List of readings recommended in the feedback text of each assessment question. List of readings will be posted as course materials for each week are released.
1. http://en.wikipedia.org/wiki/Randomized_controlled_trial - A straightforward and useful overview of randomized controlled trial, including details of different randomization method
3. http://en.wikipedia.org/wiki/Cohort_study - Overviews of observational study designs; concise and straightforward reading.
4. Appelbaum PS, Roth LH, Lidz CW, Benson P, Winslade W. False hopes and best data: Consent to research and the therapeutic misconception. Hastings Cent Rep 1987;17:20-4. This paper explains the therapeutic misconception, an essential concept in the ethics of clinical research. http://onlinelibrary.wiley.com/doi/10.2307/3562038/abstract
5. Grady C. Payment of clinical research subjects. The Journal of Clinical Investigation 2005;115:1681-7. Although payment of research subjects is only one of many issues that need to be considered, this is a thorough analysis of the considerations involved. http://www.jci.org/articles/view/25694
6. The stages of prevention. http://tinyurl.com/msbj8s4
7. A concise set of definitions of the phases of drug development and the phases of clinical trials is found at the following website: http://en.wikipedia.org/wiki/Phases_of_clinical_research
1. Phases of clinical research. http://en.wikipedia.org/wiki/Phases_of_clinical_research
4. Food and Drug Administration. Guidance for Industry: Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers. U. S. Department of Health and Human Services, July 2005. http://www.fda.gov/downloads/Drugs/Guidances/UCM078932.pdf
1. Phases of Clinical Trials http://www.cancer.net/all-about-cancer/clinical-trials/phases-clinical-trials
2. Phase 2 Clinical Trial Design http://clincancerres.aacrjournals.org/content/12/7/1966.long
3. Intention to treat analysis http://en.wikipedia.org/wiki/Intention-to-treat_analysis
4. Per protocol analysis http://en.wikipedia.org/wiki/Analysis_of_clinical_trials#Per_protocol
5. Effects of the Endpoint Adjudication Process on the Results of a Randomised Controlled Trial http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0055807
7. Don't Mess with the DSMB http://www.nejm.org/doi/full/10.1056/NEJMe1007445
8. Plint AC, Johnson DW, Patel H. et al. Epinephrine and Dexamethasone in Children with Bronchiolitis. N Engl J Med 2009; 360:2079-2089. http://www.nejm.org/doi/full/10.1056/NEJMoa0900544
1. Treweek S, Zwarenstein M. Making trials matter: pragmatic and explanatory trials and the problem of applicability. Trials 2009; 10:37 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2700087/ A narrative review comparing efficacy (explanatory) and effectiveness (pragmatic) trials; this is freely available on the internet. Note that the authors argue that more trials should be pragmatic in design; this is not necessarily the opinion of all.
3. McNeil DG. African Trial of H.I.V. Drugs Fails http://tinyurl.com/owwdzq7 - This New York Times article describes the failure of a clinical trial due to non-compliance, which is discussed in this lecture
6. Temple R, Ellenberg SS. Placebo-controlled trials and active-control trials in the evaluation of new treatments - Part 1: Ethical and scientific issues. Ann Intern Med 2000; 133: 455-63. http://annals.org/article.aspx?articleid=713868
2. http://www.fda.gov/downloads/Training/ClinicalInvestigatorTrainingCourse/UCM283378.pdf - This is a pdf of a PowerPoint presentation on clinical trial endpoints. It covers the concept very effectively.
3. Jeremy S Furyk, Carl J O’Kane, Peter J Aitken, Colin J Banks and David A Kault. Fast versus slow bandaid removal: a randomised trial. Med J Aust 2009; 191 (11): 682-683 https://www.mja.com.au/journal/2009/191/11/fast-versus-slow-bandaid-removal-randomised-trial - This paper describes the study mentioned in the question, in case you want to take a closer look at the results.
5. Herring, R. Tips for writing a statistical analysis plan. AmStatNews, http://magazine.amstat.org/blog/2013/04/01/writingtipsfunding2013/
6. Fletcher J. Subgroup analyses: how to avoid being misled. BMJ. 2007 July 14; 335(7610): 96–97. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1914513/
7. U. S. Food and Drug Administration. Guidance for clinical trial sponsors: Establishment and operation of clinical trial data monitoring committees. U. S. Department of Health and Human Services, 2006. http://www.fda.gov/downloads/Regulatoryinformation/Guidances/ucm127073.pdf
1. Campbell MJ, Julious SA, Altman DG. , Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons. BMJ 1995;311:1145. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2551061/
2. Type I and Type II errors. http://en.wikipedia.org/wiki/Type_I_and_type_II_errors
3. Gerstman BB, Introduction to hypothesis testing. http://www.sjsu.edu/faculty/gerstman/StatPrimer/hyp-test.pdf
4. Freedman LS. Tables of number of patients required in clinical trials using the log-rank test. Stat Med 1982;1:121-129. http://onlinelibrary.wiley.com/doi/10.1002/sim.4780010204/abstract
5. Statpages: http://statpages.org/ An excellent compendium of online statistical tools
6. Emory University Tutorials. Statistical power. http://www.psychology.emory.edu/clinical/bliwise/Tutorials/SPOWER/spowspdef.htm
An easy-to-follow, short interactive online guide and tutorial, designed for students learning about power calculations. As well as overviewing the figure above, provides a well-set-out section on factors that affect statistical power which is highly recommended.
7. Lenth RB. Java applets for power and sample size. http://homepage.stat.uiowa.edu/~rlenth/Power/
8. Dupont WD, Plummer WD. PS: Power and sample size calculation. http://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize
10. Halpern SD, Karlawish JH, Berlin JA. The continuing unethical conduct of underpowered clinical trials. J Am Med Assoc 2002;288(3):358-62 - http://www.ncbi.nlm.nih.gov/pubmed/12117401
4. Item 15, Consort Statement on Reporting of Clinical Trials.http://www.consort-statement.org/consort-statement/13-19---results/item15_baseline-data/. The CONSORT Statement provides valuable guidance and a checklist for authors preparing a report of a randomized clinical trial. Each of the 25 recommendations is stated briefly and accompanied by a longer explanation of the rationale for the recommendation. Though the intended audience is authors of articles about trials, the CONSORT Statement also provides valuable guidance for investigators planning a trial.
5. Barnett, AG et al. Regression to the Mean: What it is and How to Deal with it. http://ije.oxfordjournals.org/content/34/1/215.full
6. Randomization http://en.wikipedia.org/wiki/Randomization A useful introduction to randomization with many links and references
7. Wikipedia. Intention-to-treat analysis. http://en.wikipedia.org/wiki/Intention_to_treat_analysisA brief but useful explanation of an intention-to-treat analysis and its rationale.
8. Wikipedia. Randomized controlled trial, Section 4. http://en.wikipedia.org/wiki/Randomized_controlled_trial
9. CONSORT Statement. Permuted block design. http://www.consort-statement.org/?o=1155
10. Wikipedia. Minimization. http://en.wikipedia.org/wiki/Minimisation_(clinical_trials)
1. Wikipedia. Linear regression. http://en.wikipedia.org/wiki/Linear_regression. This article is a thorough introduction to linear regression, from the theoretical basis, to related methods, to applications. This article goes beyond what is covered in this lecture but is an easily accessible resource for further reading and a deeper understanding of this topic.
2. The Chi-square test http://ccnmtl.columbia.edu/projects/qmss/the_chisquare_test/about_the_chisquare_test.html This article provides a good introduction to the chi-square test and its uses. Note on the left hand side of the web page that there are several other freely available modules that may be of interest and useful.
3. Wikipedia. Logistic regression. http://en.wikipedia.org/wiki/Logistic_regression - This article provides a thorough introduction to logistic regression, from the theoretical basis, to related methods, to applications. It is an easily accessible resource for further reading and a deeper understanding of this topic.
4. HealthKnowledge. Role of chance, bias and confounding in epidemiological studies. http://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/chance-bias-confounding An accessible introduction to confounding and bias, including different types of bias that may be encountered.
5. Wikipedia - Confounding. http://en.wikipedia.org/wiki/Confounding
6. Boston University, Online module on principles of survival analysis. http://sph.bu.edu/otlt/MPH-Modules/BS/BS704_Survival/.
This module provides clear explanations of survival analysis and the calculations required to calculate common estimates of survival functions, It also includes a good discussion of the interpretation of survival curves and the Kaplan-Meier approach as well as the log-rank test and Cox proportional hazards model analysis.
7. Bland JM, Altman DG. The log-rank test. BMJ 2004;328:1073. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC403858/
8. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC478551/ - This is a very readable article that describes the Cox proportional hazards model, the interpretation of the hazard ratio with clinically relevant examples, and discusses how to explain these results to patients.
1. Identifying and Avoiding Bias in Research. Pannucci CJ, Wilkins EG. Plast Reconstr Surg. 2010 August ; 126(2): 619–625 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917255/ - This is a review article on bias and how to avoid it, also gives an overview on confounding and internal/ external validity.
2. Definition of a confounding variable and the different types http://en.wikipedia.org/wiki/Confounding
3. Levin KA. Issues of chance, bias, confounding and contamination. Evidence-Based Dentistry (2005) 6, 102–103. doi:10.1038/sj.ebd.6400356 http://www.nature.com/ebd/journal/v6/n4/full/6400356a.html - This paper gives an overview on types of biases and measures that can be taken to help minimize these biases. It is fine to just read the abstract here.
1. Visit the FDA website page “Surveillance: Post Drug-Approval Activities” and read about the following programs and evaluations at: http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/default.htm This exercise will describe:
· Postmarketing Surveillance Programs
· Regulations and Policies and Procedures for Postmarketing Surveillance Programs
· Postmarket Drug and Biologic Safety Evaluations
2. Visit the CDC Surveillance Resource Center at http://www.cdc.gov/surveillancepractice/index.html to become familiar with public health surveillance systems and the collection, analysis, and use of data to target public health prevention. By reviewing the tools in this resource center the you will become familiar with:
· Interactive database and web query systems for public health surveillance
· Methods for conducting and evaluating surveillance systems, and data standardization
· Legal, ethical and policy issues for collecting and sharing data
3. Nsubuga P. Disease Control Priorities in Developing Countries. 2nd edition. Chapter 53 Public Health Surveillance: A Tool for Targeting and Monitoring Interventions.http://www.ncbi.nlm.nih.gov/books/NBK11770/ - A comprehensive review of public health surveillance, including important definitions, objectives, principles, use and maintenance of surveillance systems. Includes some interesting examples of surveillance in major outbreaks; and where the future of surveillance may be heading
4. Gibbons RD, Amatya AK, Brown CH, Hur K, Marcus SM, Bhaumik DK, Mann JJ. Post-Approval Drug Safety Surveillance. Annu Rev Public Health. 2010;31:419-37. http://www.annualreviews.org/eprint/3zrwxYaQ6xKHi2pXVdyK/full/10.1146/annurev.publhealth.012809.103649 - This article outlines key strategies for identifying adverse drug events after a drug has been released in the marketplace. In addition, strengths and limitations of these strategies are addressed. While some content is beyond the scope of this lecture it nevertheless makes for interesting and well-written, thought-generating reading.
5. “Guidance for Industry and FDA Staff Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data” Food and Drug Administration, U.S. Department of Health and Human Services. May, 2013. This is the FDA-suggested guidance on safety studies using electronic healthcare data.http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM243537.pdf
6. Sakaeda T, Tamon A, Kadoyama K, Okuno Y. “Data Mining of the public version of the FDA Adverse Event Reporting System.” Int J Med Sci. 2013 Apr 25;10(7):796-803. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689877/
7. FDA Adverse Event Reporting System (FAERS) (formerly AERS)http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htmThis reference-based exercise will help you understand: What is FAERS? How Does FDA Use the Information in FAERS? Who Reports to FAERS? How Can I Report an Adverse Event or Medication Error to FDA? Do FAERS Data Have Limitations? Are FAERS Data Available to the Public? Where Else Can I Find Safety Information?
8. Surveillance websites:
a. FDA MedWatch http://www.fda.gov/Safety/MedWatch/default.htm
b. FDA Sentinel Initiativehttp://www.fda.gov/Safety/FDAsSentinelInitiative/default.htm
c. Mini-Sentinel http://www.minisentinel.org/
9. Staffa JA, Chang J, Green L. Cerivastatin and reports of fatal rhabdomyolysis. N Engl J Med. 2002 Feb 14; 346(7):539-40 http://www.nejm.org/doi/full/10.1056/NEJM200202143460721 - This letter expresses the concerns over the safety of all statins following Bayer’s decision to voluntarily withdraw cerivastatin from the U.S. market. It is a good example of the process of post-approval drug surveillance.
1. Sackett DL. Superiority trials, non-inferiority trials, and prisoners of the 2-sided hypothesis. Evid Based Med 2004;9: 38-39. http://acpjc.acponline.org/Content/140/2/issue/ACPJC-2004-140-2-A11.htm
2. Pivot X, Romieu G, Debled M et al. 6 months versus 12 months of adjuvant trastuzumab for patients with HER2-positive early breast cancer (PHARE): a randomized phase 3 trial. Lancet Oncol 2013;14:741-48. http://www.ncbi.nlm.nih.gov/pubmed/23764181
3. Food and Drug Administration. Guidance for Industry: Non-inferiority Trials. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatory Information/Guidances/UCM202140.pdf
4. EMEA. Guidelines on the choice of the non-inferiority margin. Doc. Ref. EMEA/CPMP/EWP/2158/99. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003636.pdf
5. D’Agostino RB. Massaro JM, Sullivan LM. Non-inferiority trials: design concepts and issues – the encounters of academic consultants in statistics. Stat Med 2003; 22: 169-86. http://www.ncbi.nlm.nih.gov/pubmed/12520555
6. Kaul S, Diamond GA. Good enough: A primer on the analysis and interpretation of noninferiority trials. Ann Intern Med 2006; 146: 62-9. http://www.ncbi.nlm.nih.gov/pubmed/16818930
7. Pivot X, Romieu G, Debled M et al. 6 months versus 12 months of adjuvant trastuzumab for patients with HER2-positive early breast cancer (PHARE): a randomized phase 3 trial. Lancet Oncol 2013; 14: 741-48. http://www.ncbi.nlm.nih.gov/pubmed/23764181