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

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

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’!

‘How To Do’ Research

• Start with defining the question • Write down a clear aim • Divide the problem into smaller, answerable questions

‘How To Do’ Research

• Develop hypotheses • Decide what data is needed to test the hypotheses • Refine the above and check the line of thought

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?

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?

Researcher Qualities

• Knowledgeable • Observant • Logical • Open-minded • Honest • Motivated • Independent • Flexible • Careful

Researcher Qualities

• Curious • Inquisitive • Eager to learn • Skeptical • Perceptive • Persistent • Patient • Original • Creative

Getting Started

• Learn your subject • Read, Read, Read • Start general and then focus • Begin with the problem

Getting Started

• Formulate the problem as a research question • Reduce the question to a single unambiguous question that is well defined and answerable

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

Hypothesis

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

Hypothesis

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

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

Study Types

• Will you test a hypothesis or describe a phenomenon?

• Observational – Longitudinal – Cross-sectional • Randomized, double-blind, parallel group, placebo controlled trial

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

Study Design

• Study Population – Age – Gender – Ethnicity/Race – Disease characteristics – Exclusions – Number – Stratification – Randomization

Human Subjects

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

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?

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?

Methods

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

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

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

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

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

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

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

Writing It Up

• If you don’t write it, then it didn’t happen • Order of writing: – Methods – Results – Introduction – Discussion – Abstract – Title

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

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

BREAK

Clinical Research

Drug Development

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)

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

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

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

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%

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

Reading Clinical Research

How to Approach RCT Reports

Reading Clinical Trials

• ‘

All that glitters is not gold’

Bengt and Curt Furberg by • 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

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

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

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?

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’?

Are the outcomes relevant?

• Quality of life • Mortality • Health economics • Surrogate markers of clinical outcome • Surrogate biologic markers

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

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

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

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?

Excluded Subjects?

• Intent to treat analyses should be reported • Two unacceptable reasons to exclude subjects are: – After randomization where they do not meet entry criteria – Because they did not take the medication

Do you need a statistician to read the study?

• In clinical trials, design should allow relatively straightforward presentation of results • Effect size and relevance are more important than P values

Do you need a statistician to read the study?

• Consider the number of patients who would have to be treated to avoid the outcome being prevented • Subgroup analyses should be avoided unless defined a priori

Economic Analysis

• “Of course our drug is more expensive, but we need to convince clinicians to use it more” • Does the medication reduce direct or indirect costs or both?

Economic Analysis

• Be sensitive to relationship between the authors and the sponsor • Be careful if soft assumptions are used • Beware of analyses based on the clinical trial setting and not the real world • Beware indirect evidence with surrogate markers