What is it? Why is it important?

A Data-Analysis-Set defines which study participants to include in an analysis.

 

Commonly used Data-Analysis-Sets are the:

  • Full-Analysis-Set (FAS): Includes all randomized study participants, regardless of study protocol adherence or deviations
  • Per-Protocol-Set (PPS): Includes only participants who did strictly adhere to a predefined set of protocol requirements (i.e. as defined by the SP-INV)

 

The FAS aims at an Intention-To-Treat (ITT) analysis. It includes all participants randomly assigned to a group (e.g. intervention or control group) irrespective of their adherence to the treatment protocol. It estimates the effect of being assigned to the treatment versus the control group.

 

The PPS assesses the performance of an intervention among participants who did closely follow the study protocol. While a PPS analysis can provide valuable insights into the performance of an intervention, one should keep in mind that analysis results may be biased (more information under More).

More

Biased results

In a Randomized Controlled Trial (RCT), the random allocation of participants to different treatment groups ensures that the two groups are comparable. When selecting a subset of randomized participants for the analysis, similarities between treatment groups can no longer be guaranteed. As a consequence, a bias may occur.

 

Example: In an active treatment vs. placebo study, all participants with an early treatment discontinuation are removed from the PPS. This may exclude participants with severe side effects in the active treatment group. Consequently, the intervention group may look better than it actually is.

What do I need to do?

As a SP-INV, define applicable analysis-set(s) for you study.

  • FAS: As all study participants are included its definition is usually straightforward
  • PPS: Only include participants who have no major protocol deviations. Define events considered as major protocol deviations (e.g. not meeting specific eligibility criteria, not completed study visits, or visits done outside the allowed timeframe, non-compliance with study treatment)

 

Document criteria used to define Data-Analysis-Sets in the study protocol and, as applicable, in the Statistical Analysis Plan (SAP).

 

As required, alternative analysis-sets can be defined. Example: A safety analysis set may be defined as all participants who received at least one dose of the study drug. Analyses of safety outcomes would then be performed on this analysis set.

Where can I get help?

Your local Research Support Centre↧ can assist you with experienced staff regarding this topic

  • Basel, Departement Klinische Forschung (DKF), dkf.unibas.ch

  • Lugano, Clinical Trials Unit (CTU-EOC), ctueoc.ch

  • Bern, Department of Clinical Research (DCR), dcr.unibe.ch

  • Geneva, Clinical Research Center (CRC), crc.hug.ch

  • Lausanne, Clinical Research Center (CRC), chuv.ch

  • St. Gallen, Clinical Trials Unit (CTU), h-och.ch

  • Zürich, Clinical Trials Center (CTC), usz.ch

Video

References

ICH GCP E6 (R3) -see in particular:

  • 3.16.2 Statistical Programming and Data Analysis
  • 4.2.6c Data sets prior to analysis

ICH Topic E9 – see in particular

  • 5.2 Analysis sets

Swiss Law

ClinO – see in particular article

  • Art. 2b Definition intervention

ClinO-MD – see in particular article

  • Art. 2a Definition of clinical intervention
  • Art. 2a Definition of performance study

HRO – see in particular article

  • Art. 3a Definition of research
Abbreviations
  • ClinO – Clinical Trials Ordinance
  • ClinO-MD – Ordinance on Clinical Trials of Medical Devices
  • HRO – Human Research Ordinance
  • ICH – International Council for Harmonisation
  • ICH GCP – International Council for Harmonisation Good Clinical Practice
  • SAP – Statistical Analysis Plan
  • SP-INV – Sponsor Investigator
  • FAS – Full-Analysis-Set
  • PPS – Per-Protocol-Set
  • ITT – Intention-To-Treat
  • RCT – Randomized Controlled Trial
Development ↦ Statistic Methodology ↦ Statistics in the Protocol ↦ Data-Analysis-Set
Study
Basic

Provides some background knowledge and basic definitions

Basic Monitoring
Concept

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Statistic Methodology
Concept Drug or Device
Development

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Drug or Device
Set-Up

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Ethics and Laws
Set-Up Statistic Methodology
Set-Up Quality and Risk
Set-Up Drug or Device
Conduct

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Statistic Methodology
Conduct Drug or Device
Completion

Starts with last study visit completed

Ends after study publication and archiving

Completion Drug or Device
Current Path (click to copy): Development ↦ Statistic Methodology ↦ Statistics in the Protocol ↦ Data-Analysis-Set