Predicting cancer patients’ quality of life: an analysis of the relationship between utility,...

Paula Lorgelly (OHE)Brett Doble (Cambridge)Rachel Knott (Monash)

Predicting cancer patients’ quality of life: an analysis of the relationship between utility, treatment regimes and time


Acknowledgements

• Cancer 2015 investigators, specifically Stephen Fox, David Thomas and John Parisot

• Funding from the Victorian Cancer Agency • The cancer patients who agreed to participate and

hospitals from which they were recruited• Cancer 2015 Expert Advisory Committee consisting of

Richard Sullivan, John Zalcberg, Andrew Biankin, Sean Grimmond, David Roder and David Goldstein


Background

• Cancer treatment has considerable toxicities/adverse events/side effects particularly chemotherapy and radiotherapy• Fatigue, nausea, hair loss, infections, skin irritation

• Detrimental to a patient's quality of life (QoL)• Clinical trials often evaluate QoL pre- and post-treatment,

specifically once improved, not necessarily during treatment phase

• Some health state valuation studies have directly elicited values for treatment effects / adverse events• Lloyd et al, 2006 Utility of stages and disutility of toxicity in

metastatic breast cancer (standard gamble)• Fordham et al, 2015 Treatment response, disease

progression, adverse events in thyroid cancer (time tradeoff)


Objective

• This paper seeks to quantify the disutility of specific cancer treatments

• Using health state utility instruments in cancer patients

• Provide a better understanding of the quality of life trajectory of cancer patients on various treatment pathways


Data: Cancer 2015

• A Victorian, whole-of-system, large-scale (Framingham-type) prospective longitudinal population-based molecular cohort study

• Total cancer journey from diagnosis, to treatment and management, and death

• Cross-disciplinary approach• Test and implement a new model of cancer diagnosis and

treatment, with a specific focus on integrating molecular pathology into routine cancer diagnosis

• www.cancer2015.org

http://www.cancer2015.org/


How does it work?

• Enrols newly diagnosed/treatment naïve cancer patients, irrespective of the tumour site (except leukaemia) and at all stages of disease

• Phase 1 targeted the enrolment of 1,000 patients from 5 hospitals in Victoria (including a private hospital)

• Patients consent to the study, tumour samples (biopsies) and blood are collected and a baseline questionnaire is completed


How does it work? cont.

• Baseline questionnaire collects information on patient demographics, patient/familial history, tumour site and stage, treatment intentions, patient reported outcome measures (PROMS) – the EORTC-QLQ-C30 and the EQ-5D-3L

• PROMS are repeated at six months and 12 months, unless deemed to have severe disease (FU at three months)

• Also at follow-up collect information on treatment (chemotherapy, radiotherapy, and surgery)

• The genotyping and mutation testing are undertaken using the TruSeq Amplicon Cancer Panel which tests for mutations in 48 cancer genes; later added to the database


Summary of Patient Accrual


Geographical location


Cancer histo-site & stage


Site Representativeness



• At enrolment patients are asked to consent to have their Federal (Commonwealth) and State administrative health data linked• In Australia the Commonwealth Department of Health

subsidises pharmaceuticals (PBS) and medical services (MBS) (e.g. primary care consultations)

• While the State (or Territory) Department of Health funds public hospital care, but they also collect information on private hospital admissions

• (Commonwealth) DHS sends MBS/PBS data for consenting patients, which we link to the cohort, we de-identify the cohort to an extent and send this to the (Victorian) DOH – Victorian Data Linkage (VDL) unit



• VDL then send us back our de-identified cohort+PBS/MBS data, VAED (admission) and VEMD (emergency) data sets, and encryption key

• The linked dataset is arguably the most comprehensive in existence, as it includes genomic, clinical, quality of life and resource use records for a range of cancer patients

• Lorgelly et al, 2016, Realising the Value of Linked Data to Health Economic Analyses of Cancer Care: A Case Study of Cancer 2015, Pharmacoeconomics, 34(2):139-154.


Cancer 2015 master database

Pathology and sequencing data

(baseline & follow-up)

MBS/PBS administrative

records for consenting

patients

VAED VEMD

De-identified database for health economics analysis

Patient demographic, history, disease, treatment data

(baseline & follow-up)

Identified dataset, linkage by Cancer 2015 researchers

Linkage by VDL using standard methodology for matching

De-identified datasets returned with linkage key

Commonwealth Government

State Government

Data collection and linkage


Deceased

Cancer DxCancer 2015

enrolment

GP visit

QoL QoL

Chemotherapy

Pathology/radiology

Psychologist Psychologist

Radiotherapy

Statins and BP lowering meds

Patient X Case History


Sample

• Initial analysis constrained to cohort data only• Tx effect only surgery, radiotherapy and chemotherapy

• Baseline EQ-5D-3L scores for 1,715 patients• 1st follow-up QoL for 1,105 patients• 2nd follow-up QoL for 616 patients


Model

• QOLT = f(eventT, eventT-1, eventT-2, eventT-3, eventT-4, eventT-5, QOLbaseline, patient, disease, hospital)

• Data are arranged monthly• Event is surgery, chemotherapy or radiotherapy• Patient, disease and hospital characteristics are time

independent• Initially consider first follow-up only, subsequently include

second follow-up by clustering on PID


Descriptives, N=1,105

• Baseline EQ-5D = 0.746 (-0.594,1)• Follow-up EQ-5D = 0.682 (-0.429,1)

• Subsequent evidence over time estimated +ve QALY gains 0.860 (range -0.108, 3.138)

• 38% radiotherapy in previous 6 months• 40% had chemotherapy in previous 6 months• 25% had surgery in previous 6 months


Patterns of treatmentRadiotherapy – same month 1.99%Radiotherapy – one month before 3.80%Radiotherapy – two months before 4.43%Radiotherapy – three months before 4.52%Radiotherapy – four months before 4.98%Radiotherapy – five months before 12.04%Chemotherapy – same month 1.99%Chemotherapy – one month before 2.71%Chemotherapy – two months before 4.43%Chemotherapy – three months before 5.07%Chemotherapy – four months before 5.25%Chemotherapy – five months before 11.95%Surgery – same month 1.18%Surgery – one month before 2.35%Surgery – two months before 2.99%Surgery – three months before 2.81%Surgery – four months before 1.99%Surgery – five months before 3.71%


Sites of cancerbreast

prostate

headneck

colo

lung

bone

cervic

renal

CUP

oesoph

othersite


Stage of cancerstage 0

stage 1

stage 2

stage 3

stage 4

stage 5

unable to stage


Patient/hospital characteristics

• Mean age 62 years• 56% male• 14% current smoker, 47% ex-smoker• 40% have health insurance (indicator of high income)

(N=1,086)

• 74% had curative treatment intention, 19% had palliative • 15% treated (recruited) in private hospital


V1 V2 V3 V4 V5 Baseline EQ-5D 0.509*** 0.510*** 0.518*** 0.511** 0.259***Radiotherapy – same month -0.025 -0.029 0.023Radiotherapy – one month before -0.149*** -0.157*** -0.107**Radiotherapy – two months before -0.116*** -0.111*** -0.072*Radiotherapy – three months before -0.021 -0.031 -0.012Radiotherapy – four months before -0.017 -0.023 -0.007Radiotherapy – five months before 0.017 0.001 -0.010Chemotherapy – same month -0.057 -0.041 0.002Chemotherapy – one month before -0.125** -0.106* -0.062Chemotherapy – two months before -0.056 -0.009 0.071*Chemotherapy – three months before -0.006 -0.005 0.033Chemotherapy – four months before 0.012 0.031 0.049Chemotherapy – five months before 0.047 0.056 -0.005Surgery – same month 0.060 0.044 0.072Surgery – one month before -0.134*** -0.144*** -0.147Surgery – two months before -0.052 -0.049 -0.029Surgery – three months before -0.009 0.005 -0.058Surgery – four months before -0.080 -0.066 -0.101*Surgery – five months before 0.026 0.028 -0.043






V1 V2 V3 V4 V5 Baseline EQ-5D 0.509*** 0.510*** 0.518*** 0.511*** 0.259***Radiotherapy – same month -0.025 -0.029 0.023Radiotherapy – one month before -0.149*** -0.157*** -0.107**Radiotherapy – two months before -0.116*** -0.111*** -0.072*Radiotherapy – three months before -0.021 -0.031 -0.012Radiotherapy – four months before -0.017 -0.023 -0.007Radiotherapy – five months before 0.017 0.001 -0.010Chemotherapy – same month -0.057 -0.041 0.002Chemotherapy – one month before -0.125** -0.106* -0.062Chemotherapy – two months before -0.056 -0.009 0.071*Chemotherapy – three months before -0.006 -0.005 0.033Chemotherapy – four months before 0.012 0.031 0.049Chemotherapy – five months before 0.047 0.056 -0.005Surgery – same month 0.060 0.044 0.072Surgery – one month before -0.134*** -0.144*** -0.147Surgery – two months before -0.052 -0.049 -0.029Surgery – three months before -0.009 0.005 -0.058Surgery – four months before -0.080 -0.066 -0.101*Surgery – five months before 0.026 0.028 -0.043


V1 V2 V3 V4 V5 Baseline EQ-5D 0.509*** 0.510*** 0.518*** 0.511*** 0.259***Radiotherapy – same month -0.025 -0.029 0.023Radiotherapy – one month before -0.149*** -0.157*** -0.107**Radiotherapy – two months before -0.116*** -0.111*** -0.072*Radiotherapy – three months before -0.021 -0.031 -0.012Radiotherapy – four months before -0.017 -0.023 -0.007Radiotherapy – five months before 0.017 0.001 -0.010Chemotherapy – same month -0.057 -0.041 0.002Chemotherapy – one month before -0.125** -0.106* -0.062Chemotherapy – two months before -0.056 -0.009 0.071*Chemotherapy – three months before -0.006 -0.005 0.033Chemotherapy – four months before 0.012 0.031 0.049Chemotherapy – five months before 0.047 0.056 -0.005Surgery – same month 0.060 0.044 0.072Surgery – one month before -0.134*** -0.144*** -0.147***Surgery – two months before -0.052 -0.049 -0.029Surgery – three months before -0.009 0.005 -0.058Surgery – four months before -0.080 -0.066 -0.101*Surgery – five months before 0.026 0.028 -0.043


Discussion

• Treatment (as a crude yes/no) appears to have an (lagged) effect on quality of life• At least for surgery and radiotherapy• Unclear the effect of chemotherapy• Relatively contemporaneous effect

• Limitations• Unable to control for disease progression, patients are not

restaged, but do have ECOG performance measure• Currently ignoring treatment intensity or specifics of

treatment regime• Have not yet explored ‘richness’ of the data


Danke Schön

Date post:	12-Apr-2017
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Predicting cancer patients’ quality of life: an analysis of the relationship between utility,...

Presentations & Public Speaking