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Why Do Clinical Research?
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Why Do Clinical Research?

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  1. Why Do Clinical Research? • Satisfaction of answering important questions which will improve the health of our patients • Status of researchers • Skill advancement • Professional advancement • Salary and Job Security

  2. What is Research? • Research is the endeavor to discover new facts, procedures, methods, and techniques by the scientific study of a course of critical investigation

  3. Clinical Research • Clinical research involves working with human subjects to answer questions relevant to their well-being • Patient oriented research is where the ‘rubber meets the road’!

  4. ‘How To Do’ Research • Start with defining the question • Write down a clear aim • Divide the problem into smaller, answerable questions

  5. ‘How To Do’ Research • Develop hypotheses • Decide what data is needed to test the hypotheses • Refine the above and check the line of thought

  6. Good Research • CLEAR • Essential for both the problem and the answer • ACCURATE • Exactness and precision come from hard work and responsible effort • RELIABLE • If repeated will the answer be the same?

  7. Good Research • OBJECTIVE • The researcher exposes all possible prejudices at the onset of the study design and strives to overcome them • Will the research be untarnished by personal gain, biases, vested interests, etc?

  8. Knowledgeable Observant Logical Open-minded Honest Motivated Independent Flexible Careful Researcher Qualities

  9. Curious Inquisitive Eager to learn Skeptical Perceptive Persistent Patient Original Creative Researcher Qualities

  10. Getting Started • Learn your subject • Read, Read, Read • Start general and then focus • Begin with the problem

  11. Getting Started • Formulate the problem as a research question • Reduce the question to a single unambiguous question that is well-defined and answerable

  12. Stages in Creativity • SENSE • Realize the need for a study • PREPARE • Gather relevant information • INCUBATE • Think through the problem • ILLUMINATE • Imagine possible solutions • VERIFY • Evaluate the solutions you have generated

  13. Hypothesis • Thesis is the position that you believe represents truth • Hypothesis is the foundation on top of which you build your thesis

  14. Hypothesis • Hypothesis is a tentative construct to be proved or disproved according to the evidence • The hypothesis is sometimes expressed as a null hypothesis

  15. A Good Hypothesis Should: • Be testable • Convey the nature of the relationship being tested • State exactly what variables form this relationship • Reflect all variables of interest • Be formulated early on in the planning stage

  16. Study Types • Will you test a hypothesis or describe a phenomenon? • Observational • Longitudinal • Cross-sectional • Randomized, double-blind, parallel group, placebo controlled trial

  17. Epidemiology vs RCT • Epidemiology allows the study of the real world and the development of hypothesis regarding disease states • Randomized, controlled trials allow the rigorous testing of hypothesis in a well characterized manner that is less real world in nature

  18. Study Design • Study Population • Age • Gender • Ethnicity/Race • Disease characteristics • Exclusions • Number • Stratification • Randomization

  19. Human Subjects • The safety and rights of human subjects must be protected • Study Design • Institutional Review Board • Informed consent • Data Safety Monitoring/Medical Monitors

  20. Key Questions • What is the main purpose of the trial? • What treatments will be used and how? • What is the participant risk? • What are the possible benefits? • How will patient safety be monitored?

  21. Key Questions • Are there alternative treatments? • Who is sponsoring the trial? • What is the participant burden? • How long and where? • What do the participants have to do? • Will there be any discomfort even if there is no risk?

  22. Methods • Define methods carefully • Decrease variability • Check reliability/reproducibility • Are you testing what you think you are testing?

  23. Methods • Try to ‘walk through’ the study and consider as many likely scenarios as possible. • Try to design in any variations in treatment or data collection that you think will occur before the study starts

  24. Operationalize Concepts • Specify how you will repeatably and reliably measure the variables you are using to answer the question • An operational definition specifies how your concepts will be observed and measured • This should allow your research to be reproduced

  25. Data • Data are the facts you measure • They should be carefully recorded in an unbiased manner • They should be measured in a manner that minimizes random variation • They should be derived from the operational definitions you have developed

  26. Data Validation • Do the data make sense? • Look critically at the data • Highest and lowest values • Data entry errors • Distribution: Normal or skewed • Check selected data entries with original data forms

  27. Data Interpretation • Do not interpret/analyze data until after study is completed • Do not ‘unblind’ subjects until the study is completed other than for safety reasons • Do not interpret/analyze data until after data has been validated and the data set closed

  28. Data Interpretation • Use the research question and hypotheses to guide analyses • Use a priori definitions for any sub-set analyses • Exploration of epidemiologic data sets is OK, but need to avoid data mining

  29. Writing It Up • If you don’t write it, then it didn’t happen • Order of writing: • Methods • Results • Introduction • Discussion • Abstract • Title

  30. Writing It Up • After the first draft, new analyses will usually be suggested by the process of putting your ideas down on paper • Put the paper away for a few weeks and then read it again • Ask mentors and colleagues to read the paper at the first draft stage

  31. Sending It In • When writing the paper, have the journal you will submit to in mind • Pick journals that will match your paper’s topic and the quality and importance of your work • Aim high and, if needed, go low • Persist, Persist, Persist

  32. BREAK

  33. Clinical Research Drug Development

  34. Drug Development • Preclinical/Laboratory Study • Cell culture in animal and human cells • Animal studies • Looking both at toxicity/carcinogenicity as well as effect, if relevant • Develop Investigational New Drug application with FDA (IND)

  35. Phase I Studies • Assess drug safety and tolerability • Healthy volunteers, then those with target disease • Pharmacokinetics • Absorption • Metabolism • Excretion • Dose escalation • 70% of new drugs pass this phase

  36. Phase II Studies • Assess drug efficacy • Usually randomized, controlled trials with smaller numbers up to several hundred subjects • Test different therapeutic strategies • Use surrogate variables and are usually short term • Only 1/3 get past phase II

  37. Phase III Studies • Large scale RCT to assess efficacy and safety of medication • Several hundred to thousands of patients enrolled • Classic randomized, placebo-controlled design • Long-term study design with real world outcome variables • Define package insert content and allow marketing

  38. Study Size and Adverse Events • The size of the treatment group determines the likely frequency of adverse events (side effects) that can be detected • A good rule of thumb is that you can detect an adverse event rate that is one event in the number of subjects divided by three: • A study with 100 patients will only detect AE’s that occur at a rate of 1/33 = 3%

  39. Phase IV Studies • Compare drugs with other drugs on the market • Define broader target population • Monitor long-term efficacy and safety • Conduct health economics assessment and quality of life study

  40. Reading Clinical Research How to Approach RCT Reports

  41. Reading Clinical Trials • ‘All that glitters is not gold’ by Bengt and Curt Furberg • Just because a study is published in a journal does not mean that it represents truth • ‘Throwaways’ and Drug company sponsored newsletters have either no or limited peer review

  42. Was the question stated A Priori? • Exploring data is acceptable to define hypotheses, but cannot definitively answer them • Primary outcomes and limited secondary outcomes should be carefully defined before study commences

  43. Was the question stated A Priori? • Multiple hypothesis testing can lead to false association • P <0.05 is subverted if there are 20 looks at the data

  44. Is the question relevant? • Does the answer clarify whether the treatment will help patients to: • Feel better • Live longer • Have less complications of illness • Are the endpoints real world or merely surrogates • How can one generalize the findings?

  45. How is improvement quantified? • Are the outcomes relevant? • Do the measures used make sense? • Is the magnitude of the difference relevant to patient care? • Is the study ‘over-powered’?

  46. Are the outcomes relevant? • Quality of life • Mortality • Health economics • Surrogate markers of clinical outcome • Surrogate biologic markers

  47. How are adverse events measured? • Side effects are characterized as: • Severe: Treatment must be stopped, or patient hospitalized, or dies, or develops cancer, or has congenital anomaly in child • Moderate: Dosage must be reduced, usually leads to discomfort, temporary disability, or reduction in functioning • Mild: No change in treatment. Limited discomfort or dysfunction

  48. How are adverse events measured? • AE’s are characterized as to whether or not they are related to the medication: • Definitely • Likely • Probably • Possibly • Not associated

  49. Are the patients representative? • This is most problematic in pediatrics where we often have to extrapolate from adult studies • Gender, age, and race can all alter outcomes • Disease classification and severity can alter outcomes • High risk patients are usually excluded

  50. Where the groups initially comparable? • Even in studies of 150-200 subjects substantive imbalance can occur between treatment groups • Was stratification used to ensure balance? • Did the treatment group start out sicker so that they likely would improve more than the placebo group?