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. 2024 Nov 23;24(1):290.
doi: 10.1186/s12874-024-02408-x.

The role of the estimand framework in the analysis of patient-reported outcomes in single-arm trials: a case study in oncology

Doranne Thomassen  1 , Satrajit Roychoudhury  2 , Cecilie Delphin Amdal  3   4 , Dries Reynders  5 , Jammbe Z Musoro  6 , Willi Sauerbrei  7 , Els Goetghebeur  5 , Saskia le Cessie  8   5   9 ; SISAQOL-IMI Work Package 3
Collaborators, Affiliations

The role of the estimand framework in the analysis of patient-reported outcomes in single-arm trials: a case study in oncology

Doranne Thomassen et al. BMC Med Res Methodol. .

Abstract

Background: Patient-reported outcomes (PROs) play an increasing role in the evaluation of oncology treatments. At the same time, single-arm trials are commonly included in regulatory approval submissions. Because of the high risk of biases, results from single-arm trials require careful interpretation. This benefits from a clearly defined estimand, or target of estimation. In this case study, we demonstrated how the ICH E9 (R1) estimand framework can be implemented in SATs with PRO endpoints.

Methods: For the global quality of life outcome in a real single-arm lung cancer trial, a range of possible estimands was defined. We focused on the choice of the variable of interest and strategies to deal with intercurrent events (death, treatment discontinuation and disease progression). Statistical methods were described for each estimand and the corresponding results on the trial data were shown.

Results: Each intercurrent event handling strategy resulted in its own estimated mean global quality of life over time, with a specific interpretation, suitable for a corresponding clinical research aim. In the setting of this case study, a 'while alive' strategy for death and a 'treatment policy' strategy for non-terminal intercurrent events were deemed aligned with a descriptive research aim to inform clinicians and patients about expected quality of life after the start of treatment.

Conclusions: The results show that decisions made in the estimand framework are not trivial. Trial results and their interpretation strongly depend on the chosen estimand. The estimand framework provides a structure to match a research question with a clear target of estimation, supporting specific clinical decisions. Adherence to this framework can help improve the quality of data collection, analysis and reporting of PROs in SATs, impacting decision making in clinical practice.

Keywords: Estimand; Oncology; Patient-reported outcomes; Quality of life; Repeated measurements; Single-arm trial.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: No data were collected for the purpose of this study. Anonymized data from a previously published trial were shared with us for the illustration of analysis methods, respecting the informed consent previously provided by the trial participants. Consent for publication: Not applicable. Competing interests: Satrajit Roychoudhury, is an employee of Pfizer Inc., and a stockholder of Pfizer Inc. and Novartis Pharmaceutical. Els Goetghebeur and Dries Reynders contribute to a university-based statistical consulting service that has received limited consulting fees from Pfizer, no personal fees. There are no other potential conflicts of interests among the authors.

Figures

Fig. 1
Fig. 1
Availability of PROs over time, in relation to treatment and survival status. Note that by design of the study, almost no PROs were reported after discontinuation of protocol treatment. As a result, there are no to very few patients in state 2 at each cycle
Fig. 2
Fig. 2
Mean and 95% CI of three possible variables of interest, within available (unimputed) data at each cycle. Top graph: absolute global QoL. Middle graph: difference in global QoL from the start of protocol treatment. Bottom graph: responder classification, where response for a patient is defined as an increase of at least 10 points in global QoL from the start of protocol treatment
Fig. 3
Fig. 3
Estimated mean global quality of life in each cycle for different estimands and corresponding models. The estimands differ in the chosen strategy to deal with the event of death in the analysis of the PROs. The two statistical methods applied to estimate mean QoL while alive (1a and 1b) overlap in the figure, as their resulting estimates were very similar
Fig. 4
Fig. 4
Estimated mean global quality of life in each cycle for different estimands and corresponding models. The estimands differ in the chosen strategy to deal with the event of treatment discontinuation (TD) and death in the analysis of the PROs
Fig. 5
Fig. 5
Estimated mean global quality of life in each cycle for different estimands and corresponding models. The estimands differ in the chosen strategy to deal with the event of disease progression (PD) and death in the analysis of the PROs
See this image and copyright information in PMC

References

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