2 - 1 - Lecture 2A- Rationale for Randomization (16-10)

7/24/2019 2 - 1 - Lecture 2A- Rationale for Randomization (16-10)

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[MUSIC]Hello, this is Janet Holbrook again.And today we're going to be covering theconceptof randomization, which is one of the realcore featuresof randomized clinical trials and probably

the most familiar characteristicto most people when we talk aboutrandomized clinical trials.We have three sections, and for the firstsection,I will be going over the rationale forrandomization.As I just alluded to, randomization is akey featureof the experimental design that a clinicaltrial is based on.So there are other features that are

indeedas important as randomization and theyinclude thingslike standardized treatment and having aprospective planfor data collection, adverse eventreporting, as wellas the regulatory requirements that goalong with clinical trials.And these features tend to distinguishclinical trials from other typesof observational studies and even from nonrandomized studies in some cases.

So, it's important to recognize that theconcept of randomization, or the drawingof lots, has been with us throughouthistory and it even is referredto in The Bible.It, it is the idea of drawing a lot as away to ensure that the benefits and riskof an activity or event areequally shared or fairly shared across agroup of people is a very early concept.One that's well illustrated with theconcept of a military draft.

Where the risk of war is shared across thepopulationby drafting men mostly, into militaryservice by the mechanism of a lottery.So this concept, which has been used inmany areas,including how to divide some scarceresource up, or the conceptof drawing the short straw.We first see the idea ofrandomization to be used in a medicalsetting in the17th century.

Van Helmont proposed that people berandomly dividedor divided by lots to determine their


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treatment because he wanted to evaluatehis form of care against more standardforms of careof the day, which includedbloodletting and other potentially harmfulinterventions.And so he proposed that a number

of patients be divided, and that at theend the half that are treated by him,accordingto his procedures, be compared to theother halfof the patients that were treated withother procedures.And then there'd be, there'll be a countof howmany people had died in order to evaluatebloodletting in healthcare.So just by way of background, many times

we talk about or use the word random in,in our usual speech, or in ordinary speechandsuch as, it's a common saying, random actsofkindness, or random acts of nature, randomacts of God.You fill in who the random acts are of.And what we mean by that is somethingthat's notpredictable and seems to be haphazard andthere's no causal relationshipthat can be identified between things.

I sometimes think my neighbors must thinkthat I randomly mow my lawn.It's very haphazard, my pattern.But in the context of a clinical trial,we have a much more rigorous definition ofrandom.And what it means is that the process, arandom process isone in which there is associated withevery legitimate outcome a probability.So there's a legitimate probabilityassociated with whetheryou'll be assigned to treatment A ortreatment B.Such as with every coin toss, we assume ifit'sa fair coin there's a 50/50 chance ofheads or tails.With dice throws there's a one in sixchanceof any particular side of the die comingup.And this is a much more rigorousdefinition thanthe lay definition.

The idea of randomization was firstintroduced formally into experimentaldesign by Ronald Fisher.


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Many of you are probably familiar with hisfamous book, The Design of Experiments.And the question that Fisher was trying toaddresswas to how to allocate plots of soil inagriculturalsettings to different treatments.

There was no question about whether thereneeded to be controls on the comparison ofthe different treatments on plots.It was just a matter of, of how the plotswere to be allocated because you canimagine that even within a field there'sdifferences in sunlight and drainage.And they wanted to have a method of fairlydistributing those effects among thetreatments.And so what he proposed was to use randomallocation, in the allocation of lots.

Because he said by doing this it was themost efficient and unbiased wayto estimate the measurement error, errorassociated with a particular treatment.And he proposed that any other way ofdoing this would either over estimate orunderestimate the kind of random errorassociated withhow the yield was in a particular plot.He also noted that there was somesafeguard against non-normality inthe outcome data, which was an added

benefit of using randomization.So note that his argumentsabout the use of randomization are allkind of based onits statistical properties for estimatingmeasurement error, andthat it's the most efficient way to do anexperiment.And, there's not a lot of emphasis in hiswork about the use of controls.The person who really is responsible forintroducing randomization to medicalscience, is Sir Austin Bradford Hill.He was from an epidemiologicalbackground and his concern was aboutconfoundingthat was associated with the type ofpeople that got a particular treatment.Because he noted that people actually werequite variablein how they may react or respond to atreatment.And there can be in some ways predicted,and some of it'sunpredictable, was a problem in estimating

treatment effects.And because of the idea of a selectionbias associated


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with treatment assignment, that theprognosis for the disease would besomehow be related to what treatment apatient was given.Commonly referred to as confounding byindication.So that people are given treatments based

on their own unique characteristics.And what Bradford Hill was suggesting, isto get rid of that problem ofthe selection bias, in which people getwhich treatments, that random allocationbe used.So thecritics of a clinical trial would beunable tosay that the groups were not equal at thestart,that they were somehow biased based on the

physicianand caretaker's opinion about whattreatment the patient should get.Perhaps, you can think of it in somesenses that they might give thesickest patients the new treatment becausetheyhadn't responded to older treatments andwerein more dire need of a new option.And someone who was promoting a treatmenthad a interest in the treatmentmight do exactly the opposite and want the

new treatment to look good.And so only give it to the bestcandidates.So Bradford Hill was very concerned aboutthese type of biases andhe proposed that randomization beintroducedto clinical trials to eliminate this.And indeed, he did design and was involvedin the first properlyrandomized trial that is documented wellin the literature.And led the way for randomization to beused in other clinical trials.And this was a trial of streptomycin fortuberculosis in 1946.Interestingly, the use of randomization inthis trial was based onthat it was a fair distribution of riskandbenefits to patients in terms of gettingthe new treatment.But it was also because this was in the UKright after World War II.There were very limited supplies of

streptomycin.Mounting a randomized clinical trial wasalso argued to


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be the only way to fairly allocate thelimited resourcesto, to patients.Which patients with TB were going to getthis newmedicine that had quite a bit of evidencefor its efficacy.

So as I've talked about, the rationale forrandomization isto avoid selection bias and to avoidconfounding by indication.That is, that there's some prognosticfactors that would be relatedto treatment assignment, and thereby ifthey aren'tsomehow controlled, would bias ourassessment of treatment.And it's important to realize that theseprognostic

factors, such as maybe age or severity ofdisease or even clinical centers, caninfluence outcomesas strongly or more strongly as manytreatments.Remember that clinical trials are reallydesigned to find small to moderatetreatment effects.If there's very strong treatment effects,many times we don't need a clinical trial.So these prognostic factors can be quiteinfluential, and sowe use randomization to ensure their

balance across the treatment groups.And that brings me to the next point, thatrandomizationtends to produce comparable treatmentgroups on know or unknown confounders.But it's importantto note, it's not a guarantee.It's still a probabilistic process, andthereare times when you don't get perfectbalance.That you may end up with what appearsto be an important imbalance in treatmentgroups.For example, you could end up with manymore women in one group than the other.And going back to R A Fisher, therandomization also assuresthe validity of the statistical test thatwe use generally to analyze trials.They're based on the idea that thetreatment assignment, the primary variablewe're looking for and effect in wasrandomly assigned across the population.And perhaps a less important point but is

also anice feature of randomization is that itgives us a


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defined time-point for trial entry.We know that we are going to measureevents andattribute them to the appropriatetreatment group from randomization onward.And so it's a very nice time zero that hassome

meaning in terms of how the patient wastreated as well.I also want to briefly address the issueof usingunequal allocation ratios forrandomization.Pretty much up to now I've been talking orreferringto randomization as sort of two groups oneto one allocation.And that is certainly the most commondesign for parallel treatment trials.

And one of the reasons the one toone allocation is so commonly used, it'sthe mostpowerful design.Other allocation ratios such as two to oneor four to one are typically lesspowerful.And I gave you an example hereof the power associated with differentallocation ratios.But there may be reasons that you willcompromise on power or alternativelyaccept maybe a larger sample size, because

you want to use a unequalallocation ratio.It may be important to acquire as muchexperience as possible with the newtreatment including the side effects andtoxicity of that treatment.So you may want to have as many peopleas possible be exposed to the, the newtreatment.That also may be an incentive forrecruitment.If you're in the situation where patientsdon't havemany options for how they'll be treated,it may bedesirable to weight the randomizationtowards the, the new treatmentas a tool for recruiting patients, so thatthey knowthat they have a greater chance of gettingthe new treatment.There may also be cost issues depending onhow much the treatments cost.Very expensive treatments you, you mayhave a different allocation

ratio in order to conserve resources.Also, you can imagine that in some casesthat there may be


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difference in the expected variance of theoutcome variable by treatment group.So you may expect that one treatment mayhave a more homogeneous effect thananother treatment.And therefore you could argue for anunequal

allocation ratio to optimize the power ifthere's adifference in the variance across thegroups that's expected.I would say that that's probably anunusual reason for having an unequalallocation ratio.And finally, I, I want to speak to thedefinitionof selection bias.Many of you who have taken other courseswith the epidemiology department have

heard people talk about selection bias asa problem in the study group, the entiregroup selected to be studied.That somehow the association between theinterventionor the risk factor and the outcome isdifferent in, in the group of people thatis studied than in the target population.So the results from the studyreally aren't generalizable to the targetpopulation.So that's sort of an epidemiologicaldefinition of selection bias.

However, in the trial definition ofselection bias, we're really talking abouteliminating the bias associated with theprognosis of the disease.So again to eliminate theconfounding by indication.And we want to, to break that linkbetween what the intervention actually isand the patient's characteristics.So it's more an issue of internal validitywhen we'retalking about, in selection bias, in thecontext of clinical trials.So that wraps up this first section withtrying to give you some of theunderpinnings andrationale for randomization.In the next section, we're going tobe talking about different types ofrandomization schemes.

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2 - 1 - Lecture 2A- Rationale for Randomization (16-10)