Effects of Pharmacological Exposure on Ovarian Cancer (EPOC)

Sharp et al. Ovarian Pharmacoepidemiology ENCePP Protocol

Effects of pharmacological exposure on Ovarian Cancer (EPOC)

PROTCOL & ANALYSIS PLAN

Project funded by Health Research Board

Date: 01/08/2014

Version: V1.0

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1. Project Synopsis Project Title: Translating basic science into improved patient outcomes in ovarian cancer: an Ireland-UK collaboration investigating common pharmacological exposures and tumour characteristics, recurrence, survival and mortality.

Research theme: Pharmacoepidemiology NCRI Staff Members: Mr Chris Brown, Dr Linda Sharp Funding source: Health Research Board Project duration: 2013 - 2015 Project Overview: By combining data from large pharmacoepidemiology databases in Ireland and the UK, will conduct a population-based study investigating associations between use of statins, beta-blockers and NSAIDs and ovarian cancer progression and outcome. This study will provide valuable population-based evidence on the effects of these common drugs on ovarian cancer and contribute to improved health outcomes for women affected by this cancer.

Investigators: Dr Kathleen Bennett, Pharmacology & Therapeutics, Trinity College, St James’s Hospital Dublin Dr Thomas Ian Barron, Department of Pharmacology & Therapeutics, Trinity College Dublin Prof Liam Murray, Centre for Public Health, Queen’s University Belfast Dr John Coulter, Gynaecological Oncology, South Infirmary Victoria University Hospital, Cork

Collaborators: Prof Carmel Hughes, School of Pharmacy, Queen’s University Belfast Dr Chris Cardwell, Centre for Public Health, Queen’s University Belfast Prof Rose Anne Kenny, Department of Medical Gerontology , Trinity College Dublin Prof John O’Leary, Histopathology , Trinity College Dublin Dr Sharon O’Toole, Obstetrics and Gynaecology/Histopathology, Trinity College Dublin

Figure 1 - Project Overview

Statins Beta-Blockers NSAIDs

Drugs type/dose/duration

Pre-diagnosis Pre/post-diagnosis exposure OVARIAN exposure CANCER

Stage/Grade Treatment Recurrence Survival

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2. Contents 1. Project Synopsis ...... 2 2. Contents ...... 3 3. Abbreviations ...... 5 4. Project description ...... 6 5. Drugs of interest ...... 7 6. Research questions ...... 9 6.1. Data Sources ...... 9 6.1.1. Dataset 1 - Ireland ...... 9 6.1.2. Dataset 2- Northern Ireland ...... 10 6.1.3. Dataset 3- United Kingdom ...... 11 6.2. Data Harmonisation ...... 11 6.3. Study population ...... 11 6.4. Study design ...... 11 6.5. Analysis Phase 1 ...... 12 6.6. Analysis Phase 2 ...... 13 6.7. Analysis Phase 3 ...... 14 7. Outcome definitions ...... 14 8. Drug definitions...... 15 9. Covariates ...... 16 10. Sample size considerations ...... 17 11. Blinding ...... 17 12. Confounding ...... 18 13. Missing data ...... 18 14. Predictive modelling ...... 18 15. Interim analysis ...... 18 16. Multiplicity ...... 18 17. Software ...... 19 18. Project Timeline ...... 19 19. Reporting...... 19 20. References ...... 20 Appendix 1. Analysis tables ...... 24 Appendix 2. Drugs of Interest ...... 27

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Listing of tables Table 1 - Baseline Characteristics ...... 24 Table 2 - Treatment at diagnosis ...... 25 Table 3 - Prescription history ...... 25 Table 4 - Survival estimates ...... 26 Table 5 - Effect of treatment on Tumour stage ...... 26

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3. Abbreviations Summary of abbreviations used in alphabetical order

ATC WHO Anatomical Therapeutic Chemical (classification) CA-125 Cancer antigen 125 CI Confidence interval COIS Clinical Oncology Information System EPD Enhanced Prescribing Database EPOC Effects of pharmacological exposure on Ovarian Cancer GMS General Medical Services Scheme Prescription Refill Database GP General practitioner CPRD Clinical Practice Research Database HIPE Hospital Inpatient Enquiry scheme HR Hazard ratios HSE Health Service Executive ICD International Classification of Diseases IRE Ireland NCDR National Cancer Data Repository NCIN National Cancer Intelligence Network NCRI National Cancer Registry of Ireland NHS National Health Service NI Northern Ireland NI GRO Northern Ireland General Register Office NICR Northern Ireland Cancer Registry NSAIDs Non-steroidal anti-inflammatory drugs OR Odds ratios OS Overall Survival PCRS Primary Care Reimbursement Services PFS Progression free survival STROBE Strengthening the Reporting of Observational studies in Epidemiology TRO Tumour Registration Officer TVO Tumour Verification Officer UK United Kingdom

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4. Project description Almost 250,000 ovarian cancers are diagnosed worldwide each year. Incidence rates are high in northern Europe, including in the Ireland and UK. These countries also have low survival and, consequently, among the highest ovarian cancer mortality rates in the world. The relatively young average age at diagnosis, advanced stage, and poor survival, means that ovarian cancer constitutes a major burden in terms of years of life lost. Research into factors that influence disease spread at diagnosis, and treatment response and survival would be of considerable public health benefit. One promising route is investigation of the role of common pharmaceutical agents. Pre-clinical studies suggest that various commonly-used drugs, including statins, beta-blockers and non-steroidal anti- inflammatory drugs (NSAIDs) might have potent anti-tumour effects in ovarian cancer, findings not yet confirmed in human populations. We will combine large, population-based, pharmacoepidemiology databases from Ireland, Northern Ireland and Great Britain, to investigate associations between exposure to (a) statins, (b) beta-blockers and (c) NSAIDs and ovarian cancer presentation, progression and outcomes. Phase 1 will use a retrospective cohort design to investigate associations between pre- and post-diagnosis exposure and mortality (disease-specific and overall survival). Phase 2 will use a nested case-control design to investigate associations between pre-diagnosis drug exposure and stage and grade at diagnosis. Phase 3 will use a retrospective cohort design to investigate associations between pre- and post-diagnosis exposure and disease recurrence (disease-free survival). Analysis will use logistic regression and Cox proportional hazards models, with adjustment for known confounders. Sensitivity analyses will explore potential impact of unmeasured confounding. Results will be translated into years of life gained to assess potential public health impact. The study will provide valuable population-based evidence on the effects of these common drugs on ovarian cancer and contribute to improved health outcomes for women affected by this cancer.

Ovarian cancer is a significant public health problem. Almost one-quarter of a million new cases are diagnosed worldwide each year1. Within Europe, the highest incidence rates are observed in northern countries, including the Ireland and the UK. Ireland and the UK also have lower survival than average2 and, consequently, ovarian cancer mortality rates in these countries are amongst the highest in the world1. The average age at diagnosis with ovarian cancer is relatively young (around 60-62 years) and, by diagnosis, the disease is usually advanced, with significant metastatic spread. Relative survival is approximately 40% at 5 years and, unlike that for many other cancers, has improved little over time2. This means that ovarian cancer has a major impact in terms of years of life lost3.

If ways could be found to inhibit metastatic spread, and/or improve treatment response, survival or mortality for ovarian cancer, the public health benefit could be considerable. One route which offers promise is investigation of the role of common pharmaceutical agents, such as cardiovascular medications, in cancer progression. Pre-clinical studies have suggested that various commonly-used drugs (including statin, beta-blockers and non-steroidal anti-inflammatory drugs (NSAIDs)) might have potent anti-tumour activity, including significant effects in ovarian cancer, but these findings have yet to be confirmed in large-scale studies of ovarian cancers in human populations.

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5. Drugs of interest Statins

Statins target lipid metabolism by blocking the enzyme HMG CoA reductase, the rate-limiting step in endogenous cholesterol synthesis. They are used routinely to manage raised cholesterol and, in European populations, more than one third of adults aged over 30 take them4. In pre-clinical studies, statins have been found to induce apoptosis and inhibit tumour growth, angiogenesis and metastases in multiple cell lines5. In ovarian and gynaecological cancer, statins inhibit ovarian cell proliferation and induce cell death, effects that may be specific to lipophilic statins (including simvastatin)6,7. In animal models, mice treated with simvastatin survive significantly longer compared to control animals7. Statins also inhibit up-regulation of cholesterol synthesis associated with chemotherapy resistance5. In ovarian cell lines, the combination of fluvastatin with cisplatin results in significantly greater inhibition than either drug alone8 and mice treated with statins before receiving carboplatin have improved survival9.

In humans, individuals using lipid-lowering medication have only one-third of the risk of dying from cancer compared to untreated individuals10. Among cancer patients, statin use has been associated with presentation with less advanced disease and more favourable pathological features in breast, prostate and colorectal cancer11–14. Several studies suggest statin use may delay cancer progression post-diagnosis. Significantly reduced risk of recurrence has been reported among women with breast cancer prescribed lipophilic statins4,15, with a modest effect also found for prostate cancer14. Statin use has also been associated with improved survival and reduced mortality in prostate and colorectal cancer12,16,17.

In a small study of ovarian cancer (n=72), HMG-CoA reductase expression was present in 65% of tumours and patients with tumours expressing the enzyme had significantly longer relapse-free survival, after adjusting for other prognostic factors18. In another study of 130 women with advanced ovarian cancer, statin use was an independent prognostic factor for progression-free and overall survival19. These data warrant further investigation of the effect of statin use on tumour characteristics at diagnosis, and subsequent outcomes, in women with ovarian cancer.

Beta-blockers

Beta-adrenergic receptors β1, β2, and β3 control the effects of the stress hormones (catecholamines) epinephrine and norepinephrine. These receptors have been found in various cancer cells, including ovarian cells5. Beta-adrenergic receptor antagonists (beta-blockers) – a class of drugs commonly used in the management of cardiac arrhythmias, hypertension, migraine and tremor, in Ireland and elsewhere – inhibit the adrenergic effects of catecholamines on beta-adrenergic receptors. Preclinical studies increasingly suggest that andrenergic signalling can regulate several pathways necessary for cancer onset, progression and metastasis through direct effects on tumour cells, the tumour microenvironment and cell mediated immunity20. Beta-blockers, by contrast, may reduce tumour progression and metastasis by inhibiting proliferation, growth factor secretion and angiogenesis and inducing apoptosis21. In ovarian cancer, in mice and cell lines, catecholamine stimulation enhances tumour growth and invasion and expression of several key proteins implicated in carcinogenesis, including VEGF, IL8, STAT3 and various MMPs22–26, and results in lower levels of anoikis, the process by which cells enter apoptosis27. These effects are blocked by the non-selective

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beta-receptor antagonist propranolol23,24. Propanolol also blocks the tumour pro-growth and pro- angiogenic effects of surgical stress in ovarian mouse models28.

As regards epidemiological evidence, a small (n=68) ovarian cancer study found that women with higher tumour norepinephrine levels had more advanced stage and grade at diagnosis29 We recently reported that women with breast cancer using propanolol pre-diagnosis were significantly less likely to present with advanced disease and had reduced risk of death from breast cancer compared to 30 non-users, effects not seen in women using the β1-selective antagonist, atenolol . Others have reported associations between beta-blocker use and lower recurrence risk, increased relapse-free survival and reduced cause-specific mortality in breast cancer 31–33 and reduced risk of death in melanoma34. Despite these intriguing findings, and compelling pre-clinical evidence, no studies have yet investigated associations between use of beta-blockers and tumour characteristics at diagnosis, and subsequent outcomes, in women with ovarian cancer.

NSAIDs

Inflammation appears to provide the mechanistic link between various theories of ovarian tumourgenesis35, suggesting anti-inflammatory drugs may impact on ovarian tumour progression. Consistent with this, NSAIDs have been shown to cause potent growth inhibition and increased apoptosis in ovarian cancer cell lines36 and, in vivo models, treatment with aspirin and other NSAIDs is associated with development of early stage/less invasive tumours37, suppressed tumour growth and prolonged survival38.

The anti-inflammatory activity of NSAIDs is achieved through inhibition of cyclooxygenase-2 (COX-2). COX-2 over-expression promotes tumour cell proliferation, reduces apoptosis and induces angiogenesis and, in small, clinical series, COX-2 over-expression has been reported in 30-80% of ovarian cancers39. Associations between COX-2 over-expression and a more aggressive phenotype, worse prognosis and poorer outcomes have been reported for various cancers, including cancer of the ovary40. These associations may be mediated by the effects of COX-2 expression on resistance to platinum-based chemotherapy41. In ovarian mice models, treatment with the NSAID meloxicam results in decreased COX-2 expression42.

Epidemiological evidence is beginning to accrue that NSAID use may impact on cancer prognosis and outcome. In pooled analysis of data from randomised controlled trials, after ≥5 years follow-up, risk of death from any solid cancer was reduced by one-third among aspirin users43. In breast cancer, in observational studies, NSAID use has been associated with reduced risk of recurrence, bone metastases, and breast cancer-specific mortality44–47. Similarly, risk of death from colorectal cancer is reduced in women with proximal tumours who used NSAIDs pre-diagnosis48. Despite considerable pre-clinical evidence, however, no population-based studies have so far investigated NSAID use and outcomes in women with ovarian cancer.

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6. Research questions In women with ovarian cancer: 1. Is pre-diagnosis drug exposure associated with mortality? Does the association vary by the type/dose/duration of drug use? Does the association vary by whether the exposure was pre- or post-diagnosis? Is pre-diagnosis drug exposure associated with stage (tumour confined to ovaries, tumour cells in pelvic fluid, pelvic and peritoneal metastases, regional nodal involvement, distant nodal involvement, distant metastases)? Is there association with grade (well, moderately, poorly differentiated) at diagnosis? Does the association vary by the type/dose/duration of drug use? 2. Is exposure associated with recurrence? Does the association vary by the type/dose/duration of drug use? Does the association vary by whether the exposure was pre- or post-diagnosis? 3. Does receipt of chemotherapy modify associations between exposure and recurrence or survival/mortality? 4. Does receipt of cancer-directed surgery modify associations between exposure and recurrence or survival/mortality?

6.1. Data Sources The following population-based resources will be used for the main analyses:

1. Ireland (IRE): National Cancer Registry-Health Services Executive Primary Care Reimbursement Services (NCRI-PCRS) linked dataset 2. Northern Ireland (NI): Northern Ireland Cancer Registry-Enhanced Prescribing Database (NICR-EPD) linked dataset 3. United Kingdom (UK): The Clinical Practice Research Database (CPRD), and the subset linked to the National Cancer Data Repository (NCDR) dataset

6.1.1. Dataset 1 - Ireland NCRI database The National Cancer Registry Ireland (NCRI) has recorded detailed information on all newly diagnosed tumours in the population usually resident in the Ireland since 1994 (http://www.ncri.ie). Information is collected on patient demographics (including their GP), data of diagnosis, tumour characteristics at diagnosis (tumour size, nodal status, metastases, morphology, grade), treatment (cancer-directed surgery, chemotherapy and radiotherapy, within the first year post diagnosis). Date and cause of death is obtained by linkage with national death records. Completeness of registration is estimated to be in excess of 98%.

HSE-PCRS database The Health Service Executive (HSE) runs the Primary Care Reimbursement Service (PCRS) which manages reimbursement payments to the Irish health care services. Complete national prescribing records exist from the year 2000 to date from the General Medical Services Scheme Prescription Refill Database (PCRS-GMS). All prescription items dispensed (≈ 40 million/year) and basic demographic information is recorded for each individual on the scheme. Drugs are coded according to the WHO Anatomical Therapeutic Chemical (ATC) classification.

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National Cancer Registry Ireland-Health Services Executive Primary Care Reimbursement Services (NCRI-PCRS) linked dataset The PCRS-GMS master file is linked to registered cancers using probabilistic matching techniques implemented in Automatch software. Agreement between records is determined based on a threshold below which manual matching is conducted. This linked file is then matched to the prescription datasets to extract information on all prescriptions issued to each individual diagnosed with invasive ovarian cancer (ICD10 C56) during 2007-2010. This matching was completed in Aug 2013 and provides 1609 incident invasive ovarian cancers diagnosed between 2001-2010 (310 cases per annum of which 52% are active in the GMS scheme at diagnosis). For this cohort there is data on all prescriptions 2000-2011 and registry follow-up is complete to 31/12/2011.

6.1.2. Dataset 2- Northern Ireland Northern Ireland Cancer Registry (NICR) The Northern Ireland Cancer Registry (NICR) has registered all incident cancers diagnosed within the province since 1993 (http://www.qub.ac.uk/nicr). Data routinely collected includes date of cancer diagnosis, age at diagnosis, cancer site, histological type and tumour grade. Cancer stage is available for approximately 80% of registered cases. NICR regularly links to the NI General Registrar Office to provide date and cause of death for all registered cases. Follow-up of cancer cases is very complete as out-migration from Northern Ireland is low.

Enhanced Prescribing Database (EPD) In NI, since 2010, all prescription medications were dispensed free of charge to the entire population (1.8 million people), irrespective of age or means. In 2008 an Enhanced Prescribing Database (EPD) was established to record all medications prescribed and dispensed within NI primary care. Approximately 90% of all dispensed medications are captured and maintained in this database (for a few GP practices, data capture is not complete).

Linked NICR-EPD dataset All invasive ovarian cancer cases (ICD 10 C56) diagnosed within NI since mid-2009 will be identified within NICR. A dataset containing all relevant diagnostic, staging, treatment and recurrence data will be constructed for these patients. Data on surgical, chemotherapy and radiotherapy treatments along with information on cancer recurrences will be abstracted by a trained Tumour Verification Officer. This data is available in the Clinical Oncology Information System (COIS), which captures both inpatient and outpatient data from oncology units within NI. Information on occurrence and cause of death will be obtained from the NI General Register Office (GRO). This data will be linked to EPD to provide information on all prescriptions dispensed within primary care to these patients since mid-2008. It is estimated that the ovarian cancer linked dataset will include approximately 400 cancers diagnosed in 2009 and 2010 (they will have complete follow-up until at least 31/12/2011).

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6.1.3. Dataset 3- United Kingdom The Clinical Practice Research Database (CPRD), and the subset linked to the National Cancer Data Repository (CPRD-NCDR) The UK CPRD is the world’s largest computerised database of anonymised longitudinal patient records. It includes 500 general practices comprising a 6% representative sample of the UK population. Data include demographics, clinical diagnoses, hospital referral and discharge information and details of all prescriptions issued. CPRD data can be linked with data from English cancer registries contributing to the National Cancer Data Repository (NCDR). For around 50% of CPRD practices, this linkage can provide confirmation of cancer diagnoses, and dates and causes of death, as well as information on stage of disease at diagnosis and some treatment data (e.g. receipt of chemotherapy, surgery).

6.2. Data Harmonisation The three registry/prescription datasets will be harmonised on core data items. These items will be processed to be coded in a similar fashion before combining.

6.3. Study population The source population will consist of women with a diagnosis of invasive ovarian cancer. All analyses phases (described later) will include cases eligible for at least one year follow-up post-diagnosis.

Phase 1/2  Women diagnosed in NCRI-PCRS & CPRD between January 2001-December 2010  Women diagnosed in NICR-EPD between January 2009-December 2010

Phase 3  Women diagnosed NCRI-PCRS & NICR-EPD between January 2008-December 2010 with  non-metastatic (stage I-III) ovarian cancer  where recurrence status is available

Exclusions Women will be excluded if:

 They had another cancer prior to the ovarian diagnosis (not non-melanoma skin cancer) or  There is less than 12 months prescription data prior to diagnosis available

6.4. Study design Study Design and General Approach Three separate studies will be conducted, relating to each of the drugs of interest (i.e. Study 1, statins; Study 2, beta-blockers; Study 3, NSAIDs). Each study will employ the same approach and will be undertaken in three phases, as follows:

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 Phase 1 will use a retrospective cohort approach to investigate associations between exposure to the drug of interest and mortality (disease specific survival & overall survival) in women with incident ovarian cancer.  Phase 2 will investigate associations between pre-diagnostic exposure to the drug of interest and tumour characteristics at diagnosis. This phase will use a nested case-control approach, with the comparison of “cases” and “controls” undertaken within a defined cohort of women with incident ovarian cancer.  Phase 3 will use a retrospective cohort approach to investigate associations between exposure to the drug of interest and disease recurrence (disease free survival) in women with stage I-III incident ovarian cancer. Pooling data sources In this project, data will be extracted from the available databases and coded using a common protocol. Prior to analysis, we will compare data and conduct data harmonisation.

 Individual-level patient data will be pooled where data is considered sufficiently comparable (for example, when the ovarian cancer cases originate from population-based-cancer registries and similar data items are available).

 Where pooling of individual-level data is considered inappropriate, a replication approach will be used. That is, the initial analysis will be done in one dataset, with replication in others; in this instance a common approach to the statistical analysis will be used throughout and, where appropriate, point estimates will be pooled using a meta-analysis approach.

6.5. Analysis Phase 1 Associations between drug exposure and mortality (disease specific & overall survival).

Study design A retrospective cohort design will be used to evaluate associations between exposure to each of the study drugs of interest and mortality (disease specific and overall survival) in women with ovarian cancer. Analysis Cox proportional hazards models will be used to compute unadjusted and adjusted hazard ratios (HR) and 95% confidence intervals for each exposure of interest. The primary analysis will be an as- treated analysis, comparing exposed versus unexposed, with exposure defined at the time of diagnosis (i.e. pre-diagnosis exposure). The secondary analysis will consider exposure (including post-diagnostic period) as a time-varying dichotomous (ever/never) covariate. Exposure will be lagged by 6 months to impose a reasonable induction period for an effect on recurrence and guard against the possibility that imminent recurrences affected prescribing patterns. In order to evaluate the robustness of results. Subgroup analyses will be performed for statin exposure (hydrophilic, lipophilic) and beta-blocker exposure (β1 selective, β1/β2 non-selective). Models will be adjusted for prognostic and confounding variables identified a priori and empirically, including stage and co- prescriptions of other drugs of interest. Interactions will be tested between study drug exposures and drug exposure and treatment received (statins and beta-blockers only). Stratification will be used to determine whether results vary across key strata.

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Sensitivity analyses will be conducted to determine the effect of varying the exposure lagging time in the as-treated analysis. To determine robustness of results to the assumptions required in censoring non-cancer deaths, those who died from other causes will be accounted for using competing risk models. New approaches to the analysis of pharmacoepidemiology data may be explored as appropriate (e.g. adjusted risks and risk differences; relative time to event; and marginal structural Cox models). Conditional survival beyond 6/12 months post diagnosis will be analysed to enable incorporation of treatment into models.

6.6. Analysis Phase 2 Associations between pre-diagnostic drug exposure and tumour characteristics. Study design A nested case-control design will be used to evaluate associations between tumour characteristics at diagnosis and exposure to each of the drugs of interest (i.e. statins, beta-blockers, NSAIDS; each considered separately) in women with incident ovarian cancer. Primary analysis  Tumour stage (FIGO 1-3 vs. 4) Secondary analyses  tumour grade (1,2 vs. 3,4)  morphology (Serous vs. other) Analysis Cases and controls (matched as described below) will be compared using conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals for exposure to the study drug of interest. Analyses will be adjusted for demographic variables. Interactions between study drug exposures will be tested for, to determine any possible treatment synergies. Sensitivity analyses will be conducted to determine the effect of increasing the pre-diagnostic exposure period from one year to two and three years.  Pre-diagnostics exposure period o 2 years o 3 years Significant pelvic or abdominal pain has been reported to precede ovarian cancer diagnosis by up to five months49. There is the potential that these pre-diagnostic symptoms may be associated with increased NSAID exposure, although this is unlikely to be associated with significant confounding as these symptoms are not strong predictors of tumour stage at diagnosis49. Sensitivity analyses will also be conducted to identify the presence of protopathic bias by excluding de novo NSAID prescriptions in the 6 months prior to ovarian cancer diagnosis.  Exposure lag o 6 months Further sensitivity analyses will be undertaken to explore the effect of different categorisations of the ovarian cancers where stage is unknown.

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6.7. Analysis Phase 3 Associations between exposure to the drug of interest and disease recurrence (disease free survival)

Study design A retrospective cohort design will be used to evaluate associations between exposure to each of the drugs of interest and recurrence/disease free survival in women with ovarian cancer. Analysis Cox proportional hazards models will be used to estimate unadjusted and adjusted HRs, with 95% confidence intervals, for each exposure of interest. As for Phase 1, the primary analysis will be an as- treated analysis. Secondary analyses – as in Phase 1, models will be adjusted for relevant prognostic and confounding variables identified a priori and empirically. A subgroup analysis will be performed as per Phase 1. Interactions between exposure to drugs of interest and treatment receipt (statins, beta-blockers) will be estimated. Sensitivity analyses will be as for Phase 1, with the addition of an analysis exploring the effect of excluding women with missing stage information.

7. Outcome definitions Phase 1 - Survival The primary outcome will be:  Disease specific survival (time from diagnosis to death from ovarian cancer (based on SEER definition50 using attribution on death certificate)). Women alive at the end of follow-up (31/12/2011) will be censored. Non-cancer deaths will be censored but all results will be interpreted in the context of potential interactions with other causes. The secondary outcome will be:  Overall survival (time from diagnosis to death from any cause). Women alive at the end of follow-up (31/12/2011) will be censored

Phase 2 - Tumour stage Cases and controls will be identified from within the source population.

 Cases: In the analysis of stage, women with a diagnosis of N1 or M1 ovarian cancer (distant nodal or metastatic involvement) will be designated cases.

 Controls: Those with a diagnosis of N0&M0 ovarian cancer (no distant nodal or metastatic involvement) will be designated controls.

 It is expected that similar number of cases and controls will be observed and therefore 1:1 matching will be used

 Matching: 1:1 matching51 of cases and controls will be based on:

o age band at diagnosis (+/- 5 years)

o year of diagnosis

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o country

Phase 3 - Recurrence The primary outcome of this phase will be:

 disease free survival (time from diagnosis to the first of either ovarian cancer recurrence or death from any cause or end of follow-up (31/12/2011)).

Secondary outcomes will include:

 recurrence occurring within six months of adjuvant chemotherapy, a prognostic marker indicative of platinum chemotherapy resistant disease, and

 presence/absence of recurrence adjusted for follow-up time

Recurrence will be estimated in the NCRI-PCRS dataset and recorded from notes in the NICR-EPD databases. Recurrence information is not available as part of the CPRD dataset.

8. Drug definitions Statins Statin exposure is defined based on ATC code as follows: C10AA Exposures will be sub-classified as:  hydrophilic,  lipophilic  or both. Indications: symptomatic cardiovascular disease, prevention of cardiovascular disease events in asymptomatic individuals who are at increased risk, patients over 40 with diabetes mellitus.

Beta-blockers Beta-blocker exposure is defined based on ATC code as follows: C07A, C07B

Exposures will be sub-classified as:

 β1 selective (C07AB, C07BB)

 β1/β2 non-selective (C07AA, C07BA)

 or both (C07A, C07B)

Indications: Angina, myocardial infarction, arrhythmias, heart failure, thyrotoxicosis, anxiety, prophylaxis of migraine, glaucoma.

NSAIDs NSAID exposure is defined based on ATC code as follows:

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 ATC codes M01A (non-aspirin)

 Aspirin (ATC B01AC06 75mg; N02BA01 300mg) + M01BA03 N02BA51 N02BA71 Exposures will be sub-classified as: aspirin, non-aspirin or both.

Indications: Aspirins: mild to moderate pain, pyrexia; anti-platelet

Standard dose: Aspirin: by mouth, 300–900 mg every 4–6 hours when necessary; max. 4 g daily. Low dose 75mg or less.

Definitions of drug exposure Statin, beta-blocker and NSAID exposure at the time of, and subsequent to, ovarian cancer diagnosis will be identified from linked prescribing data using relevant ATC codes.

For each drug, daily period exposure will be determined:

 NCRI. Using pack-size/tablets dispensed and the fact that Ireland uses 28-day prescriptions (except prescription codes 61559 which are 7-day emergency prescriptions obtained from hospitals).

 CPRD. Tablets per days = number of tables by duration of the exposure period. Data available is for prescriptions written (there is no confirmation of dispensing).

 NICR. Exact format of the prescription data is still to be confirmed.

Exposure will be quantified both dichotomously and as continuous variables. The proportion of days within the exposure window with active medication will be estimated. Duration of exposure and dose intensity will be considered as follows.

Exposure % = Number of days with drug exposure / Total number of days

Total Dose = Total dose prescribed during the period

Dose Intensity = Total dose received / exposed days in period

Period is defined as required (e.g. 12 months prior to diagnosis)

Pre-diagnosis/post-diagnosis exposure definitions Primary analysis will focus on pre-diagnosis exposures. As secondary analysis, post-diagnosis exposure will be evaluated using the time-varying approach.

9. Covariates Covariate information will be available from each patient’s linked prescribing and cancer registration records. Models will be adjusted for the following covariates:

 demographic (age, year of diagnosis, country, county, smoking, marital status)

 tumour characteristics (T/N/M stage, grade, behaviour morphology)

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 comorbidity as defined by the RxRisk model52

 (for survival endpoints) treatment received at the time of diagnosis

10. Sample size considerations Phase 1 It is estimated that 3,708 patients with a diagnosis of ovarian cancer during January 2001 and December 2010 will be available (NCRI-PCRS: 10 years*337 cases*0.56 GMS eligible = 1,890; CPRD- NCRD: 1,500; NICR-EPD=318). Based on a mortality rate of 34% at 1 year (based on NCRI data), 1,260 deaths would be expected. Assuming 8.2% exposure (aspirin), a two-sided significance level of 5% will have >80% power to detect a HR=0.70 for exposure preventing death.

Phase 2 Pooling the three data sources will provide approximately 2960 ovarian cancers. Of these, it is estimated that 40% will be potential controls (N0&M0) and the remainder potential cases. If cases and controls are matched 1:1 (i.e. n=1184 controls vs. 1184 cases) and assuming 8.2% exposure (aspirin prevalence in women in Ireland) and a 2-sided significance level of 5%, this sample will have >80% power to detect an OR=0.53 for exposure being protective for early stage disease (N0&M0). .

Phase 3 It is estimated that 1,043 women with a diagnosis of stage I-III ovarian cancer diagnosed January 2008-December 2010 will be available (NCRI-PCRS: 3 years*337 cases*0.56 GMS eligible= 556; NICR- EPD: 3 years*159 cases=477). Assuming that recurrence data is available for 70% of these, this will result in an evaluable population of 730 patients. Based on a recurrence rate of 55% at 1 year (35% stage I/II with 25% recurrence and 65% stage III/IV with 70% recurrence), 400 recurrent cases are expected. Assuming 8.2% exposure (aspirin), a two-sided significance level of 5% this number of recurrences will have >80% power to detect a HR=0.53 for exposure being protective against recurrent disease.

11. Blinding All analyses will be carried out in the following order to eliminate any potential chance of assessment bias:

1. Analysis planning 2. Data collection

3. Endpoint determination for all women, 4. Analysis.

Any endpoint evaluation requiring subjective evaluation will be carried out or validated by an independent investigator.

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12. Confounding Measured confounding Measured potential confounding variables (e.g. patient demographics, tumour characteristics, treatments, co-prescriptions etc.) will be controlled using standard techniques (e.g. matching, stratification, adjustment) as described above. Where necessary, propensity scores based on multiple potential confounders will be constructed to permit the tighter control of confounding53.

Evaluation and control of unmeasured confounding A variety of methods are available for the evaluation and control of unmeasured confounding in pharmacoepidemiology studies54,55. We will conduct sensitivity analyses based on an array of informed assumptions as outlined above.

Assessing level of confounding adjustment achieved Indications for drugs of interested will be included as potential confounders. Recognising that underlying conditions can change, time-varying models will be used to evaluate the potential effects of these.

Sensitivity analyses for residual confounding Analyses to identify the strength of residual confounding required to explain the observed association, will be evaluated54. Adjustment of associations considering additional information from external, population-based, datasets will be considered.

13. Missing data In the primary analysis, no imputation of missing data will be performed. Women with missing covariate data will be classified separately in primary analysis. If the degree of missing data exceeds 25% we will perform sensitivity analyses, considering imputation to evaluate the robustness of results.

14. Predictive modelling No predictive modelling is involved in this project.

15. Interim analysis No interim analyses of the data are planned.

16. Multiplicity There are a number of treatment comparisons to be conducted in this project. It is therefore possible that type-1 errors may be observed. Results of all models will be reported to enable independent post-hoc adjustment.

Consistency of estimates within country subgroups will increase confidence in significant results.

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17. Software All analyses will be performed using SAS version 9.3 (SAS Institute, Cary, NC, USA). Matching of cases and controls within the cohort will be performed using the GMatch macro based on the greedy matching algorithm51.

18. Project Timeline Date when funding contract was signed: 01/05/2013 Start date of data collection: 31/10/2013 Start date of data analysis: 31/12/2014 Date of final study report: 31/12/2015

19. Reporting All results will be reported according to the STROBE statements56 for observational studies. In general, results will be presented with 95% confidence intervals

Publication of all analyses will be submitted to scientific journals within 1 year of the results being finalised. All publications relating to the work will reference the project acronym (EPOC) to ensure transparency regarding multiple endpoints/exposures.

This protocol will be registered with the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP).

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Appendix 1. Analysis tables

Table 1 - Baseline Characteristics

Characteristic Statin Beta Blocker NSAID User Nonuser User Nonuser User Nonuser Registry Ireland NI England Year of Diagnosis 2001-2005 2006-2010 Age Mean Age <50 50-70 70+ Smoking Current Never Previous Unknown Marital Status Single Married Divorced/Separated Widowed Unknown Deprivation Level Low Medium High T/N/M 1 FIGO Stage 2 3 4 Unknown Grade 1 2 3 Unknown Comorbidities Gastric reflux / Peptic ulcer Steroid responsive disease Pain Allergies Inflammation/pain Anxiety Hypertension Anti-platelet therapy Medication Aspirin History Other NSAID Statin Beta Blocker HRT Etc. Baseline tables will be repeated split by country (to check populations) and for subgroups as outlined in the analysis i.e. within drug: by type, dose, and duration.

Table 2 - Treatment at diagnosis

Characteristic Statin Beta Blocker NSAID User Nonuser User Nonuser User Nonuser Initial treatment Surgery Chemotherapy Radiation Surgery None Oophorectomy Hysterectomy Salpingo-oophorectomy Salpingo-oophorectomy + hysterectomy Other Chemotherapy No Yes Radiation No therapy Yes

Table 3 - Prescription history

Characteristic Statin Beta Blocker NSAID User Nonuser User Nonuser User Nonuser Within 12months # Prescriptions prior to diagnosis Duration Exposure (%) Daily dose High dose (%) Within 6months # Prescriptions prior to diagnosis* Duration Exposure (%) Daily dose High dose (%) Within 24months # Prescriptions prior to diagnosis* Duration Exposure (%) Daily dose High dose (%) Post-diagnostic # Prescriptions exposure Time to Start Duration Exposure (%) Daily dose High dose (%) *Sensitivity analyses

Table 4 - Survival estimates

Characteristic Unadjusted Adjusted HR 95% CI HR 95% CI Drug None exposure* Pre-diagnostic use Registry Ireland NI England Year of 2001-2005 Diagnosis 2006-2010 Age <50 50-70 70+ Smoking Current Never Previous Unknown Marital Status Single Married Divorced/Separated Widowed Unknown T/N/M Stage 1-2 3-4 Unknown Grade 1-2 3-4 Unknown Morphology Serous Endometrioid Mucinous Clear cell Other/Unknown Comorbidities 0-1 2-4 5 or more Surgery None at diagnosis Surgery at diagnosis *Statin/ Beta Blocker/ NSAID

Table 5 - Effect of treatment on Tumour stage

Characteristic** Statin Beta Blocker NSAID OR 95% CI OR 95% CI OR 95% CI Stage 1-3 4 Grade* 1-2 3-4 Morphology* Serous Other/Unknown * Secondary endpoints **Repeated adjusted for covariates

Appendix 2. Drugs of Interest The following tables list the drugs of interest in this project ATC codes will be converted to BNF and SNOMED codes for extraction of data from CPRD and NI databases.

Statins

Drug / Class Group ATC simvastatin statin-lipo C10AA01 lovastatin statin-lipo C10AA02 pravastatin statin-hydro C10AA03 fluvastatin statin-lipo C10AA04 atorvastatin statin-hydro C10AA05 cerivastatin statin-lipo C10AA06 rosuvastatin statin-hydro C10AA07 statin combination (simva & lova) statin-lipo C10BA simvastatin & aspirin statin-lipo C10BX01 pravastatin & aspirin statin-hydro C10BX02 atorvastatin & aspirin statin-hydro C10BX03

NSAIDs

Drug / Class Group ATC ASPIRIN ANTITHROMBOTIC aspirin B01AC06 ASPIRIN & CORTICOSTEROID aspirin M01BA03 ASPIRIN ANALGESIC aspirin N02BA01 ASPIRIN & COMBINATION aspirin N02BA51 ASPIRIN & COMBINATION PSYCHOLEPTIC aspirin N02BA71 simvastatin & aspirin aspirin C10BX01 pravastatin & aspirin aspirin C10BX02 atorvastatin & aspirin aspirin C10BX03 butylpyrazolidines nsaid M01AA acetic acid derivatives and related nsaid substances M01AB oxicams nsaid M01AC propionic acid derivatives nsaid M01AE fenamates nsaid M01AG coxibs nsaid M01AH other antiinflammatory and nsaid antirheumatic agents, non-steroids M01AX

Beta Blockers

Drug / Class Group ATC alprenolol non-selective C07AA01 oxprenolol non-selective C07AA02 pindolol non-selective C07AA03 propranolol non-selective C07AA05

timolol non-selective C07AA06 sotalol non-selective C07AA07 nadolol non-selective C07AA12 mepindolol non-selective C07AA14 carteolol non-selective C07AA15 tertatolol non-selective C07AA16 bopindolol non-selective C07AA17 bupranolol non-selective C07AA19 penbutolol non-selective C07AA23 cloranolol non-selective C07AA27 sotalol, combinations non-selective C07AA57 practolol selective C07AB01 metoprolol selective C07AB02 atenolol selective C07AB03 acebutolol selective C07AB04 betaxolol selective C07AB05 bevantolol selective C07AB06 bisoprolol selective C07AB07 celiprolol selective C07AB08 esmolol selective C07AB09 epanolol selective C07AB10 s-atenolol selective C07AB11 nebivolol selective C07AB12 talinolol selective C07AB13 metoprolol, combinations selective C07AB52 bisoprolol, combinations selective C07AB57 labetalol alpha+beta C07AG01 carvedilol alpha+beta C07AG02 oxprenolol and thiazides non-selective C07BA02 propranolol and thiazides non-selective C07BA05 timolol and thiazides non-selective C07BA06 sotalol and thiazides non-selective C07BA07 nadolol and thiazides non-selective C07BA12 metipranolol and thiazides, combinations non-selective C07BA68 metoprolol and thiazides selective C07BB02 atenolol and thiazides selective C07BB03 acebutolol and thiazides selective C07BB04 bevantolol and thiazides selective C07BB06 bisoprolol and thiazides selective C07BB07 nebivolol and thiazides selective C07BB12 metoprolol and thiazides, combinations selective C07BB52 labetalol and thiazides alpha+beta C07BG01