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Marion Campbell Profile picture
@MarionKCampbell
Feb 20, 2023 • 8 tweets • 4 min read • Read on X
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Choosing the right outcome is key to a clinical trial. Sometimes a #CompositeOutcome
- an outcome that combines more than one dimension into a single measure - is felt to be most appropriate. These can be useful but can be fraught with difficulty 1/8
#MethodologyMonday
One of the primary reasons for using a composite outcome is trial efficiency - you can get more events quickly compared to the individual components thus increasing precision and efficiency in sample size calculations 2/8
jamanetwork.com/journals/jama/…
However, the validity of a composite relies on consistency of the individual components -see Montori et al 3/8

bmj.com/content/330/74…
Montori et al also stress the need for both elements to be important to patients & suggest a set of questions to help with the interpretation of composite outcomes 4/8
Composite endpoints can be problematic & very difficult to interpret if the different components actually have different levels of importance to clinicians/patients 5/8
This is exacerbated if the events in the component of greater importance is small compared to the number of events in the components of lesser importance 6/8
bmj.com/content/334/75…
To accommodate for the situation where there is a hierarchy in the composite components, the “win ratio” method was developed by Pocock as a method to deal with it. 7/8
pubmed.ncbi.nlm.nih.gov/21900289/
As such care needs to be taken before deciding whether to use a composite and, if so, which one. A neat summary of the pros & cons is outlined in this paper by Sue Ross 8/8
sciencedirect.com/science/articl…

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More from @MarionKCampbell

Marion Campbell Profile picture
May 13
A popular, but often misused design, is the #Crossover trial design. But what are the key things to look out for if you are considering using it? 1/9 #MethodologyMonday #87
In a crossover trial each participant receives two (or more) treatments in a random order. The most common design is an AB/BA design (2 treatment, 2 period design) which randomises half the sample to receive treatment A first then B and the other half to B first then A. 2/9
Because each person acts as their own control this removes a large element of variation, making the design more powerful than a standard parallel group study 3/9
Read 9 tweets
Marion Campbell Profile picture
Mar 25
In clinical trials, we sometimes undertake #SensitivityAnalysis alongside the main primary analysis. But when should we use them and to what purpose? 1/8
#MethodologyMonday #80
Sensitivity analyses can assess the impact of key elements/assumptions of a trial on the result eg impact of any baseline imbalance, impact of the choice of analysis approach etc 2/8
If the different sensitivity analyses provide similar results one is reassured that the trial result is robust and thus the credibility of the trial findings are increased 3/8
Read 8 tweets
Marion Campbell Profile picture
Mar 4
I have spoken about “usual care” or “treatment as usual” as a control arm in trials before, but should you ever protocolise usual care or just measure it as is? 1/8
#MethodologyMonday #77
Whilst “usual care” implies a common package of care being applied across sites, there is often a high degree of heterogeneity in care provided - but many would argue that the heterogeneity will increase the external validity of the trial results 2/8
However, heterogeneity in the usual care control group may affect the internal validity of the trial. It can affect the effect size and can make the trial result hard to interpret. 3/8
Read 8 tweets
Marion Campbell Profile picture
Jun 5, 2023
I have spoken about the importance of minimally clinically important differences (#MCID)
before in relation to sample sizes but how do you decide what it should be? There was an interesting paper published this week adding to this literature 1/8
#MethodologyMonday
The MCID drives the sample size - it is the minimum clinically important difference you set your trial to detect. Set the MCID too small and the sample size will be much larger than needed; but make the MCID too big & your trial will miss clinically important effects 2/8
There are different methods of calculating #MCIDs - the DELTA project is a great resource in this regard. 3/8
DELTA: journalslibrary.nihr.ac.uk/hta/hta18280/#… Image
Read 8 tweets
Marion Campbell Profile picture
May 29, 2023
While 1:1 randomisation to interventions is most common in clinical trials, sometimes #UnequalRandomisation is used. There are a number of factors that influence which randomisation ratio to use 1/9
#MethodologyMonday
One justification for unequal randomisation is when there is a substantial difference in cost of treatments. In this scenario, randomising unequally with fewer to the very expensive arm maximises efficiency when a trial has set resources 2/9
bmj.com/content/321/72… Image
Another is if you are undertaking an early investigation of a new treatment and need to have greater insights into its underlying safety/benefit profile. Here, increasing allocation to the new treatment will provide greater precision around these estimates 3/9
Read 9 tweets
Marion Campbell Profile picture
Apr 24, 2023
One phenomenon that can affect clinical trials is the #HawthorneEffect. This is when purely being involved in a trial can improve performance. 1/9
#MethodologyMonday
The #HawthorneEffect was named after a famous set of experiments at the Hawthorne Western Electric plant, Illinois in the 1920/30s. 2/9
In one experiment lighting levels were repeatedly changed & with each change, productivity increased .. even when reverting to poorer lighting. This was attributed to workers knowing their work was being observed. Productivity returned to normal after the experiments ended 3/9
Read 9 tweets

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