Principle O3: the more data, the better

Everyone running or taking part in studies should be aware that collecting as much as possible of a study’s planned data can help a study reach a clear and reliable conclusion.

This should be made clear to potential study participants using ethically-approved wording before they agree to take part in the study.

Clinical trials and other studies are designed to answer research questions by collecting certain data about a specific number of study participants. If the study analysis includes fewer participants’ data than planned, this can make study results less reliable.

For example, it might mean results are unclear about whether or not a new treatment is better, when in reality it is better. For this reason, it is important that as much of the relevant data as possible is included in the study analysis. This is particularly the case for study outcome data, i.e. the data that will be used to answer the study’s research questions.

Everyone running and designing studies should be aware of this, so that they can take appropriate action to make sure the study collects enough of the planned data to answer the main research question, wherever possible.

It is also important that study participants know how vital data collection is to the accuracy of study results.

This information must not prevent study participants doing what is right for them – for example stopping study-specific hospital visits, or stopping data being collected entirely, if that is what they want to do.

But they should be aware – before they join the study – of the effect of their data not being available for analysis, so that they can make an informed choice about any changes to their participation.

For example, it is sometimes possible for participants to stop study-specific visits but continue participating in the study in other ways that involve less commitment from them.

Participants might like to do this, if they are given the choice. Studies should also be designed to allow this, where possible (see principle D1 on protecting study integrity by design).

Other important considerations

The amount of data collected for a study is not the only important thing. It is also important that the data is good ‘quality’, for example that it is accurate and contains the details that are needed to reliably answer the study’s research questions.

Collection of quality data should be achieved through robust data collection and data management mechanisms. If any participants are happy to continue taking part but with reduced commitment, they should only be presented with options that will still provide reliable data to answer the study’s research questions (see principle D1).

See also:

Related PeRSEVERE resources:

Related PeRSEVERE principles:

  • The benefits of collecting as much as possible of the study’s planned data should be communicated to potential study participants so that they can use this information to inform their decisions about stopping, reducing or changing their participation. See principle O2.
  • Any forms of reduced participation, i.e. participants continuing to take part in some capacity but with reduced commitment, should be feasible and should not negatively affect the scientific integrity of a study. See principle D1.
  • Participants should receive clear and balanced information, before they agree to take part in a study, about what will happen if they want to stop or change their participation later on. See principle D3 for more on this.
  • Dialogue about study participation should be encouraged between researchers and participants throughout the study. See principle D5 for more on this. These discussions will be more productive if participants are as informed as they can be about the implications of their decision.
  • We use the term “running” a study here to mean all activity involved in making a study happen, including getting the relevant approvals to start the study, working with the NHS and other organisations to set up study sites where participants will be recruited, making decisions about the management of the study, collecting and processing study data, and so on.
  • Data collection: this means the act of adding relevant data onto study forms or systems, to make the data available for running and analysing each study. It does not refer to any separate tests or procedures used to generate the data in the first place.
  • Outcome data: all research studies involve measuring something in order to reach conclusions. The thing being measured is the ‘outcome’, and data about it is the outcome data. For example, a healthcare study might give two groups of people different treatments, then measure how their health changes over time to see which treatment is better. The data about how the participants’ health changes over time is the outcome data. Outcome data is particularly important, because without it, studies cannot reach clear conclusions.