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Marion Campbell Profile picture
@MarionKCampbell
Jun 5 • 8 tweets • 4 min read Twitter logo Read on Twitter
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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
DELTA identified seven main methods for calculating MCIDs - anchor, distribution, health economic, opinion-seeking, pilot study, review of evidence base and standardised effect sizes. 4/8 Image
The DELTA team also published a guide on how to choose an appropriate MCID (in the DELTA-2 project) 5/8
bmj.com/content/363/bm… Image
This week, Wang et al also published a useful framework to help guide which MCID to use (this one focussing on patient reported outcomes) especially if there are a selection of different possible MCID estimates available. 6/8
bmj.com/content/381/bm… Image
The framework proposes a systematic step-by-step action plan to ensure the choice of MCID is both methodologically sound and contextually relevant. They provide a detailed flow chart to navigate the process 7/8 Image
Repeated studies have found that the choice of MCID is often not defined or justified in trial protocols or reports. As with most other aspects of trials, reporting needs to be better 8/8

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More from @MarionKCampbell

Marion Campbell Profile picture
May 29
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
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
Marion Campbell Profile picture
Apr 17
This week some of my discussions have centred on #ClusterTrials. Cluster trials involve the randomisation of intact units (wards, hospitals, GP practices etc) rather than individuals. They have a number of key elements that must be accounted for 1/11
#MethodologyMonday
There are very good reasons for cluster/group randomisation eg when evaluating interventions like clinical guidelines or educational interventions which apply at practice/hospital level; or when there is potential of contamination of the intervention across trial groups 2/11
However, cluster randomisation has some major impacts for design & analysis primarily because observations within a cluster are not independent (outcomes are likely to be more similar within a cluster) 3/11
Read 11 tweets
Marion Campbell Profile picture
Apr 3
The first step in a clinical trial is deciding the #ResearchQuestion. Knowing which question is most important to focus on may not be clear cut. An interesting paper was recently published which developed a tool to rank the importance of research questions 1/7
#MethodologyMonday
This tool was developed for the musculo-skeletal field (ANZMUSC-RQIT), but the concepts are highly likely to be transferable to other fields 2/7
journals.plos.org/plosone/articl…
The tool identified 5 domains to be ranked:1) extent of stakeholder consensus, 2) social burden of health condition, 3) patient burden of health condition, 4) anticipated effectiveness of proposed intervention, and 5) extent to which health equity is addressed by the research 3/7
Read 7 tweets
Marion Campbell Profile picture
Mar 27
There was an interesting paper this week on different stakeholders understanding of the concept of #equipoise. Equipoise is an essential concept in clinical trials but is often not well understood 1/8
#MethodologyMonday
For it to be ethical to randomise in a trial, it is important that there is uncertainty which treatment is best 2/8
Originally uncertainty (equipoise) had to be at the level of the individual clinician but was refined to uncertainty at the professional community level by Freedman in the 1980s 3/8
nejm.org/doi/full/10.10…
Read 8 tweets
Marion Campbell Profile picture
Mar 20
The #FactorialTrial design is one of the very original efficient trial designs yet its potential often remains underused 1/7
#MethodologyMonday
In a #FactorialTrial you can evaluate the effectiveness of more than one treatment simultaneously and for the same sample size requirements as doing a single trial 2/7
bmcmedresmethodol.biomedcentral.com/articles/10.11…
For a factorial trial of say 2 treatments, patients are allocated to 1 of 4 groups: Gp1 receives both treatments A and B; Gp2 receives only A; Gp3 receives only B; and Gp4 receives neither A nor B (the control) 3/7
cambridge.org/core/services/…
Read 7 tweets

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