Skip to main content

Abstract

Comparative effectiveness research (CER) guidelines have been developed to direct the field toward the most rigorous study methodologies. A challenge, however, is how to ensure the best evidence is generated, and how to translate methodologically complex or nuanced CER findings into usable medical evidence. To reach that goal, it is important that both researchers and end users of CER output become knowledgeable about the elements that impact the quality and interpretability of CER. This paper distilled guidance on CER into a practical tool to assist both researchers and nonexperts with the critical review and interpretation of CER, with a focus on issues particularly relevant to CER in oncology.

Supplementary Material

File (cer-2018-0007 supplementary data.docx)

References

Papers of special note have been highlighted as: •• of considerable interest
1.
Federal Coordinating Council for Comparative Effectiveness Research (FCCCER). FCCCER definition of Comparative Effectiveness. US National Library of Medicine (2017). https:/osp.od.nih.gov/wp-content/uploads/FCCCER-report-to-the-president-and-congress-2009.pdf
2.
Price-Haywood EG. Clinical comparative effectiveness research through the lens of healthcare decision makers. Ochsner J. 15(2), 154–161 (2015).
3.
Blumenthal GM, Kluetz PG, Schneider J, Goldberg KB, McKee AE, Pazdur R. Oncology drug approvals: evaluating end points and evidence in an era of breakthrough therapies. Oncologist 22(7), 762–767 (2017).
4.
Patient-Centered Outcomes Research Institute (PCORI). Patient-Centered Outcomes Research Institute (PCORI) (2017). www.pcori.org/
5.
International Society for Pharmacoeconomics and Outcomes Research (ISPOR). The leading global scientific and educational organization for HEOR and its use in decision making to improve health (2017). www.ispor.org/
6.
International Society for Pharmacoepidemiology. International Society for Pharmacoepidemiology (ISPE), Home Page (2017). www.pharmacoepi.org
7.
Agency for Healthcare Research and Quality (AHRQ). Agency for Healthcare Research and Quality (AHRQ). Home Page (2017). www.ahrq.gov/
8.
European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Home Page (2017). www.encepp.eu/
9.
STROBE. STROBE Statement. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) (2017). www.strobe-statement.org/index.php?id=strobe-home
10.
GRACE. Good Research for Comparative Effectiveness Initiative (GRACE) (2017). www.graceprinciples.org/
11.
National Pharmaceutical Council (NPC). CER Collaborative Initiative (2017). www.npcnow.org/issue/cer-collaborative-initiative
12.
Benchimol EI, Smeeth L, Guttmann A et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 12(10), e1001885 (2015).
13.
Browman GP. Special series on comparative effectiveness research: challenges to real-world solutions to quality improvement in personalized medicine. J. Clin. Oncol. 30(34), 4188–4191 (2012).
14.
Dorey FJ. Statistics in brief: statistical power: what is it and when should it be used? Clin. Orthop. Relat. Res. 469(2), 619–620 (2011).
15.
Brookhart MA, Sturmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: challenges and potential approaches. Med. Care 48(6 Suppl.), S114–S120 (2010).
•• Reviews approaches to control for confounding in pharmacoepidemiology database research.
16.
Suissa S, Garbe E. Primer: administrative health databases in observational studies of drug effects – advantages and disadvantages. Nat. Clin. Pract. Rheumatol. 3(12), 725–732 (2007).
17.
Lund JL, Richardson DB, Sturmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr. Epidemiol. Rep. 2(4), 221–228 (2015).
•• Reviews methodology to implement the active comparator, new user study design.
18.
Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am. J. Epidemiol. 158(9), 915–920 (2003).
19.
Setoguchi S, Gerhard T et al. Comparator Selection. Chapter 5. In: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. Velentgas P, Dreyer NA, Nourjah P et al. Eds). Agency for Healthcare Research and Quality. Rockville, MD, USA (2013).
20.
Groenwold RH, Hoes AW, Hak E. Confounding in publications of observational intervention studies. Eur. J. Epidemiol. 22(7), 413–415 (2007).
21.
Glynn RJ, Schneeweiss S, Sturmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin. Pharmacol. Toxicol. 98(3), 253–259 (2006).
22.
Tan FES. Confounding in (non-) randomized comparison studies. OA Epidemiol. 1(3), 21 (2013).
23.
Streeter AJ, Lin NX, Crathorne L et al. Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. J. Clin. Epidemiol. 87, 23–34 (2017).
24.
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 49(12), 1373–1379 (1996).
25.
Cepeda MS, Boston R, Farrar JT, Strom BL. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am. J. Epidemiol. 158(3), 280–287 (2003).
26.
Suissa S. Immortal time bias in pharmaco-epidemiology. Am. J. Epidemiol. 167(4), 492–499 (2008).
27.
Velentgas P, Dreyer NA, Nourjah P, Smith SR, Torchia MM. Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. AHRQ. Agency for Healcare Research and Quality. Rockville, MD, USA (2013).
•• Contains detailed information on best practices for comparative effectiveness research (CER) protocol development.
28.
Dreyer NA, Bryant A, Velentgas P. The GRACE checklist: a validated assessment tool for high quality observational studies of comparative effectiveness. J. Manag. Care Spec. Pharm. 22(10), 1107–1113 (2016).
29.
Shin S, Park CM, Kwon H, Lee KH. Erlotinib plus gemcitabine versus gemcitabine for pancreatic cancer: real-world analysis of Korean national database. BMC Cancer 16, 443 (2016).
30.
Moore MJ, Goldstein D, Hamm J et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a Phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J. Clin. Oncol. 25(15), 1960–1966 (2007).
31.
American Cancer Society. Pancreatic Cancer Survival Rates, by Stage. (2016). www.cancer.org/cancer/pancreatic-cancer/detection-diagnosis-staging/survival-rates.html
32.
Shin DW, Cho B, Guallar E. Korean National Health Insurance Database. JAMA Intern. Med. 176(1), 138 (2016).
33.
Yeh JM, Tramontano AC, Hur C, Schrag D. Comparative effectiveness of adjuvant chemoradiotherapy after gastrectomy among older patients with gastric adenocarcinoma: a SEER-Medicare study. Gastric Cancer 20(5), 811–824 (2017).
34.
Smalley SR, Benedetti JK, Haller DG et al. Updated analysis of SWOG-directed intergroup study 0116: a Phase III trial of adjuvant radiochemotherapy versus observation after curative gastric cancer resection. J. Clin. Oncol. 30(19), 2327–2333 (2012).
35.
Cunningham D, Allum WH, Stenning SP et al. Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N. Engl. J. Med. 355(1), 11–20 (2006).
36.
Doria-Rose VP, Marcus PM. Death certificates provide an adequate source of cause of death information when evaluating lung cancer mortality: an example from the Mayo Lung Project. Lung Cancer 63(2), 295–300 (2009).
37.
Penson DF, Albertsen PC, Nelson PS, Barry M, Stanford JL. Determining cause of death in prostate cancer: are death certificates valid? J. Natl Cancer Inst. 93(23), 1822–1823 (2001).
38.
Percy C, Ries LG, Van Holten VD. The accuracy of liver cancer as the underlying cause of death on death certificates. Public Health Rep. 105(4), 361–367 (1990).
39.
Percy C, Stanek E 3rd, Gloeckler L. Accuracy of cancer death certificates and its effect on cancer mortality statistics. Am. J. Public Health 71(3), 242–250 (1981).