Leveraging Real-World Data for Time-to-Event Endpoints in Clinical Trials


Parashar D1, Almgren P2, Berglund A3, Guasconi A4, Smith C5, Torlinska B6, Wang J7, Wang Q8
1University of Warwick, Coventry, UK, 2LEO Pharma, Ballerup, Denmark, 3Daiichi Sankyo Europe GmbH, Munich, Germany, 4CHIESI FARMACEUTICI, Parma, Italy, 5AstraZeneca, Cambridge, UK, 6University of Birmingham, Birmingham, UK, 7Bristol-Myers Squibb, Steinhausen, Switzerland, 8F. Hoffmann-La Roche AG, Basel, Switzerland

Presentation Documents

Objectives: Use of Real-World Data (RWD) to augment evidence in clinical trials is often constrained by the type of endpoints considered. Particular limitations arise for Time-to-Event (TTE) endpoints where there are differences in missing events between trial data and RWD, definition of “time zero”, reporting bias, immortal time bias, among others. This research focuses on the problem of time zero in RWD, reviews currents methods to address this challenge, and makes recommendations.

Methods: Uncertainty in defining time zero arises when it is not clear if baseline of therapy initiation is the same for subjects in the clinical trial and those in RWD, especially the control arm. Not knowing the common baseline makes comparison of events difficult, and subsequent results less reliable.

Results: Drawing upon experience of TTE endpoints across therapeutic areas, we (Methodology Team of PSI Special Interest Group on Real-World Data) propose leveraging RWD for ‘softer’ endpoints for time zero such as time to ‘first’ events in neuroscience (multiple sclerosis relapse), respiratory (asthma exacerbations), infectious diseases (viral suppression), atopic dermatitis (change in treatment), oncology (treatment discontinuation, next therapy).

Conclusions: We make recommendations to mitigate the time zero uncertainty in RWD for TTE by adjusting the baseline to ‘first’ events for multiple diseases.

Conference/Value in Health Info

2021-11, ISPOR Europe 2021, Copenhagen, Denmark

Value in Health, Volume 24, Issue 12, S2 (December 2021)




Clinical Outcomes, Methodological & Statistical Research, Real World Data & Information Systems

Topic Subcategory

Comparative Effectiveness or Efficacy, Confounding, Selection Bias Correction, Causal Inference, Health & Insurance Records Systems


Multiple Diseases

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