Having spent the last couple of weeks discussing composite & surrogate outcomes, I was reminded this week of the importance of thoughtful planning on the choice of outcomes in the first place 1/6 #MethodologyMonday
In particular I was reminded of the fundamental work of #Donabedian to conceptualise what is important to measure to assess quality of (and improvement in) health care. Although developed decades ago, it remains just as relevant today 2/6 jamanetwork.com/journals/jama/…
When we seek to assess the impact of a new intervention on care, the Donabedian model suggests there are 3 elements that may be impacted - the #structure the #process and the #outcome of care 3/6
#Structure refers to the organisational delivery of the care/intervention. #Process refers to the impact on how care is delivered eg length of stay etc and #outcome refers to the endpoint result of care eg recovery, QoL 4/6
It is important to consider in advance each domain (structure, process & outcome) and assess what might be influenced by your intervention and to measure accordingly 5/6
The Donabedian approach recommends that elements from all domains be included - to allow quantification of the implications on each. The scale of impact may vary depending on the domain 6/6
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Last week I discussed composite endpoints and how while they can be useful, they can also be fraught with difficulty. The same descriptors could equally be applied to #SurrogateOutcomes in clinical trials 1/9 #MethodologyMonday
A #SurrogateOutcome is a substitute measure (eg blood pressure) that one might use to stand in for the real outcome of interest (eg stroke) when the real outcomes of interest may take a very long time to measure - to allow trials to be completed more quickly & efficiently 2/9
Surrogate outcomes can take many forms and may be histological, physiological, radiological etc … biomarkers that predict events 3/9
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
Given the complexity of delivering clinical trials, they are a fertile ground to gain from #interdisciplinary thinking. For example, the field of trial #recruitment has already gained enormously from insights from other disciplinary approaches 1/7 #MethodologyMonday
A recent paper highlighted the use of #StatedPreference methods in this space. It showed aspects of trial design can affect recruitment 2/7
#StatedPreference methods eg discrete choice experiments are more commonly used by health economists to value and quantify aspects of health care but can be used to determine preference priorities in any domain 3/7
It’s good to start a new year getting the basics right. It’s the same with methods; important not to slip into using common errors.The recent Xmas BMJ paper which showed the most common stats/methods errors is a great place to start 1/7 #MethodologyMonday
The BMJ stats editors highlighted the top 12 most common stats errors they come across. They are summarised in a neat infographic 2/7
All are important,but a couple particularly resonate. One is “dichotomania” (the term coined by Stephen Senn for this) where a perfectly good continuous measure eg blood pressure or weight is then arbitrarily dichotomised into two categories - good/bad; high/low etc 3/7
Following the paper noted this week to have just added capital “T”s to a graph to depict standard errors 😱🤯, a short note on the importance of accurate data visualisation in any research report … 1/8 #MethodologyMonday
This was the tweet & thread which highlighted T-gate. There are lots of other issues with that paper, but data visualisation is a core element 2/8
The paper had attempted to use a #DynamitePlot (sometimes known as a Plunger Plot) to display the data. Even without adding T’s there are major issues with dynamite plots and frankly most statisticians would like them consigned to history! 3/8
We are all being rightly encouraged to be #efficient in our trial design & conduct. Efficiency comes primarily through design choices … whether classic or more modern efficient designs … a few reflections below 1/7 #MethodologyMonday
A #crossover design can be highly efficient. Each person acts as their own control removing large element of variation, making the design more powerful. The outcome needs to be short term however & the intervention can’t have a long-standing effect 2/7 bmj.com/content/316/71…
This is particularly the case when a cluster design is also in play. A #ClusterCrossover design can majorly reduce the sample size requirements compared with a std cluster design. A good primer on this was published by @karlahemming and colleagues 3/7 bmj.com/content/371/bm…