Stages in the Design of a Clinical Trial

From the inclusion of this topic in the primary curriculum, one might think that the college expects their fellows to be competent in running and designing a clinical trial. Pity that the majority submerge into the rich thick ooze of private practice. Either way, this topic has come up in the primary exams, so it's worth knowing a bit about it. A corresponding chapter (Steps in designing and conducting a clinical trial) exists in the Required Reading section for the Fellowship Exam, but is little more than a "stub" by the Wikipedia definition, there only as an aide memoir to the fatigued Fellowship candidate. Here however, the stages of trial design are dissected in some detail.

How much detail is required? Hard to say. One might be able to draw one's own conclusions from Question 12 from the second paper of 2016 ("Discuss the stages in designing a clinical trial"). The college answer was literally four lines, of which two were wasted on complaining about poorly prepared candidates. According to those comments from the examiners, "An outline of the background literature review, defining the hypothesis, study design, ethics, funding, consent, conduct and follow-up was expected." With nothing better than this for guidance, the following chapter attempts to answer part A.a from the 2014 Primary Exam curriculum, "Describe the stages in the design of a clinical trial."

First, in briefest summary, as seen in LITFL:

Stages of clinical trial design

  1. Research the question
  2. Define the hypothesis 
  3. Review the literature 
  4. Involve a statistician 
  5. Develop inclusion and exclusion criteria
  6. Calculate the sample size 
  7. Develop methodology (trial protocol)
    Steps taken to minimise bias may include: 

  8. Gain ethics approval
  9. Perform a pilot study
  10. Modify protocol as needed
  11. Perform the study (collect data)
  12. Measure the outcomes
  13. Analyse the data
  14. Compare to null hypothesis (is the difference statistically significant?)
  15. Consider sources of bias and errors
  16. Submit for publication

Now, in luxurious detail. 

The majority of the material below has been drained out of Statistical methods for anaesthesia and intensive care (P S Myles, T Gin - 1st ed - Oxford : Butterworth-Heinemann, 2001). This is the recommended text in the 2014 revision of the primary curriculum, and it has a nice 10-page section (pp.135-145) titled "How to design a clinical trial". Beyond that, the discussion was guided by what the college examiners had complained about in their comments. 

Stages in the design of a clinical trial

Definition of the hypothesis

  • What does the trial aim to achieve? What question does it attempt to answer?
  • What is the significance of this question? Is the question useful? Time money and effort will be expended, not only by the primary investigator but by potentially an entire team of people. Perhaps the question is not worth it? Does one really wish to spend hours and days to arrive at the answer? Cui bono?
  • What are the endpoints? One should identify some sort of primary endpoint. If one is cursed with excess interest, one might want to read about how mortality and functional outcomes perform when used as endpoints for ICU research.
  • The primary endpoint should be clearly defined. An ill-defined endpoint invalidates the rest of the study. The primary endpoint should also be valuable enough to pursue. It should be readily measured, and ideally it should be a direct measure of outcome (as it is inferior to rely on surrogate endpoints).
  • The hypothesis needs to be succinctly formulated. Without a clearly defined hypothesis,  the study is no trial at all - merely a series of recorded observations. A clearly defined hypothesis also assists in trial design (one asks themselves perpetually, "does my trial answer the hypothesis?")

Literature review

  • What current published evidence exists? One might find that the question has already been answered to a satisfactory degree
  • How were those studies conducted? Deficiencies in their methodology may guide the design of your new study
  • What other questions remain unanswered? This may guide you towards considering some different secondary outcome measures, as well as purely exploratory measures (eg. using a novel yet-to-be-validated biomarker)

Study design

Develop a study protocol. The aim is to minimise bias and to maximise precision. That development has numerous facets to it:

  • Define the study population; determine which inclusion and exclusion criteria you are going to use
  • Calculate the sample size: this will be largely determined by the expected effect size of the treatment on the primary outcome measure.
  • Define treatment groups. These should ideally be equal in almost every way.
  • Decide on a method of randomisation, if you are going to randomise the patients. Or, come up with a really good reason as to why you cannot randomise.
  • Determine the method of treatment allocation and how youre going to conceal allocation
  • Define the timing of intervention; i.e. a protocol for when the randomised patients end up receiving the target intervention. The protocol should be sufficiently explicit and "fool-proof" so that protocol violations are kept to a minimum.
  • Clearly define the data collection protocol and the instruments which will be used for this
  • Arrnage a convenient method of reporting adverse events so that no harm is done

Perform a pilot study. This, in the Myles and Gin book, is described as "an important and often neglected process". This tests the feasibility of the full-scale trial, assessing the assumptions made in the course of making the trial protocol. Results may require that the final trial protocol be modified, or that the required sample size be recalculated.


Ogden and Goldberg (2002) are an excellent resource for this topic, and offer excellent answers to the question "why was my research not funded?". In summary:

  • Major government bodies only fund the top 20% of projects
  • Other sources of cash are available:
    • Institutions (eg. hospitals and universities)
    • Colleges (eg. CICM)
    • Associations (eg. ANZICS)
    • Benevolent bodies
    • Corporate sponsors
  • A lot of the money is spent on insurance, of which there need to be a fair amount (to the tune of $20 million) so that if your reserach kills or cripples somebody, they and their families can be compensated out of the fund.
  • According to Ogden and Goldberg , the ten most common reasons for failure to secure funding with the NIH were:
    • Lack of original ideas
    • Diffuse, unfocused, or superficial research plan
    • Lack of knowledge of published relevant work
    • Lack of experience in essential methodology
    • Uncertainty concerning future directions
    • Questionable reasoning in experimental approach
    • Absence of acceptable scientific rationale
    • Unrealistic large amount of work
    • Lack of sufficient experimental detail
    • Uncritical approach

Conduct of the study

Basic rules as to how to do it properly can be found in the The Australian Clinical Trial Handbook from the TGA. In short, as thew trial runs there needs to be:

  • Registration in an online database, eg. the Australian Clinical Trial Registry
  • Collection of data which is preserved (7 years)
  • Collection of consent documents which are preserved ( 7 years)
  • Notification to sponsor and HREC of serious adverse events
  • Regular reports of study progress.
  • Regular review by an independent committee, for large trials
  • No deviation from protocol without HREC endorsement, unless serious harm is being avoided


The final outcome of a trial is some sort of paper. That paper should be formatted according  toThe Consolidated Standards of Reporting Trials (CONSORT) statement. In their media release there is a table (Table 1) expanding upon over thirty item numbers which must be satisfied for successful compliance. This table is not reproduced here even by this details-hungry author. The primary exam candidate is left to decide by themselves as to how much of their time this is worth.

Follow-up of the study

Ethics approval and issues regarding consent

Ethic in human research is governed by both Hippocratic principles (i.e. don't kill any patients in the name of science) as well as by utilitarian principles (to benefit many, a few may be exposed to some risk). But how do we decide how much risk is ok, and how many must benefit in order to tolerate it? If some of the experimental subjects might die as a result, how many people need to be saved in order to make this an acceptable loss? Who is the ultimate arbiter of right and wrong?

As in most things in life, the general principles guiding medical research can be summarised in the recommendation "don't do what the Nazis would have done". This suggestion was put into recognisable modern form with the Nuremberg Code, which was drafted at the end of the the Doctor's trial in Nuremberg (1947). This itself was based (very closely, to the point of plagiarism) on the German Guidelines for Human Experimentation of 1931- to the extent that Ghooi (2001)wondered how the authors were able to successfully pass it off as original work. The code consisted of six (later, ten) statements which define legitimate medical research, as contrasted with the grotesque experiments performed by the 22 Nazi doctors standing trial. These were:

  1. Required is the voluntary, well-informed, understanding consent of the human subject in a full legal capacity.
  2. The experiment should aim at positive results for society that cannot be procured in some other way.
  3. It should be based on previous knowledge (like, an expectation derived from animal experiments) that justifies the experiment.
  4. The experiment should be set up in a way that avoids unnecessary physical and mental suffering and injuries.
  5. It should not be conducted when there is any reason to believe that it implies a risk of death or disabling injury.
  6. The risks of the experiment should be in proportion to (that is, not exceed) the expected humanitarian benefits.
  7. Preparations and facilities must be provided that adequately protect the subjects against the experiment’s risks.
  8. The staff who conduct or take part in the experiment must be fully trained and scientifically qualified.
  9. The human subjects must be free to immediately quit the experiment at any point when they feel physically or mentally unable to go on.
  10. Likewise, the medical staff must stop the experiment at any point when they observe that continuation would be dangerous.

This is not quoted very often, as it was largely superceded by the Declaration of Helsinki in 1967.  That thing is now in its seventh revision (2013), and the original ten commandments from Nuremberg have bloated and mutated in line with medical advances into 37 points, covering topics which range from the ethical use of placebo therapies to issues of sponsorship and privacy.

Now, that all sounds fine and good, but neither document was ever made into law. In Australia, the Declaration of Helsinki has informed and guided the NHMRC standards as laid out in the National Statement on Ethical Conduct in Human Research (2007, updated 2015). These guidelines direct the approval of the research by local Human Research Ethics Committees (HRECs)

Ethics of randomisation and the principle of equipoise

To some extent, randomisation in a trial violates the principle of informed consent (directly, because by definition nobody is "informed" as to what treatment the patient is getting). This issue is dissected in detail by Ben Freedman (NEJM, 1987). The principle of equipoise is what underlies the ongoing practice of randomising patients into trials. The principle dictates that it is appropriate to randomise to alternative treatments in a situation where the clinician and the patient have no particular preference or reason to favour one treatment over another. In other words, because neither treatment is known to carry more risk than the other, it is reasonable to offer either treatment with the confidence that no harm will be done. "The requirement is satisfied if there is genuine uncertainty within the expert medical community — not necessarily on the part of the individual investigator"

This is of course absolute bullshit, because the clinician definitely has some inclination that one treatment is superior to the other (in fact that's the hypothesis of the study). An as the trial progresses and data is collected, whatever equipoise was present will be disturbed if the trial data overwhelmingly favours one treatment over another. How to deal with this? Freedman (1987) offers several viewpoints from contemporary authors, which are essentially philosophical backdoors into unethical practice and "frank counsels of desperation" by his description, relying on such bizarre suggestions as the proposition that many people are altruistic enough to forgo some personal gain (or even survival) in the interest of progress.

Thus, the concept of clinical equipoise is needed. Freedman's widely quoted paper suggests the following:

"We may state the formal conditions under which such a trial would be ethical as follows: at the start of the trial, there must be a state of clinical equipoise regarding the merits of the regimens to be tested, and the trial must be designed in such a way as to make it reasonable to expect that, if it is successfully concluded, clinical equipoise will be disturbed."

That means, the trial must be expected to resolve a dispute among doctors. The investigators may have their equipoise disturbed as much as they like, provided they recognise that their less-favored treatment is preferred by colleagues who are also responsible and competent people. As trial results roll in and interim analysis data becomes available, an independent body (eg. an independent data and safety monitoring committee established to monitor the trial) can independently arrive at the conclusion that the available evidence favours one treatment so strongly that equipoise is disturbed in the entire clinician community. In this case, to protect the public the trial can be brought to an end (see the chapter on trials which end prematurely).



Statistical methods for anaesthesia and intensive care (P S Myles, T Gin - 1st ed - Oxford : Butterworth-Heinemann, 2001)

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