Patient and public Involvement in Numerical aspects of Trials (PoINT)

 What does it mean? When patients enter a trial, we collect their information. We usually collect their information at baseline (who is taking part in the trial?), and during follow-up (with questionnaires and/or clinical visits). This information is gathered in a database and analysed to answer the ultimate trial question - does the treatment work when compared with a control (usually another treatment)?  Trial analysis is complex and involves statistical models that estimate the treatment effect, ie how well the treatment works when compared with a control.  The statistical models we use to analyse trial data may be "adjusted for" patient's characteristics. This usually happens if we believe those characteristics are important to understand the treatment effect. Statistical models make assumptions about the information collected and it is important to know those assumptions in order to interpret the findings of the trial correctly. An example In the toothpaste trial, we

What does it mean? Trial results are presented as numbers (for example, how well a treatment works) in long and detailed reports. An example When our toothpaste trial is over, we will have to discuss what the findings mean, how can they be applied in a wider context. We will want to show results to the trial team, and to the dental community, academics and patients. To do that, trial teams usually prepare presentations and reports. Whenever numerical information is presented, we will have to make calls on how to report it.  How could patients be involved? Patients could contribute to a better understanding of what the findings mean, as well as help co-design dissemination materials that include quantitative trial findings reported in a clear way.

What does it mean? Subgroup analyses try to find out whether the effect of a treatment is different for different types of people (e.g. men and women or different age groups).  Subgroup analyses are usually defined at the start of a trial, but may be discussed later on. Any subgroup analyses should be well justified as it can lead to wrong results or interpretation if not planned carefully. You can read more about subgroup analyses and how they can be tricky to interpret here .  An example In our toothpaste trial, we might be interested in finding out whether the effect of toothpaste A when compared with toothpaste B varies depending on the patient's age. For that reason, we will collect participant's age at the start of the trial and present the treatment results by age group. How can patients be involved? Patients could be involved in the discussions about what characteristics might be important to measure to explore differences in how well a treatment work.  

What does it mean? When a participant outcome is unavailable, due to a missing questionnaire or non-attendance to a trial related clinical appointment, their results are unknown. We call this missing data. In the toothpaste trial, this would mean we don’t know a participant's pain score at the end. We have a number of options available to try to account for the missing values. For example, we can use statistical tools that aim to predict what happened to patients that did not provide results, taking into account these predictions are uncertain, ie we do not know for sure what happened to those participants. You can find out more about what missing data is and its impact on interpretation of treatment claims here . How could patients be involved? Patients can help us predict missing values so we can assess treatment effect with more certainty. Example  Some participants in the toothpaste trial will have their pain score missing. For those participants and to ensure we investigate di

 What does this mean? In trial analysis we are interested in understanding how well a treatment works. However, as the trial journey evolves and becomes more complex, this question can also be more complicated than what it looks. For example, we might be interested in understanding whether the treatment works for all patients regardless of whether they take it as instructed, or whether the treatment works in patients that tolerate the treatment. An example When Emma developed mouth ulcers after three months of using toothpaste A, she decided to stop using the toothpaste. In this case, Emma was no longer adhering to the treatment after three months because she was not using it at all. A decision needs to be made about whether Emma is included in the final analysis of the trial or not. This will depend on the question - are we interested in knowing how well toothpaste A works compared with toothpaste B irrespective of whether patients took the treatment? Or are we interested in knowing h

What does it mean? Most treatments come with a risk attached - they may lead to harms and side effects, even when they benefit patients. It is crucial to measure harms and side effects and show this information by treatment. This ensures patient's safety is always considered when running a trial and interpreting findings. An example As Emma starts using toothpaste A, she notices she has developed a strange side effect - she develops mouth ulcers and is concerned. She contacts her dentist who immediately reports this side effect to the trial team. The trial team keep note of this side effect, along with others that are reported from other patients. During the trial, and at the end, the trial team analyses and presents this information. How can patients be involved? Patients can help researchers when summarising and presenting data on safety, to ensure it is clear and can be discussed by the whole trial team. Patients can be involved in interpreting the safety analysis findings.