Managing Confounding in Observational Databases

7/31/2019 Managing Confounding in Observational Databases

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Managing Confounding in

Observational Databases

Annie Burden

Senior Statistician

RIRL


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Annie Burden

o Senior Statistician

Vicky Thomas

o

Lead Statisticiano SAS

Francesca Barion

o Lead Health Economist

o STATA

Muzammil Ali

o Statistician

o SPSS

RiRL Statistics TeamStatistics & Health Economics


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RCTso Subjects are Randomised

o Variation in baseline characteristics should be random

across treatment groups

Observational Studieso We get what we get!

o Variation in the baseline characteristics between

treatment groups may be:

Systematic

Random

Introduction


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Any claim coming from anobservational study is most

likely to be wrong.S. Stanley Young & Alan Karr

Significance Magazine September 2011 Volume 8 Issue 3

The mis-conception


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Multiple Testingo Keep asking questions of the data & something will

eventually come up positive;

o E.g. Women eating breakfast cereals leads to more

boy babies. Bias

o Hidden Confounders

Multiple Models

o Limitless possible combinations of confounders.

The mis-conception (continued)


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Multiple Testingo Limited number of Primary Outcomes;

o Outcomes set a priori;

o Interpretation is important...

Clinician Input (Female diet cannot influence babysgender)

Bias

o Try to include all potential confounders.

Multiple Modelso Very careful selection of:

Potential confounders; and

Covariates to include in the final model.

The truth


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A confounding variable is an extraneous variable in astatistical model that correlates (positively or negatively)with both the dependent variable and the independentvariable.

... Confounding is a particular challenge.

Wikipedia

Confounding where the estimated association is NOTthe same as the true causal effect...

Thomas Lumley 2005

Throwing things into disorder; mixing up; confusing.The Concise Oxford Dictionary

Confounding - Definitions


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Confounding


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Baseline / Characterisation Variables related to theOutcome (Dependent) Variable

o Predictive Variables;

Baseline / Characterisation Variables related to the

Treatment (Independent Variable)o Baseline Differences;

Check for relationships between the potential

confounders to avoid double-accounting

o Linear relationships through correlation coefficients;

o Non-linear relationships via modelling / plots.

Potential Confounders


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Treatment GroupP value

Control Treatment Total

Number of

Exacerbations (ATS

Definition)

N (% non-missing) 194 (100.0) 388 (100.0) 582 (100.0)

0.949Mean (SD) 0.32 (0.60) 0.32 (0.60) 0.32 (0.60)

Median (IQR) 0 (0, 1) 0 (0, 1) 0 (0, 1)

Total acute Oral

steroids

N (% non-missing) 194 (100.0) 388 (100.0) 582 (100.0)

0.986Mean (SD) 0.12 (0.35) 0.12 (0.33) 0.12 (0.33)

Median (IQR) 0 (0, 0) 0 (0, 0) 0 (0, 0)

LRTI Consultations

resulting in

prescription for

Antibiotics

N (% non-missing) 194 (100.0) 388 (100.0) 582 (100.0)

0.781Mean (SD) 0.24 (0.54) 0.22 (0.54) 0.23 (0.54)

Median (IQR) 0 (0, 0) 0 (0, 0) 0 (0, 0)

Baseline Differences


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Gender Height Weight BMI (categorised)

Gender

Height 0.54

Weight 0.34 0.55

BMI (categorised) 0.78

Linear Relationships

Spearmans Correlation Coefficients


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Non-linear Relationships

Example Output from SPSS Statistics19

www.spss.com/uk/software/statistics/
http://www.spss.com/uk/software/statistics/http://www.spss.com/uk/software/statistics/


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2 main options: Perform comparisons only between observations

that have the same value of the confounder:

o

Stratify data by confounder(s)oAdjust for a confounder(s) in regression (an

approximation to stratification that requires less data).

Perform comparisons only between groups that

have the same distribution of the confounder:o Match on the confounder(s)

Removing Confounding


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Score Statistics For Type 3 GEE Analysis

Source DF Chi-Square Pr > ChiSq

Treatment 1 9.8 0.0017

Age 1 3.84 0.0501

Acute_Oral_Steroids 2 20.01


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To minimise baseline differences between treatmentgroups

Match on:

o Demographic variables (Age, Gender);

o Study-appropriate measures of baseline diseaseseverity, for example:

Baseline Exacerbations

Baseline Consultations

Controller Medication

Reliever Medication.

Matching Ratio (e.g.1:1, 1:2, 1:3) to maximise powerof statistical tests

Matching


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Minimal adjustment for residual confounderso Parsimony is Good!

Conditional Models

o Conditional Logistic Regression

o Conditional Poisson Regressiono Conditional Ordinal Logistic Regression

Matching aims to minimise differences between

treatment groups at baseline BUT.... Need to consider whether matched cohorts are then

representative of the full populations.

Matching


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Another option but not as readily understood byreviewers etc.

Using covariates predictive of outcome, calculatePropensity Score (using Logistic Regression):

= P(Treatment | Covariates) Match on Propensity Scores

Advantages

o Easily Includes Multiple Covariates

Disadvantageso Does not necessarily match clinically similar patients

Propensity Scoring


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Asthma ControlTreatment Group

TotalControl Active

Controlled n (%) 267 (70.4) 540 (71.2) 807 (71.0)

Uncontrolled n (%) 112 (29.6) 218 (28.8) 330 (29.0)

Total n (%) 379 (100) 758 (100) 1137 (100)

Odds Ratio adjusted for baseline

confounders* (95% CI)1.00

1.24

(0.92, 1.66)

Example ResultsASTHMA CONTROL

Defined as:

Controlled: the absence of the following during the one-year

outcome period:

Asthma-related:

oHospital attendance or admission

oA&E attendance, OR

oOut of hours consultations, OR

oOut-patient department attendance

GP consultations for lower respiratory tract infection

Prescriptions for acute courses of oral steroids.

Uncontrolled: all others.

*Adjusted for: Number of exacerbations (clinical

definition) (categorised), Number of non-asthma-

related consultations (categorised) and GERD diagnosis

&/or therapy (YES/NO).

Logistic Regression Model


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Number of Exacerbations

(ATS Definition)

Treatment Group

Total

Control Active

None n (%) 306 (80.7) 613 (80.9) 919 (80.8)

1 n (%) 57 (15.0) 99 (13.1) 156 (13.7)

2+ n (%) 16 (4.2) 46 (6.1) 62 (5.5)

Total (n) 379 (100) 758 (100) 1137 (100)

Rate Ratio adjusted for baseline


1.04

(0.76, 1.44)

Example Results

EXACERBATIONS (ATS DEFINITION)

Defined as the occurrence of:

Asthma-related:oHospital attendance / admissions OR

oA&E attendance

Use of acute oral steroids.

*Adjusted for: GERD diagnosis &/or therapy (YES/NO),

Number of Primary Care Consultations (categorised) and

Number of prescriptions for SABA (categorised).

Poisson Regression Model


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Adherence to ICS Therapy

Treatment Group

TotalControl Active

< 50%n (%) 186 (49.1) 162 (21.4) 348 (30.6)

50-69% n (%) 53 (14.0) 190 (25.1) 243 (21.4)

70-99% n (%) 79 (20.8) 132 (17.4) 211 (18.6)

100% n (%) 61 (16.1) 274 (36.1) 335 (29.5)

Total n (%) 379 (100) 758 (100) 1137 (100)

Odds Ratio adjusted for baseline


2.79

(2.21, 3.52)

Example Results

ADHERENCE TO ICS THERAPY

*Adjusted for: Age, Asthma diagnosis (YES/NO), Number of Primary Care consultations (categorised),

Number of prescriptions for SABA (categorised), spacer device use (YES/NO) and Year of IPD.

Ordinal Regression Model


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Treatment Effect varies with stratum of the 3rd

variableo E.g. Active Drug has different effectiveness relative to

Control depending on smoking status / gender / year

Effect Modification & Confounding can exist separately

or together:o Effect modification without confounding

Adjust & Look at interactions

o Confounding without effect modification

Adjust / match

o Both confounding and effect modification

Adjust / match AND

Look at interactions

Effect Modification


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Effect Modification (cont.)


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Exploration of and familiarisation with Data veryimportant

o Data validation (check for inconsistencies etc.)

o Patient Characterisation & Baseline Differences

between Treatment Groupso Variables Predictive of Outcome

o Relationships between Variables

Interpretation of the results / Clinical Input

o Ensure results are sensible

o Ensure adjustments are sensible

Summary


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Cardiovascular disease risk andpharmalogical smoking cessation

interventions: a retrospective, real-life evaluation

Dr. Erika Sims

Senior Researcher


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RiRL Research Team

Clinical Research Team RiRL Academic

Professor David Price RiRL Chief Investigator Professor of Primary Care

Respiratory Medicine

University of Aberdeen

Dr Erika Sims RiRL Senior Researcher Honorary Research FellowUniversity of East Anglia

Dr Stan Musgrave RiRL Research and Medical

Data Associate

Senior Research Fellow

University of East Anglia

Dr Yolande Cordeaux Medical Researcher Research Fellow; University of

Cambridge


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Tobacco dependence is a chronic, relapsingcondition

Smoking cessation interventions


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In EU in 2000

o 655,000 deaths attributed to tobacco use

o Societal & healthcare costs97.7130.3 billion



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In EU in 2000



Two approaches to smoking cessation:

o Smoking Cessation Advice

o

Pharmacological support



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In EU in 2000



Two approaches to smoking cessation:

o Smoking Cessation Advice

o

Pharmacological support Nicotine replacement therapy - since 1980s

Bupropion (2000) & Varenicline (2006)



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Nicotine Replacement Therapy

Nicotine Replacement Therapyo Substitutes for nicotine

o nasal sprays, inhalers, gum and tablets, transdermal

patches


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Safety of NRT

Gillies et al 2012; Intensive Care Medicine, in presso Retrospective case review

o No evidence of harm associated with NRT, with the ICUmodel actually trending towards benefit.

o n = 423

Ruiz et al 2012; Nicotine & Tobacco Research, in presso Prospective study of COPD patients

o All types of treatments were safe.

o n = 472

Zapawa et al 2011; Addictive Behaviours vol 36: 327o Systematic Review

o Persistent (i.e., long-term) use of NRT does not appearharmful


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Preliminary analyses: NRT vs SC

Adjusted for history of CVD, age and sex:o 1.44 (95% CI: 1.171.79) for CVD


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Adjusted for history of CVD, age and sex:o 1.44 (95% CI: 1.171.79) for CVD

No difference in cardiovascular risk profile :

o body mass index (BMI),

o hyperlipidaemia,o systolic blood pressure,

o hypertension and

o diabetes.


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Adjusted for history of CVD, age and sex:

o 1.44 (95% CI: 1.171.79) for CVD

No difference in cardiovascular risk profile :o body mass index (BMI),

o hyperlipidaemia,

o systolic blood pressure,

o hypertension and

o diabetes.

But limited analysis.

o population not large enough to draw conclusion on mortality.

o Other confounders?


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Patients exposed to NRT and other smokingcessation pharmacotherapies are at a higher risk of

CVD compared with patients undertaking quit

attempts unaided by pharmacotherapies.

Hypothesis


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4 way analysis:

NRT Smoking Cessation

Varenicline AdviceBupropion

Hypothesis

VS


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4 way analysis:

NRT Smoking Cessation

Varenicline AdviceBupropion

Hypothesis

VS


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Study Design

Index prescription date

Initiation of NRT

Baseline periodno CS pharmacological aids

Outcome period outcome comparison

adjusted for baseline confounders

0-12m +12mSC advice

NRT

Retrospective, matched cohort study using GPRD

2006 - 2009


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Study Cohorts

Exposure Cohort

NRTo No recorded exposure to CS pharmacological aids in the prior year,

o First recorded smoking cessation intervention was NRT (using any of, or acombination of products) at the index date.


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Study Cohorts

Exposure Cohort



Comparison Cohorto No recorded smoking cessation attempts using pharmacological aids in the prior year

o First recorded smoking cessation intervention was smoking cessation adviceunaided by pharmacological therapies at the IPD and during the outcomeperiods.


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Study Cohorts

Exposure Cohort



Comparison Cohorto No recorded smoking cessation attempts using pharmacological aids in the prior year

o First recorded smoking cessation intervention was smoking cessation adviceunaided by pharmacological therapies at the IPD and during the outcomeperiods.

All Patientso Aged: 1875 years

o Current smoker throughout the prior year (any quantity of cigarettes).

o No past history of CVD

o Have at least one year of up-to-standard (UTS) baseline data as defined by GPRD(prior to the IPD) and at least 4 weeks of UTS outcome data (following the IPD) orUTS data up to the time of death if death occurred within the outcome period.


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Study Outcomes

Cardiovascular event during 52-week outcome period:

o Coronary Heart Disease diagnosis

o Coronary Heart Disease related death

o Cerebrovascular disease diagnosis

o Cerebrovascular disease death and No of Days from IPD

o Number of GP consultations

o Hospital attendances for Coronary Heart Disease or

Cerebrovascular disease, (including admission, A&E attendance,

out-of-hours attendance, or Out-Patient Department (OPD)attendance)


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Demographics Co-morbidities

Therapies

Clinical Outcomes

Healthcare utilisation

As for Baseline Variables Death

Baseline Variables


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Statistical Analysis

Baseline Variableso Descriptive Analysis Means: t-test

Medians: Mann-Whitney U-Test

Proportion: Chi-squared test

Matched Baseline & Outcome Variables

o Conditional Logistic Regression

Change from Baseline Analyseso Unadjusted: Conditional Logistic Regression

oAdjusted: Cox Proportional Hazards Model


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Baseline Variables

Matching

Baseline Analysis for MatchedCohorts

Outcome Variables

Outcome Analysis

Results

R l


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Baseline Variables

Matching


Outcome Variables

Outcome Analysis

Results


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Variables CS Advice NRT p

n 40,799 17,121

Age at IPD (years) Mean (SD) 47.9 (11.6) 46.8 (11.3)


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Baseline Variables: co-morbidities

1 Read Code at any time, 2 At any time prior to and including IPD

3 Calculated using the Charlson Comorbidity Index over 1 year prior to & including IPD

0

4

8

12

16

CS Advice

NRT

**

*

*

* *

*

* *

%

cohort

*p


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Baseline Variables therapies

0

2

4

6

8

CS AdviceNRT%

cohort

*

*

*

*

*p


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Systolic Blood Pressure Mean (SD) 2496 (6.1) 1034 (6.0)


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Baseline Variables

Matching


Outcome Variables

Outcome Analysis

Results

M t hi


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To reduce difference between cohorts, cohortspopulations were matched

2 SC Advice : 1 NRT

Patients were matched on:

o Gender

o Diabetes

o Cardiovascular Disease

o Hypertension

Matching

R lt


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Baseline Variables

Matching


Outcome Variables

Outcome Analysis

Results

Baseline Variables demographics


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n 33,852 16,926

Age at IPD (years) Mean (SD) 46.96 (11.2) 46.87 (11.3)


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ase e a ab es co o b d t esMatched

1 Read Code at any time, 2 At any time prior to and including IPD

3 Calculated using the Charlson Comorbidity Index over 1 year prior to & including IPD

0

4

8

12

16

CS Advice

NRT

%

matched

cohort

*

*p


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Baseline Variables therapiesMatched

0

2

4

6

8

CS AdviceNRT

%

matched

cohort

*p


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Systolic Blood Pressure Mean (SD) 130.9 (18.7) 130.8 (18.1) 0.018

Diastolic Blood Pressure Mean (SD) 79.5 (11.0) 79.2 (10.6) 0.012

GP Consultations Mean (SD) 7.5 (7.5) 8.8 (8.6)


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Baseline Variables

Matching


Outcome Variables

Outcome Analysis

Results

O t 52 k t


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Outcomes 52 week outcome

0

0.2

0.4

0.6

0.8

1

Coronary Heart Disease

Diagnosis

Cerebrovascular Disease

Diagnosis

All Cause Mortality

CS Advice

NRT

* *

%

matched

cohort

*p


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0

2

4

6

8

10

12CS Advice

NRT

%

matched

cohort


*p


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Healthcare Utilisation


Total Primary & Secondary Care 1 n (%)

Consultations for CHD or

Cerebrovascular Disease 2+ n (%)

135 (0.4) 107 (0.6)0.110

54 (0.2) 49 (0.3)

GP Consultations for CHD n (%)89 (0.3) 74 (0.4)


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Baseline Variables

Matching


Outcome Variables

Outcome Analysis

Results

Secondary Outcomes 52 week


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Secondary Outcomes 52 weekAdjusted for Baseline Confounders

Time to first Coronary Heart Disease diagnosis

Time to first CardioVD diagnosis (ex prior Hx)

Time to first Cerebrovascular disease diagnosis

Time to first CerebroVD diagnosis (ex prior Hx)

All Cause Mortality

All Cause Mortality (ex prior Hx)

Primary & Secondary Care Consultations for CVD


(ex prior Hx)



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Cardiovascular DiseaseCerebrovascular Disease



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All Cause Mortality

All Cause Mortality (ex prior Hx)



(ex prior Hx)



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Mortality



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All Cause MortalityAll Cause Mortality (ex prior Hx)

Consultations for CHD & CVD

Consultations for CVD (ex prior Hx)



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Time to first CHD diagnosis (ex prior Hx)

Time to first Cerebrovascular Disease diagnosis

Time to first CVD diagnosis (ex prior Hx)

All Cause MortalityAll Cause Mortality (ex prior Hx)

Consultations for CVD

Consultations for CVD (ex prior Hx)

Deaths in 52 week Outcome Period


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Deaths in 52 week Outcome Period

Why?

o Predisposing factors?

Demographics

Co-morbidities

Therapies

Other?

Baseline Characteristics Deaths in 52 week


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Clinical Outcomes

0

20

40

60

80

2006

2007

2008

COPD

An

gina

CCIScore=1

CCIScore=2

Beta-Blo

cker

Antiplatelet

Year of IPD Comorbidities Therapies

CS Advice

NRT%

cohort

*p


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Healthcare Utilisation

0

10

20

30

40

50

60

70

0 - 2 3 - 5 6 - 10 11+

Total GP Consultations Total OPDAttendance

CS Advice

NRT%

cohort

*p


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Conclusions

NRT is strongly associated with increased risk of

o coronary heart disease

o cerebral vascular disease

o all cause mortality

Increased Risk is independent of prior history

Death in NRT cohort associated with

o Earlier formulations of NRT

o Higher prevalence of COPD but not Angina

o Prescription of Beta-blockers, anti-platelet therapies

Limitations


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Limitations

Availability of NRT

o NRT available over the counter in UK

Data Availability

o Limited to data held in GPRD

oAre we missing other confounding factors?

Causal Link

o No information on causality

Team Effort


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Team Effort

Data Management Julie von Ziegenweidt et al

o Protocol Design

o GPRD to usable dataset

o Matching

Statistics Annie Burden et alo Protocol Design

o Statistical Analysis

Research Team David Price & Erika Simso Protocol Design

o Manuscript

Questions


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Questions

Documents

Managing Confounding in Observational Databases