This document outlines the rationale, objectives, hypotheses, study design, and planned analyses for investigating the potential association between calcium channel blocker (CCB) use and cancer risk. The study will be conducted using multiple epidemiological databases from the UK, Denmark, Netherlands, Spain, and Germany. The specific objectives are to conduct descriptive studies, a population-based cohort study, and nested case-control analyses to compare cancer outcomes between CCB users and non-users. The primary outcome of interest is risk of all cancers combined, and secondary outcomes include breast, prostate, and colon cancers analyzed separately.

2. Rationale and Background
CCB and cancer risk is one study among a larger study
to, develop, test and disseminate methodological
standards for the design, conduct and analysis of,
Phamacoepidemiological (PE) studies applicable to
different safety issues and using different data sources
by (Pharmacoepidemiological Research on Outcomes of
Therapeutics by a European ConsorTium- PROTECT).

3. Selection criteria
 Adverse events that caused regulatory
decisions
Public health impact (seriousness of the
event, prevalence of drug exposure,
etiologic fraction)
Feasibility
 Range of relevant methodological issues

4. The five pairs
 Antidepressants (incl.
Benzodiazepines) ‐ Hip Fracture
 Antibiotics ‐ Acute liver injury
 Beta2 Agonists ‐ Myocardial infarction
 Antiepileptics ‐ Suicide
 Calcium Channel Blockers ‐ Cancer

5. Rationale and background….
Emphasis will be on the
methodological aspects of the
studies in this protocol and not
on the clinical consequences of
the association under
investigation.

6. Primary Objective
Investigating the possible
association between uses of CCBs
and risk of all forms of cancer
combined, among adult patients (18
to 79 years of age during the study
period, 1 January 1996 and 31
December 2009)

7. Specific Objectives of PROTECT
 Assess the feasibility to ascertain key adverse events
(AEs) with sufficient validity using standard definitional
algorithms in data sources used for PE studies;
 Develop study protocols for pilot PE studies of
associations between selected drugs and key adverse
events (AEs);
 Provide guidelines on how to identify and assess the
validity and use of national drug utilisation data to
estimate public health impact of adverse events (AEs);
 Identify and optimise methods to control for confounding
in simulation studies;

8. Specific objectives…
 Investigate discrepancies in results from pilot PE studies
between databases, and explore differences with other
data sources and paradigms as appropriate;
 Identify and evaluate statistical methods for conducting
multi-database studies;
 Evaluate identified signal from signal detection detection
strategies applied by WP3 in electronic healthcare
databases;
 Test guidelines and standards in validation studies
performed with WP6, regarding different safety endpoints
in the same databases, the same safety endpoints in
different databases, and same safety endpoints in same
databases among patients with different indications.

9. Hypothesis
CCBs modify the risk of cancer (all forms of cancer
combined and various groups of cancers).
 It has been proposed that CCBs may interfere with
apoptosis, leading to an increased potential for abnormal
cell proliferation and tumor growth (Daling JR. Calcium channel
blockers and cancer: is an association biologically plausible? Am J Hypertens
1996;9:713‐714.)
 CCBs have also been shown to inhibit apoptosis in certain
non‐transformed cell lines but promote apoptosis in other
non‐transformed and transformed cell lines. The results from
non‐human genotoxicity studies have shown no link
between CCB use and tumor development (Mason RP. Effects of
calcium channel blockers on cellular apoptosis: implications for carcinogenic
potential. Cancer 1999;85:2093‐2102).

10. Data sources for investigating hypothesis
Cohort and Case control studies will be
conducted from data from the following data
sources
 UK
• General Practice Research Database (GPRD)
• The Health Improvement Network (THIN)
 Danish national database
Descriptive studies will be conducted in the
following
 Mondriaan (The Netherlands)
 BIFAP (Spain)
 Bavarian Claims Database (Germany)

11. Study design
Study designs
• Descriptive
• Cohort
• Population based cohort
• Nested case control

12. General Practice Research Database (United
Kingdom)
 largest ongoing health care database available in
the UK since 1987
 more than 4.8 million active research quality patient
data from more than 590 general practices in the UK
 The validity of a wide range of drug exposure data is
routinely tested.
 Cancer registration in England is conducted by eight
regional registries, which also submit a standard
dataset of information to the Office for National
Statistics (ONS), for collation of national cancer
incidence data.

13. The Health Improvement Network
(United Kingdom)
 collaboration between two companies; In Practice Systems
Ltd. (INPS), developer of Vision software used by general
practices in the UK, and EPIC, provider of access to data for
use in medical research.
 collected during routine medical practice and regularly
delivered to a central database.
 database currently contains the electronic medical records of
almost 8 million patients (more than 3 million active patients)
collected from over 386 general practices in the UK.
 Patient data are arranged in five standardized files per
practice: patient, medical, therapy, additional health data and
a file to enable data linkage containing postcodes

14. Danish National Database (Denmark)
 The Danish national databases can provide
computerized records of: sociodemographic factors,
visits to general practitioners and to hospital, the
dispensing of medication by pharmacies, and
mortality for the Danish population (5.5 million
people).

15. Mondriaan (The Netherlands)
 The Dutch Mondriaan project is a private‐public
collaboration
 Under the umbrella of Mondriaan, the
participating databases currently include: the
Dutch General Practitioner (LINH) database, The
Almere Health Care (ZGA) database, The
General Practitioners of Utrecht (HNU) database
and The Leidsche Rijn Julius Health Centre
(LRJG) database.
 The four databases within Mondriaan have
different starting dates and scope of data.

16. BIFAP (Spain)
 A computerized database of medical records of
Primary Care)
 The project has started in 2003 having the goal to
achieve a pool of collaborators in the range of 1000
general practitioners and pediatricians. Currently,
1190 physicians (995 GPs and 195 pediatricians)
from 9 different autonomous communities in Spain
collaborate with BIFAP and send their data to BIFAP
every 6 months.

17. Bavarian Claims Database (Germany)
 This German database includes a
population‐based data on diagnosis
and medical services, covering 10.5
million people.
 It is a pharmacy (claims) database
linked to outpatient treatment data
through general practitioners and
specialists.

18. Data collection
Inclusion criteria
 CCB first‐time users and non‐users from 1 January
1996 to 31 December 2009, aged 18 to 79 years.
 Concerning GPRD and THIN/BIFAP patients are
included if they have Primary Care Practice history
of at least two years and at least one year’s
computerized GPRD/GP prescription history.
Exclusion criteria
 Patients with any cancer recorded in the GPRD or
The Danish National registration of patients prior to
cohort entry will also be excluded from the analysis
 Patients less than 18 years and over 70 years

19. Definition of exposure
 Exposure in the cohort study and the nested
case‐control study will be defined as cumulative use
of CCB. The cumulated exposure will be derived
from the total amount of DDD, the number of
prescriptions and the period the prescriptions covers
(package size).
 The descriptive study will require one prescription to
define exposure.

20. Definition of outcomes
Outcomes will include all forms of
cancer combined, as well as
breast cancer in women, prostate
cancer and colon cancer analyzed
separately.

21. Descriptive study
 The period prevalence and point prevalence of CCB use
(defined as ≥ 1 prescription for a CCB) will be described
by age group (0‐9,10‐19,20‐29, 30‐39, 40‐49, 50‐59,
60‐79) and sex or by indication for the period 2000 to
2009 (ten years)
 Period prevalence and point prevalence will also be
presented for the years 2000, 2004 and 2009.
 Period prevalence of patients having ever used a CCB
will be presented by number of prescriptions (1, 2‐4,
5‐11, 12‐23, >23) for 2000 to 2009.

22. Descriptive study…
 The period prevalence of cancer will be presented
by age group (10 year categories) and sex for the
period from Jan 1999 to Dec 2001 inclusive.
 The yearly incidence of cancer will be presented by
age group (10 year categories) and sex from 2000 to
2009.
 Incidence and prevalence will be presented for all
forms of cancer combined, as well as for breast
cancer.
 Prostate cancer and colon cancer will be described
separately.

23. Databases where descriptive studies will be
held

24. Cohort study
 CCB first‐time users and CCBs non‐users between 1
January 1996 and 31 December 2009.
 Index date will be the date of the first registration of
cancer
 CCB non‐users will be drawn as a random sample
matched on age and gender.
 Members of each cohort will be followed from entry to
the study until the earliest of: diagnosis date of study
outcome (cancer), date of disenrollment from database,
reaching 80 years of age, date of death, or end of study
period.

25. Cohort study….
 The cohort study will employ various methods to assess
the risk of cancer outcomes by CCB user status. CCB
users will be compared with CCB non‐users in terms of
their incidence of cancer outcomes. Initial analysis will be
made comparing cancer occurrences from a period of six
months to one year of cohort entry.
 Also analysis for duration and dose of CCB treatment in
relation to cancer outcomes as well as the cumulative
number of prescriptions will be done.
 Comparison of cancer outcomes occurring six months –
1 year, 1‐4 years and >5 years following initiation of CCB
therapy will be done. This will take into account the
lag‐time for clinical onset of cancer and the diagnose
delay.

26. Cohort study
 Preliminary data analysis will employ standard
methods such as non‐parametric univariate and
bivariate analyses. Further analyses will include Cox
proportional hazard models with time dependent
covariates.
 the relative risk of cancer

27. Population based Cohort study
 The population‐based study will describe
prevalence of CCB use and prevalence and
incidence of cancer outcomes within the study
population defined above, over the study period.
 In order to be more representative of the population,
all patients with a history of cancer before the start
of the study will be included in the analysis.
 Cox proportional hazard analysis with time
dependent covariates will be used to calculate RR
and 95 % Cl to compare cases and controls.

28. Nested case‐control study
 A nested case control study design identifying cases of cancer
outcomes between 1January 1996 and 31 December 2009, and
matching them on a ratio of 1:4 with controls that had not yet
developed any form of cancer before the date of cancer diagnosis of
the case.
 Cases will be matched to controls on same age (years) and gender.
 Analysis will be made regarding length of use as well as number of
prescriptions. Cancer outcome will be defined as six months – 1
year, 1‐4 years and >5 years.
 Conditional logistic regression analyses will be used to calculate
odds ratios (ORs) and 95% CIs to compare cases and controls in
terms of exposure to CCBs (<1‐year, 1‐4 years and >5 years before
the date of cancer diagnosis of the case) and duration of CCB use,
as well as other potential risk factors for cancer outcomes

29. Covariates in regression analysis
 Key variables in the Cox proportional hazards
regression and conditional logistic regression
analyses will include antihypertensive treatment
other than CCB, cardiovascular medication other
than antihypertensives,
 co‐prescription data (e.g. post‐menopausal hormone
treatment, NSAID, immunosuppressants)
 the data specification document for description of
cardiovascular medication), Body Mass Index (BMI),
smoking, alcohol consumption and
 Comorbidities such as pulmonary disease

30. Other statistical methods
Propensity score
modeling
Instrumental variable
analysis

31. Limitations of study designs, Datasources and
analytical
Methods
 The most important (potential) limitation to this study relates to the
challenge of obtaining quality information on important confounder data
(such as smoking, BMI, and alcohol consumption).
 Pharmaco‐epidemiological studies involving cancer as an outcome
always have the potential to be confounded by smoking in particular.
 breast cancer, analysis will be restricted to women; it is unlikely that we
will be able to obtain data on age at menopause and information on oral
contraceptive use may be limited.
 For colon cancer, we are unlikely to obtain information concerning
dietary habits (relating to the consumption of fiber, fruit, vegetables and
red meat) or physical activity.

32. Anticipated results of studies
 Creation of a European database network and further
development of methodological standards for the conduct of
(multi-) national PE studies.
 Methodological standards will be included when appropriate
in the EMA (European Medicines Agency)-based
ENCePP(European Network of Centres for
pharmacoepidemiology and Pharmacovigilance) guidance
on methodological standards
 Increasing methodological standards and registration of
study protocols to decrease discrepancies in results from
these studies and increase transparency and thereby
increasing the usefulness and reliability of these studies for
benefit-risk assessment and decision- making of marketed
drugs in Europe and beyond.

33. Thank you