity scores: methods, considerations, and applications. J Thorac Cardiovasc Surg. 2015;150:14–9.

Patsopoulos NA. A pragmatic view on pragmatic trials. Dialogues Clin Neurosci. 2011;13:217–24.

Frieden TR. Evidence for health decision making—beyond randomized, controlled trials. N Engl J Med. 2017;377:465–75.

Ye F, Agarwal R, Kaur A, et al. Real-world assessment of patient characteristics and clinical outcomes of early users of the new insulin glargine 300U/mL. Poster 943-P, American Diabetes Association (ADA) 76th Scientific Sessions, New Orleans, LA, US. June 11, 2016.

Zhou FL, Ye F, Berhanu P, et al. Real-world evidence concerning clinical and economic outcomes of switching to insulin glargine 300 units/mL vs other basal insulins in patients with type 2 diabetes using basal insulin. Diabetes Obes Metab. 2018;20(5):1293–7.

Bolli GB, Riddle MC, Bergenstal RM, et al. New insulin glargine 300 U/ml compared with glargine 100 U/ml in insulin-naïve people with type 2 diabetes on oral glucose-lowering drugs: a randomized controlled trial (EDITION 3). Diabetes Obes Metab. 2015;17:386–94.

Riddle MC, Bolli GB, Ziemen M, et al. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using basal and mealtime insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 1). Diabetes Care. 2014;37:2755–62.

Yki-Järvinen H, Bergenstal R, Ziemen M, et al. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using oral agents and basal insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 2). Diabetes Care. 2014;37:3235–43.

Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383:999–1008.

Stratton IM, Adler AI, Neil HA, et al. Association with glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321:405–12.

Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. JAMA. 2002;287:2563–9.

Freemantle N, Danchin N, Calvi-Gries F, Vincent M, Home PD. Relationship of glycaemic control and hypoglycaemic episodes to 4-year cardiovascular outcomes in people with type 2 diabetes starting insulin. Diabetes Obes Metab. 2016;18:152–8.

Nathan DM, Buse JB, Kahn SE, et al. Rationale and design of the glycemia reduction approaches in diabetes: a comparative effectiveness study (GRADE). Diabetes Care. 2013;36:2254–61.

Wermeling PR, Gorter KJ, Stellato RK, et al. Effectiveness and cost-effectiveness of 3-monthly versus 6-monthly monitoring of well-controlled type 2 diabetes patients: a pragmatic randomised controlled patient-preference equivalence trial in primary care (EFFIMODI study). Diabetes Obes Metab. 2014;16:841–9. https://doi.org/10.1111/dom.12288 .

Young LA, Buse JB, Weaver MA, et al. Three approaches to glucose monitoring in non-insulin treated diabetes: a pragmatic randomized clinical trial protocol. BMC Health Serv Res. 2017;17:369.

Furler J, O’Neal D, Speight J, et al. Supporting insulin initiation in type 2 diabetes in primary care: results of the Stepping Up pragmatic cluster randomised controlled clinical trial. BMJ. 2017;356:j783.

Choudhry NK, Isaac T, Lauffenburger JC, et al. Rationale and design of the Study of a Tele-pharmacy Intervention for Chronic diseases to Improve Treatment adherence (STIC2IT): a cluster-randomized pragmatic trial. Am Heart J. 2016;180:90–7.

Green JB, Bethel MA, Armstrong PW, et al. Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2015;373:232–42.

Holman RR, Bethel MA, Mentz RJ, et al. Effects of once-weekly exenatide on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2017;377:1228–39.

Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373:2117–28.

Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–57.

Kosiborod M, Cavender MA, Fu AZ, et al. Lower risk of heart failure and death in patients initiated on sodium-glucose cotransporter-2 inhibitors versus other glucose-lowering drugs: the CVD-REAL study (comparative effectiveness of cardiovascular outcomes in new users of sodium-glucose cotransporter-2 inhibitors). Circulation. 2017;136(3):249–59.

STROBE. STROBE Statement: Strengthening the reporting of observational studies in epidemiology. https://www.strobe-statement.org/index.php?id=available-checklists . Accessed 26 Sep 2018.

Zwarenstein M, Treweek S, Gagnier JJ, et al. Pragmatic Trials in Healthcare (Practihc) group. Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ. 2008;337:a2390.

Pitt B, Zannad F, Remme WJ, et al. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. N Engl J Med. 1999;341(10):709–17.

Freemantle N, Marston L, Walters K, et al. Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ. 2013;347:f6409.

Penning de Vries BBL, Groenwold RHH. Cautionary note: propensity score matching does not account for bias due to censoring. Nephrol Dial Transplant. 2017;1–3.

Zhang R, Wang Y, Liu B, et al. Clinical data quality problems and countermeasure for real world study. Front Med. 2014;8:352–57.

Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507.