Conventional quality-of-life data analyses underestimate gains in Mantle Cell Lymphoma clinical trials: A re-examination of enrich study data

J Jeremy Hobart (1University Hospitals Plymouth NHS Trust, Plymouth, United Kingdom) D David Andrich (2University of Western Australia Medical School, Perth, Australia) M Mats Jerkeman T Toby Eyre (3Oxford University Hospitals NHS Foundation Trust, Churchill Cancer Center, Oxford, United Kingdom) M Mark Bishton (4Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom) I Ingrid Glimelius A Annika Pasanen (6Helsinki University Hospital, Helsinki Comprehensive Cancer Center, Helsinki, Finland) A Andrew Davies N Nick Morley (8Sheffield Teaching Hospitals, Sheffield, United Kingdom) C Christopher McNamara (8University College London Hospitals NHS Foundation Trust, Haematology, London, United Kingdom) C Cathy Burton (4The Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom) N Nicola Crosbie (17University Hospitals Plymouth NHS Trust, Plymouth, United Kingdom) C Christian Poulsen (17Zealand University Hospital, Roskilde, Denmark) K Karin Wader (5St Olav University Hospital, Department of Oncology, Trondheim, Norway) I Ingemar Lagerlöf (14Linköping University Hospital, Linköping, Sweden) J Jacob Christensen (Gentofte Hospital, Hellerup, Denmark) J Jon Riise (16Oslo University Hospital, Oslo, Norway) K Kristina Sonnevi (17Karolinska University Hospital, Karolinska, Sweden) S Simon Rule (18AstraZeneca, Cambridge, United Kingdom) D David Lewis

Abstract

Abstract Introduction ENRICH compared Ibrutinib + Rituximab (IR) with immunochemotherapy (R-CHEMO) as first line treatments for mantle cell lymphoma (MCL) in older people. A key secondary outcome was quality of life (QOL), measured using the EORTC QLQ-C30 (QLQ-C30), a patient-reported outcome measure (PROM) used widely in cancer clinical trials. PROMs have fixed and restricted measurement ranges. These can constrain the magnitude of observed changes when samples are not well aligned to PROMs. We demonstrate, using ENRICH study QLQ-C30 physical functioning subscale (PF-5) data, that the PF-5 constrained measurement. When this constraint was reduced, remarkably different results were found. Patients and methods People aged ≥60ys with untreated, stage II-IV MCL were randomised to IR or R-CHEMO. The treatment arms were 6-8 cycles of chemotherapy or daily Ibrutinib. Both arms received Rituximab; 6-8 cycles then maintenance for 2ys. Ibrutinib participants continued daily treatment until disease progression or unacceptable toxicity. QLQ-C30 data were collected at baseline (BL), end of treatment (EOT) and end of maintenance (EOM). PF-5 data were analysed using the modern psychometric method Rasch measurement theory (RMT). Unlike conventional PROM data analyses, RMT provides forensic, context-specific information enabling measurement problems to be diagnosed and managed. Specifically, changes in PF-5 physical functioning estimates from BL to EOT in the total sample (TS), where measurement of many people was constrained (see below), were compared with changes in the subset of people whose physical functioning measurement was unconstrained (UC sample). PF-5 changes were compared at the group-level (p-values from paired samples t-tests, effect sizes [ES]) and individual-level (percent of sample with a significant improvement in PF-5 estimate, SI%). Results ENRICH randomized 397 patients (IR=199; R-CHEMO=198). QLQ-C30 data were available for n=376 at BL, and n=297 at BL and EOT. In the people with data at BL and EOT (TS; n=297), PF-5's measurement range was poorly aligned to the BL physical functioning of ENRICH participants. The distribution of PF-5 estimates was artificially skewed, and the ceiling effect was 30%, indicating constrained measurement as many people could not improve their PF-5 estimate. Only n=151 (50.8%) participants had unconstrained measurement as they could have a significant change from their BL estimate (UC sample) The group-level and individual-level changes for the two treatment arms, in the TS and UC samples are below. They show: A) group level changes were statistically and clinically trivial in the total sample, but significant in the UC sample. B) more individuals had significant changes in NC than TS sample. C) IR was superior to R-CHEMO, albeit not significantly. R-CHEMO [p (ES), SI%] TS: p=0.816, ES=-0.02, SI%=4.3%; UC: p=0.026, ES=0.25, SI%=7.4%. I-R [p (ES), SI%] TS: p=0.111, ES=0.13, SI%=8.3%; UC: p=<0.001, ES=0.43, SI%=17.1%. The Fatigue and Global Health Status QLQ-C30 subscales were also examined fully. The same pattern of results was found. The other six multi-item QLQ-30C subscales were examined partially. All had evidence of constrained measurement implying the same pattern of results would be found. Conclusion ENRICH was associated with statistically and clinically significant gains in QoL that conventional total sample analyses underestimated and misrepresented. IR was consistently superior to R-CHEMO. Results imply clinical trial PROM data should be examined pro-actively for constrained measurement. This supports the routine use of modern psychometric methods like RMT, to better capture patient-perceived benefits. .

Article Details

Journal Blood
Volume / Issue Vol. 146, Issue Supplement 1
Published November 03, 2025
Pages 1804-1804
ISSN 0006-4971
Publisher Elsevier BV

Journal Info

Blood

Elsevier BV

ISSN: 0006-4971 Health Sciences

Authors (20)

J

Jeremy Hobart

1University Hospitals Plymouth NHS Trust, Plymouth, United Kingdom

D

David Andrich

2University of Western Australia Medical School, Perth, Australia

M

Mats Jerkeman

T

Toby Eyre

3Oxford University Hospitals NHS Foundation Trust, Churchill Cancer Center, Oxford, United Kingdom

M

Mark Bishton

4Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom

I

Ingrid Glimelius

A

Annika Pasanen

6Helsinki University Hospital, Helsinki Comprehensive Cancer Center, Helsinki, Finland

A

Andrew Davies

N

Nick Morley

8Sheffield Teaching Hospitals, Sheffield, United Kingdom

C

Christopher McNamara

8University College London Hospitals NHS Foundation Trust, Haematology, London, United Kingdom

C

Cathy Burton

4The Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom

N

Nicola Crosbie

17University Hospitals Plymouth NHS Trust, Plymouth, United Kingdom

C

Christian Poulsen

17Zealand University Hospital, Roskilde, Denmark

K

Karin Wader

5St Olav University Hospital, Department of Oncology, Trondheim, Norway

I

Ingemar Lagerlöf

14Linköping University Hospital, Linköping, Sweden

J

Jacob Christensen

Gentofte Hospital, Hellerup, Denmark

J

Jon Riise

16Oslo University Hospital, Oslo, Norway

K

Kristina Sonnevi

17Karolinska University Hospital, Karolinska, Sweden

S

Simon Rule

18AstraZeneca, Cambridge, United Kingdom

D

David Lewis