This page is a repository of useful books and articles on all things EBM.

Books and user manuals

  1. Users' Guides to the Medical Literature: A Manual for Evidence-Based Clinical Practice. 
  2. User manual for trial sequential analysis (TSA). Copenhagen Trial Unit, Centre for Clinical Intervention Research, Copenhagen, Denmark. 2011. p. 1-115.


  1. Pocock SJ, et al. Statistical controvesies in reporting of clinical trials. J Am Coll Cardiol 2015;66:2648-62 (review of various aspects of clinical trial reporting, including secondary outcomes, intention-to-treat vs per-protocol, subgroup analyses, and interpreting unexpected/surprising findings)
  2. Pocock SJ, et al. Design of major randomized trials. J Am Coll Cardiol 2015;66:2757-66 (review of key design considerations for clinical trials)
  3. Pocock SJ, et al. Challenging issues in clinical trial design. J Am Coll Cardiol 2015;66:2886-98 (review of alternate clinical trial designs, including non-inferiority trials, factorial trials, and adaptive designs)

Generalizability (external validity)

  1. Rothwell PM. Factors that can affect the external validity of randomized controlled trials. PLoS Clin Trials 2006;1:e9.

Risk of bias (internal validity)

  1. Higgins JP, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928.
  2. Juni P, et al. The hazards of scoring the quality of clinical trials for meta-analysis. JAMA 1999;282:1054-60.
  3. Savovic J, et al. Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Ann Intern Med 2012;157:429-38.
  4. Hrobjartsson A, et al. Observer bias in randomized clinical trials with measurement scale outcomes: A systematic review of trials with both blinded and nonblinded assessors. CMAJ 2013;185:E201-11.
  5. Lane P. Handling drop-out in longitudinal clinical trials: A comparison of the LOCF and MMRM approaches. Pharm Stat 2008;7:93-106.
  6. Molnar FJ, et al. Have last-observation-carried-forward analyses caused us to favour more toxic dementia therapies over less toxic alternatives? A systematic review. Open Med 2009;3:e31-50.
  7. Kjaergard LL, et al. Reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Ann Intern Med 2001;135:982-9.

Results and statistics

  1. Pocock SJ, et al. Making sense of statistics in clinical trial reports. J Am Coll Cardiol 2015;66:2536-49 (fantastic review of fundamental statistical concepts used in clinical trials)
  2. Pereira TV, et al. Empirical evaluation of very large treatment effects of medical interventions. JAMA 2012;308:1676-84.
  3. Furukawa TA, et al. Can we individualize the 'number needed to treat'? An empirical study of summary effect measures in meta-analyses. Int J Epidemiol 2002;31:72-6.
  4. Freemantle N. Interpreting the results of secondary end points and subgroup analyses in clinical trials: Should we lock the crazy aunt in the attic? BMJ 2001;322:989-91.
  5. Hackshaw A, et al. Interpreting and reporting clinical trials with results of borderline significance. BMJ 2011;343:d3340.
  6. Schulz KF, et al. Multiplicity in randomised trials II: Subgroup and interim analyses. Lancet 2005;365:1657-61.
  7. Bassler D, et al. Stopping randomized trials early for benefit and estimation of treatment effects: Systematic review and meta-regression analysis. JAMA 2010;303:1180-7.
  8. Sun X, et al. Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ 2010;340:c117.
  9. Rothwell PM. Treating individuals 2. Subgroup analysis in randomised controlled trials: Importance, indications, and interpretation. Lancet 2005;365:176-86.
  10. Turgeon RD. Critically appraising randomized controlled trials: Is there substance in subgroups? UBC PSSJ 2013;1:19-21.
  11. Wallach JD, et al. Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials. JAMA Intern Med 2017;epub.

Non-inferiority trials

  1. Mulla SM, et al. How to use a noninferiority trial: Users' guides to the medical literature. JAMA 2012;308:2605-11
  2. Kaji AH, et al. Noninferiority trials: Is a new treatment almost as effective as another? JAMA 2015;313:2371-2
  3. Thornby KA, et al. Simplifying and interpreting the FACTS of noninferiority trials: A stepwise approach. Am J Health Syst Pharm 2014;71:1926
  4. Kaul S, et al. Good enough: A primer on the analysis and interpretation of noninferiority trials. Ann Intern Med 2006;145:62-9.
  5. Head SJ, et al. Non-inferiority study design: Lessons to be learned from cardiovascular trials. Eur Heart J 2012;33:1318-24.

Trials used as examples in NERDCAT-RCT

  1. HYVET Beckett NS, et al. Treatment of hypertension in patients 80 years of age or older. NEJM 2008;358:1887-98.
  2. HPS MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: A randomised placebo-controlled trial. Lancet 2002;360:7-22.
  3. ROCKET-AF Patel MR, et al. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. NEJM 2011;365:883-91.
  4. El-Khalili N, et al. Extended-release quetiapine fumarate (quetiapine XR) as adjunctive therapy in major depressive disorder (MDD) in patients with an inadequate response to ongoing antidepressant treatment: A multicentre, randomized, double-blind, placebo-controlled study. Int J Neuropsychopharmacol 2010;13:917-32.
  5. Wilt TJ, et al. Effectiveness of statin therapy in adults with coronary heart disease. Arch Intern Med 2004;164:1427-36.
  6. Tonelli M, et al. Efficacy of statins for primary prevention in people at low cardiovascular risk: A meta-analysis. CMAJ 2011;183:E1189-202.
  7. CAPRIE A randomised, blinded, trial of clopidogrel versus aspirin in patients at risk of ischaemic events (CAPRIE). Lancet 1996;348:1329-39.
  8. HOPE Yusuf S, et al. Effects of an angiotensin-converting-enzyme inhibitor, ramipril, on cardiovascular events in high-risk patients. NEJM 2000;342:145-53.
  9. FIELD Keech A, et al. Effects of long-term fenofibrate therapy on cardiovascular events 9795 people with type 2 diabetes mellitus (the FIELD study): Randomised controlled trial. Lancet 2005;366:1849-61.
  10. ELITE Pitt B. Randomised trial of losartan versus captopril in patients over 65 with heart failure (Evaluation of Losartan in the Elderly Study, ELITE). Lancet 1997;349:747-52.
  11. ELITE II Pitt B. Effect of losartan compared with captopril on mortality in patients with symptomatic heart failure: Randomised trial - the Losartan Heart Failure Survival Study ELITE II. Lancet 2000;355:1582-7.
  12. Nguyen-Khac E, et al. Glucocorticoids plus N-acetylcysteine in severe alcoholic hepatitis. NEJM 2011;365:1781-9.
  13. CONDOR Chan FK, et al. Celecoxib versus omeprazole and diclofenac in patients with osteoarthritis and rheumatoid arthritis (CONDOR): A randomised trial. Lancet 2010;376:173-9.
  14. UKPDS 38 UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 1998;317:703-13.
  15. JUPITER Ridker PM, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. NEJM 2008;359:2195-207.
  16. Ridker PM, et al. Rosuvastatin in the primary prevention of cardiovascular disease among patients with low levels of low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: Rationale and design of the JUPITER trial. Circulation 2003;108:2292-7.
  17. PHS Steering Committee of the Physicians' Health Study Research Group. Final report of the aspirin component of the ongoing Physicians' Health Study. NEJM 1989;321:129-35.
  18. WHS Ridker PM, et al. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. NEJM 2005;352:1293-304.
  19. RE-LY Connolly SJ, et al. Dabigatran versus warfarin in patients with atrial fibrillation. NEJM 2009;361:1139-51.
  20. RESET Kim BK, et al. A new strategy for discontinuation of dual antiplatelet therapy: The RESET Trial (REal Safety and Efficacy of 3-month dual antiplatelet Therapy following Endeavor zotarolimus-eluting stent implantation). J Am Coll Cardiol 2012;60:1340-8.
  21. CREDO Steinhubl SR, et al. Early and sustained dual oral antiplatelet therapy following percutaneous coronary intervention: A randomized controlled trial. JAMA 2002;288:2411-20.
  22. EINSTEIN-PE Buller HR, et al. Oral rivaroxaban for the treatment of symptomatic pulmonary embolism. NEJM 2012;366:1287-97.
  23. Hart RG, et al. Antithrombotic therapy to prevent stroke in patients with atrial fibrillation: A meta-analysis. Ann Intern Med 1999;131:492-501.

Systematic review +/- meta-analysis

  1. Royle P, et al. Literature searching for randomized controlled trials used in Cochrane reviews: Rapid versus exhaustive searches. Int J Technol Assess Health Care 2003;19:591-603.
  2. Scott I, et al. Cautionary tales in the interpretation of systematic reviews of therapy trials. Intern Med J 2006;36:587-99.
  3. Pogue J, et al. Overcoming the limitations of current meta-analysis of randomised controlled trials. Lancet 1998;351:47-52.
  4. Tierney JF, et al. Individual participant data (IPD) meta-analyses of randomized controlled trials: Guidance on their use. PLoS Med 2015;12:e1001855.

Risk of bias assessment

  1. Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group guidelines

Publication bias and grey literature

  1. Ioannidis JP. Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. JAMA 1998;279:281-6.
  2. Hopewell S, et al. Publication bias in clinical trials due to statistical significance or direction of trial results. Cochrane Database Syst Rev 2009;(1):MR000006.
  3. McAuley L, et al. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? Lancet 2000;356:1228-31.
  4. Jones CW, et al. Non-publication of large randomized clinical trials: Cross sectional analysis. BMJ 2013;347:f6104.

Selective outcome reporting bias

  1. Hart B, et al. Effect of reporting bias on meta-analyses of drug trials: Reanalysis of meta-analyses. BMJ 2012;344:d7202.
  2. Chan AW, et al. Empirical evidence for selective reporting of outcomes in randomized trials: Comparison of protocols to published articles. JAMA 2004;291:2457-65.
  3. Chan AW, et al. Outcome reporting bias in randomized trials funded by the Canadian Institute of Health Research. CMAJ 2004;171:735-40.

Results: Heterogeneity and fixed- versus random-effects models

  1. Higgins JP, et al. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60.
  2. Guyatt GH, et al. GRADE guidelines: 7. Rating the quality of evidence - Inconsistency. J Clin Epidemiol 2011;64:1294-302.
  3. Fleiss JL. The statistical basis of meta-analysis. Stat Methods Med Res 1993;2:121-45.
  4. Poole C, et al. Random-effects meta-analyses are not always conservative. Am J Epidemiol 1999;150:469-75.
  5. Engels EA, et al. Heterogeneity and statistical significance in meta-analysis: An Empirical study of 125 meta-analyses. Stat Med 2000;19:1707-28.

Causality determination

  1. Hill AB. The environment and disease: Association or causation. Proc R Soc Med 1965;58:295-300 (Sir Bradford-Hill's 9 proposed criteria for determining causation)

Clinical prediction rules

  1. McGinn TG, et al. Users' guides to the medical literature: XXII: How to use articles about clinical decision rules. JAMA 2000;284:79-84.
  2. Braitman LE, et al. Predicting clinical states in individual patients. Ann Intern Med 1996;125:406-12.
  3. Laupacis A, et al. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA 1997;277:488-94.
  4. Stiell IG, et al. Methodologic standards for the development of clinical decision rules in emergency medicine. Ann Emerg Med 1999;33:437-47.
  5. Altman DG, et al. What do we mean by validating a prognostic model? Stat Med 2000;19:453-73.
  6. Moons KG, et al. Prognosis and prognostic research: What, why, and how? BMJ 2009;338:b375.
  7. Moons KG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012;98:683-90.

Generalizability of clinical prediction rules

  1. Justice AC, et al. Assessing the generalizability of prognostic information. Ann Intern Med 1999;130:515-24.
  2. Altman DG, et al. Prognosis and prognostic research: Validating a prognostic model. BMJ 2009;338:b605.
  3. Moons KG, et al. Prognosis and prognostic research: Application and impact of prognostic models in clinical practice. BMJ 2009;338:b606.
  4. Moons KG, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012;98:691-8.

Risk of bias in studies of clinical prediction rules

  1. Hayden JA, et al. Assessing bias in studies of prognostic factors. Ann Intern Med 2013;158:280-6.

Statistics used in studies of diagnostic tests and clinical prediction rules

  1. Cook NR. Statistical evaluation of prognostic versus diagnostic models: Beyond the ROC curve. Clin Chem 2008;54:17-23.
  2. Concato J, et al. The risk of determining risk with multivariable models. Ann Intern Med 1993;118:201-10.
  3. Cook EF, et al. Empiric comparison of multivariable analytic techniques: Advantages and disadvantages of recursive partitioning analysis. J Chronic Dis 1984;37:721-31.
  4. Moons KG, et al. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 2006;59:1092-101.
  5. Janssen KJ, et al. Missing covariate data in medical research: To impute is better than to ignore. J Clin Epidemiol 2010;63:721-7.
  6. Peduzzi P, et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995;48:1503-10.

Clinical prediction rules used as examples in NERDCAT-CPR

  1. CHADS2 derivation study Gage BF, et al. Validation of clinical classification schemes for predicting stroke: Results from the National Registry of Atrial Fibrillation. JAMA 2001;285:2864-70.
  2. Rothberg MB, et al. Risk factor model to predict venous thromboembolism in hospitalized medical patients. J Hosp Med 2011;6:202-9.