| Literature DB >> 34321066 |
Beatriz Goulao1, Hanne Bruhn2, Marion Campbell2, Craig Ramsay2, Katie Gillies2.
Abstract
BACKGROUND AND AIMS: Patient and public involvement is increasingly common in trials, but its quality remains variable in a lot of settings. Many key decisions in trials involve numbers, but patients are rarely involved in those discussions. We aimed to understand patient and public partners' experiences and opinions regarding their involvement in numerical aspects of research and discuss and identify priorities, according to multiple stakeholders, around the most important numerical aspects in trials to involve patients and the public in.Entities:
Mesh:
Year: 2021 PMID: 34321066 PMCID: PMC8316879 DOI: 10.1186/s13063-021-05451-x
Source DB: PubMed Journal: Trials ISSN: 1745-6215 Impact factor: 2.279
Focus group participant characteristics (stage 1)
| Characteristics | Frequency (total = 9 participants) |
|---|---|
| Gender | |
| Female | 4 |
| Male | 3 |
| Missing or prefer not to say | 2 |
| Age category | |
| 18–35 | 1 |
| 36–55 | 0 |
| > 55 | 7 |
| Missing | 1 |
| Ethnicity | |
| White | 7 |
| Missing | 2 |
| Previous experience of being a patient and public partner in research | |
| Yes | 9 |
| No | 0 |
Fig. 1Visual representation of the thematic analysis results (stage 1)
Focus group analysis themes (Stage 1)
| Theme | Sub-theme | Meaning | Illustrating citation |
|---|---|---|---|
| Determinants of PPI in numerical aspects of research | Relationship with researcher and research environment | Quality of interaction with research, including empathy and trust that facilitates and motivates patient and public partners | “ |
| Patient and public partners confidence | Being able to question researchers and their assumptions | “ | |
| Communication of numerical aspects | The use of jargon and the inaccessibility of definitions and resources to help patient and public partners participate in number related discussions | ||
| General perceptions of statistics and numbers | Observations about general public’s perceptions of statistics and numbers as a potential determinant of interest and understanding of research’s numerical aspects | ||
| Identity and role | Patient and public partner’s role | What patient and public partners believe their role should be in relation to numerical aspects; their interest in helping define the context and assumptions behind deriving a number, as well as its interpretation | “ |
| Patient and public partner’s characteristics | How motivation and personal experience can lead to more interest in being involved in numerical aspects of research; reflection on what that means for involvement in these aspects | “ | |
| Impact | Transparency | Ability to scrutinise researcher’s decisions on numerical aspects leads to more transparency in the whole process | “ |
| Feeling useful | Belief that numerical aspects are crucial in the research and policy making process and, therefore, being involved in them leads to a better understanding of the pathway and higher quality involvement | ||
| Improved research | As outsiders bringing questions in, patient and public partners can help improve the quality of the research done and disseminated | ||
| Time consuming | Involvement is time consuming and this leads to exclusion of certain groups of people which is problematic; involvement in numerical aspects of research may be challenging due to taking time both for researchers and patient and public partners | “ |
Numerical aspects selected to be discussed at the priority setting meeting (stage 2)
| Aspects | Meaning |
|---|---|
| Target differences (clinically meaningful difference, non-inferiority margins) | This is the difference that will make researchers and clinicians conclude a treatment is better or good enough compared with a control |
| Risk/benefit trade-off | In a clinical trial, we usually test to find out whether a treatment gives more benefit than another. However, there could be risks or burdens to the patient there are different depending on the treatment. |
| Expected contamination | People in the control group unintentionally take the treatment or people in the treatment group unintentionally do not. |
| Clinical equipoise | A state of uncertainty in terms of what treatment option is best. |
| Randomisation allocation ratio | Ratio in which patients are allocated to receive a treatment compared with a control. It is usually done on a 1:1 basis which means the same number of people will get randomised to the treatment and the control. |
| Discussions about representativeness of sample | Discussion about the characteristics of people included in research studies and whether they are representative of the population of interest |
| Recruitment and retention projections | Recruitment is the process through which an individual is recruited as a study participant. Participant retention is the engagement of the participant in the research study. |
| Stop/go criteria | Often trials include specific criteria to decide on whether they should move forward, i.e. collect all data as planned or stop due to unfeasibility of recruitment, treatment delivery or due to treatment harm. |
| Data monitoring committee data discussions | A committee that may be established by the trial sponsor to assess at intervals, the progress of a clinical trial, the safety data, and the critical efficacy endpoints, and to recommend to the sponsor whether to continue, modify, or stop a trial. (INVOLVE; webpage consulted in 09/11/2020) |
| Missing data | When a participant outcome is unavailable, due to a missing questionnaire or non-attendance to a trial related clinical appointment |
| Cost-effectiveness (value for money) | Economic analysis that views effects in terms of overall health specific to the problem, and describes the costs for some additional health gain (e.g. cost per additional stroke prevented). |
| Interpretation of trial results and their dissemination | Discussion about trial results (presented as numbers, for example, treatment effects) and how to present them to patients and the public |
Fig. 2Number of responses that classified each numerical aspect as their top 2; each participant could vote twice so the total is 28 (14 participants × 2) (stage 2)