uc/hh epc comparative effectiveness reviews and evidence- based practice c. michael white, pharmd,...

UC/HH EPCUC/HH EPC

Comparative Effectiveness Reviews and Evidence-based Practice

C. Michael White, PharmD, FCCP, FCPProfessor of Pharmacy & Interim Dept Head,

Pharmacy PracticeDirector,

University of Connecticut/Hartford Hospital Evidence-Based Practice Center

University of Connecticut School of Pharmacy

[email protected]

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Faculty Disclosure

C. Michael White does not have any actual or potential conflict of interest in relation to this CE Activity

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Learning Objectives By attending this program, participants

should be able to: Identify and describe evidence based

medicine Identify and describe the fundamental

components of a meta-analysis Describe the importance of using meta-

analysis in key areas of clinical practice

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What is Happening in Healthcare? Increase number and expense of tests and

treatments available (60% of the growth in costs) Monoclonal antibodies, ICDs, erythropoietin

Aging of Baby Boomers Annual prescriptions filled increased by 1.5 billion

over ten years Healthcare costs exploded over 40 years

Cost have grown by 2.5X more than economy annually from 1960-present

Emmanuel EJ. AHA QCOR Conference.

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A Lot of Money Spent $2.7 trillion spent on healthcare in 2008

1 out of every 6 dollars spent in US How big is a trillion?

1 billion seconds ago Richard Nixon resigned 1 trillion seconds ago was 30,000 years BC

If spending continues to rise at this rate by 2082, 100% of GDP will be spent on healthcare

$200 billion spent on prescription drugs

Emmanuel EJ. AHA QCOR Conference.

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Evidence-Based Medicine Model: What is it?

“Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients.” Not simply cookbook medicine but integration of

evidence into practice and knowing when that evidence applies to particular patient and when it does not

Sackett D. BMJ 1996;312:72.

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Evidence-Based Medicine (EBM) EBM benefits

Maximizes benefits Encourages accountability Enhances efficiency Diminishes harms

EBM implementation Develop consensus on evidence-based practice Disseminate evidence and recommendations to decision-

makers Create incentives to practice EBM

Quality measures Pay-for-performance

Sackett DL. BMJ 1996;312:71–72.; Antman EM. JAMA 1992;268:240-8.; Alexander JA. Health Care Manage Rev 2007;32:150-9.

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Where Does Evidence Come From? Good evidence:

Both benefits and harms evaluated Evaluated strong endpoints

Strong endpoints: survival, risk of MI, cancer recurrence rate, quality of life, cost-effectiveness

Weak endpoints: blood pressure, cholesterol, glucose

Evaluated effectiveness Effectiveness includes efficacy and applicability

Subpopulations such as women, ethnic minorities, and other groups evaluated

Treadwell JR. BMC 2006;6: doi:10.1186/1471-2288-6-52

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Which Single Study Should You Use?

Observed Effect+ 40%

Random Error + 5%

Systematic Error+ 5%

True Effect+ 30%

True Effect+ 30%


Systematic Error - 5%

Random Error + 5%

True Effect+ 30%


Random Error- 5%

Systematic Error- 5%

True Effect+ 30%


Systematic Error + 5%

Random Error - 5%

Study 1

Study 2

Study 3

Study 4

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No, Believe Me, This Single Trial Tells The Whole Truth… No, No, There are No Other

Good Trials…

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Methodological Rigor in ReviewsIndividual Patient

Data Meta-Analyses

Methodological Rigor HIGHLOW

Qualitative Informal and

subjective methodology

Selective, not comprehensive, literature identification

Rarely report literature selection criteria

Subject to systematic and personal biases

Qualitative Formal methods to

find studies but not to evaluate data

Comprehensive literature inclusion

Apply criteria to select high-quality studies

Describe results in evidence tables

Quantitative Systematic

evidence search with statistical analysis

Pool data from multiple studies to estimate summary statistics with confidence intervals

Quantitative Combine and analyze

patient-level data from primary studies to estimate summary statistics

Infrequently seen because of cost, time, and data considerations

Systematic Review w/ Meta-

AnalysesTraditional, Narrative Reviews

Systematic Reviews w/o

Meta-Analysis

Shea B. BMC Medical Res Methodol 2007;7:10 doi:10.1186/1471-2288-7; Lau J. Lancet 1998;351:123-7.Pai M, National Medical Journal of India 2004;17:86-95.

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Rationale for Using Systematic Reviews

Provide transparent and objective summary of large amounts of data

Help to cohere conflicting data/results of primary research

Form the basis of policymakers work (e.g., risk assessments, economic analyses)

Identify gaps in knowledge, helping define further avenues for research

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Meta-Analysis Caution!!!

Healthcare decision-makers need to critically evaluate and understand the value of a given meta-analysis If decision-makers simply accept the pooled result without

exploring the meta-analysis further, they pass all the biases and limitations of the meta-analysis on to their decisions

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Would You Swim in Here??

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Meta-Analysis: The Source Matters

Multiple Poor Studies

Meta-Analyzed

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Publication Bias Publication bias is the tendency of certain

types of trials (such as those with the largest effects) to be published

Publication bias increases the risk that the observed effect might not reflect the true effect May negatively impact consistency, precision, and

magnitude of effect Expanding searches to include additional

languages, citation tracking, hand searching, and grey literature can help identify and possibly minimize publication bias

AHRQ Methods Guide, Finding Evidence, Chapter 5.

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Publication Bias Example Study: 74 antidepressant studies

registered with the FDA 97% of positive studies published 39% of neutral or negative studies published

11 of 14 published in a way that conveyed the positive results but deemphasized the negative

When only published literature was meta-analyzed, a 32% increase in relative effect size occurred versus the more complete dataset of conducted trials

Turner EH. NEJM 2008;358:252-60.

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Detecting Publication Bias Funnel Plots

A pictorial representation of each study plotted by its effect size on the horizontal axis and a measure of variance on the vertical axis. If the plot represents an inverted symmetrical funnel, it is said that publication bias is unlikely but publication bias cannot be excluded when any other configuration is shown.

Egger’s weighted regression statistics Simpler to interpret compared to a funnel plot Provides a p-value, if <0.05 indicates publication bias cannot

be ruled out. Begg’s test

A p-value <0.05 indicates publication bias cannot be ruled out.

Begg’s test requires a larger number of studies (>15-20) in order to provide robust results

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Example of a Funnel Plot

Egger’s p-value =0.51

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Quantifying the Impact of Publication Bias Duval and Tweedie (2000)

Developed the “Trim and Fill” method Uses funnel plot symmetry to estimate the

number of “missing” studies and the magnitudes of their effects.

Then, re-estimates the overall effect size after imputing potentially “missing” studies into the meta-analysis to determine if the results of the original analysis were replicated.

Duval S, Tweedie R. Biometrics 2000;56:455-63

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Example of a Trim & Fill Plot

0

2

4

6

8

10

12

14

-0.2 0 0.2 0.4 0.6 0.8

Ln RR

Inve

rse

stan

dard

err

orTrim & fill funnel plot

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Heterogeneity Between study differences

Some differences between studies is expected due to random variation (chance), but other causes are…

Clinical: Different patient populations, interventions, follow-up times, choice and measurement of outcome

Methodological: Different study designs, quality issues Statistical: Numerical variation in treatment effects

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How to Assess for Heterogeneity

Summary meta-analysis plot [fixed effects]

combined

Study B

Study A

WMD (95% confidence interval)

Summary meta-analysis plot [fixed effects]

combined

Study B

Study A

WMD (95% confidence interval)

Low (No) Heterogeneity High Heterogeneity

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How to Assess for HeterogeneityStatistically

Chi-squared test (Cochrane Q statistic) Measures if observed variation is due to chance This test is problematic and has lower power, so if there

are few studies, it may not detect heterogeneity A p-value <0.10 is typically considered significant

I2 (calculated from Q statistic) Gives % of variation likely due to heterogeneity <25% low heterogeneity; 25% - 75% moderate

heterogeneity; >75% high heterogeneity

Higgins et al. BMJ 2003; 327:557-60

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How to Handle Heterogeneity Use caution when pooling studies that

are not similar clinically, or that have different study designs or where the treatment effects seem inconsistent

If there is a large amount of heterogeneity, explore it… Use subgroup analyses and/or meta-

regression and sensitivity analyses

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Applicability: PopulationConditions That Limit Applicability

Narrow eligibility criteria, high exclusion rate

Differences between study population and patients in community

Narrow or unrepresentative severity or stage of illness

Run in periods with high exclusion rates.

Events rates markedly different than in community

Disease prevalence in study population different than in community

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Applicability: InterventionConditions That Limit Applicability

Regimen not reflective of current practice

Intensity of intervention not feasible for routine use

Monitoring practices or visit frequency not used in practice

Versions not in common use

Co-interventions that likely modify effectiveness of therapy

Level of training not widely available

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Comparator, Outcomes, and Applicability

Conditions That Limit Applicability

ComparatorRegimen not reflective of current practice

Use of substandard alternative therapy

OutcomesSurrogate endpoints, improper definitions for outcomes, composite endpoints

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Systematic Reviews and U.S. Policymakers Findings from systematic reviews are

being used increasingly by U.S. policymakers Since 1999, the Centers for Medicare

and Medicaid Services (CMS) has commissioned systematic reviews as a step in making national coverage decisions

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AHRQ Evidence-Based Practice Centers EPCs develop evidence reports and

technology assessments on topics relevant to clinical, social science/behavioral, economic, and other health care organization and delivery issues— Specifically those that are common,

expensive, and/or significant for the Medicare and Medicaid populations

http://www.ahrq.gov/clinic/epc/

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UCONN/HH EPCDirector:

C. Michael White, Pharm.D., FCP, FCCP UCONN School of Pharmacy

Associate Director/Medical Chief:Jeffrey Kluger, MD, FACC

Hartford Hospital

Supporting Investigators:William Baker, Pharm.D., BCPS

Ripple Talati, Pharm.D., Vanita Tongbram, MBBS, MPH,Ajibade Ashaye, MBBS, MPH,

Wendy Chen, Pharm.D.,Jennifer Colby, Pharm.D.,

Jennifer Scholle, Pharm.D.,Soyon Lee, Pharm.D.

Hartford Hospital/UCONN School of Pharmacy

Medical Librarian:Sharon Giovenale, MS

UCONN School of Pharmacy

Medical Editor:Robert Quercia, MS

Hartford Hospital

Statistician:Jeffrey Mather, MSHartford Hospital

Co-Director/Methods Chief:Craig I. Coleman, Pharm.D.UCONN School of Pharmacy

Project Manager:Diana Sobieraj, Pharm.D.

Hartford Hospital

Content Experts:

Jay Lieberman, MD, Charles Lapin, MD, Raymond McKay, MD, Francis Kiernan, MD, Isaac Silverman, MD, Jennifer Ellis, Pharm.D., Nancy

Rodriguez, PhD, Others to Come

Hartford Hospital, UCHC, CCMC, UCONN

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Systematic Review Programmatic Themes

Nutraceuticals

Movement Disorders

Cardiology

Internal Medicine

Soluble FibersEchinacea Herbs &

SpicesMagnesium

Early & Late Parkinson’s

Restless Legs

Syndrome

Benefits & Harms of Statins

ACE I and ARBs in

Preserved LV Function

Prevention of Atrial

Fibrillation

rhGH in Cystic

Fibrosis

Asthma/ COPD

ACEI/ARB & DM

Nephro

ICDs

CoQ10 & HF

Warfarin/INR

Control

VTE Ortho Surgery

Guidelines

Transfusion in

Transplant

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ACE inhibitors or ARBs in CAD ACE inhibitors and ARBs prolong

survival in MI pts with LVD What is the benefit in CAD pts with

preserved LV function? CMS discussing making ACE inhibitor or

ARB use a performance measure Needs CER to discern evidence

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Mortality and Nonfatal MI

Total Mortality Nonfatal MIRelative risk meta-analysis plot (random effects)

0.2 0.5 1 2 5

TRANSCEND, 2008 1.05 (0.91, 1.20)

PEACE, 2004 0.89 (0.77, 1.03)

CAMELOT, 2004 1.30 (0.47, 3.56)

EUROPA, 2003 0.89 (0.78, 1.02)

SCAT, 2000 0.73 (0.31, 1.74)

PART-2, 2000 0.64 (0.35, 1.17)

HOPE, 2000 0.85 (0.76, 0.95)

combined [random] 0.91 (0.84, 0.98)

relative risk (95% confidence interval)

Relative risk meta-analysis plot (random effects)

0.2 0.5 1 2

PEACE, 2004 1.00 (0.84, 1.20)

CAMELOT, 2004 0.56 (0.27, 1.16)

EUROPA, 2003 0.78 (0.67, 0.90)

SCAT, 2000 0.59 (0.24, 1.42)

PART-2, 2000 0.95 (0.51, 1.76)

HOPE, 2000 0.78 (0.67, 0.91)



Ann Intern Med 2009;151:861-71.

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Stroke and Nonfatal MI

Stroke Composite EndpointRelative risk meta-analysis plot (random effects)

0.01 0.1 0.2 0.5 1 2 5 10

TRANSCEND, 2008 0.83 (0.65, 1.06)

PEACE, 2004 0.77 (0.57, 1.04)

CAMELOT, 2004 0.65 (0.27, 1.53)

EUROPA, 2003 0.96 (0.73, 1.26)

SCAT, 2000 0.22 (0.05, 0.91)

PART-2, 2000 1.76 (0.55, 5.57)

HOPE, 2000 0.69 (0.57, 0.84)




0.5 1 2

TRANSCEND, 2008 0.88 (0.77, 1.00)

PEACE, 2004 0.94 (0.82, 1.07)

HOPE, 2000 0.79 (0.72, 0.87)



Ann Intern Med 2009;151:861-71.

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ACEIs and ARBs in Close Proximity to CABG or PTCA


0.01 0.1 0.2 0.5 1 2 5 10 100

IMAGINE, 2008 0.99 (0.60, 1.66)

AACHEN, 2006 0.91 (0.05, 15.56)

QUIET, 2001 0.99 (0.59, 1.67)

PARIS, 2001 0.98 (0.06, 16.77)

APRES, 2000 0.25 (0.06, 0.99)

MARCATOR, 2000 2.35 (0.38, 14.63)




0.01 0.1 0.2 0.5 1 2 5 10 100

IMAGINE, 2008 0.76 (0.40, 1.43)

AACHEN, 2006 0.45 (0.06, 3.38)

QUIET, 2001 0.89 (0.58, 1.38)

PARIS, 2001 2.94 (0.25, 35.36)

MARCATOR, 1995 1.13 (0.53, 2.43)




0.01 0.1 0.2 0.5 1 2 5

IMAGINE, 2008 1.07 (0.52, 2.17)

APRES, 2000 0.33 (0.03, 3.95)



Total MortalityMI

Stroke

Ann Intern Med 2009;151:861-71.

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Harms Associated with ACE Inhibitors

Conclusions:

Favorable balance of benefits to harms in most patients, but not for those recently undergoing CABG or PTCA

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Systematic Review in Pediatrics

rhGH 0.27-0.35mg/kg/wk in CF Trials are rhGH vs. no therapy except

for one placebo controlled trial Sample sizes are small, most

underpowered Perfect role for systematic review

Phung OJ. Pediatrics 2010;21:347-54.

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rhGH Improves AnthropometricsChange in Height (cm) from Baseline

-1.0 1.5 4.0 6.5 9.00

I2 = 77.3%Egger’s p-value = NA

Weighted Mean Difference (95% Confidence Interval)

Hardin, 2005b

Hutler, 2002

Hardin, 2001

Combined

3.90 (0.52, 7.28)

1.40 (-0.07, 2.87)

4.40 (2.95, 5.85)

3.13 (0.88, 5.38)

Change in Height (cm) from Baseline

-1.0 1.5 4.0 6.5 9.00

I2 = 77.3%Egger’s p-value = NA


Hardin, 2005b

Hutler, 2002

Hardin, 2001

Combined

3.90 (0.52, 7.28)

1.40 (-0.07, 2.87)

4.40 (2.95, 5.85)

3.13 (0.88, 5.38) Change in Weight (kg) from Baseline

-2.0 0.5 3.0 5.5 8.0 10.5

Stalvey, 2008

Schnabel, 2007B

Schnabel, 2007A

Hardin, 2005b

Hutler, 2002

Hardin, 2001

0

I2 = 49%Egger’s p-value = 0.18


Combined

1.00 (0.13, 1.87)

1.00 (-0.35, 2.35)

0.80 (-0.78, 2.38)

5.50 (1.76, 9.24)

1.00 (-1.05, 3.05)

2.80 (1.27, 4.33)

1.48 (0.62, 2.33)

Change in Weight (kg) from Baseline

-2.0 0.5 3.0 5.5 8.0 10.5

Stalvey, 2008

Schnabel, 2007B

Schnabel, 2007A

Hardin, 2005b

Hutler, 2002

Hardin, 2001

0



Combined

1.00 (0.13, 1.87)

1.00 (-0.35, 2.35)

0.80 (-0.78, 2.38)

5.50 (1.76, 9.24)

1.00 (-1.05, 3.05)

2.80 (1.27, 4.33)

1.48 (0.62, 2.33)

Height improves 3.13 cm more with rhGH

Weight improves 1.48 kg more with rhGH

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rhGH in CF: Pulmonary Function Change in FEV1 (L) from Baseline

-0.5 0.5 1.0 1.5

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

Hutler, 2002

0

I2 = 43.2%Egger’s p-value = 0.11


Combined

0.20 (-0.01, 0.41)

0.60 (-0.05, 1.25)

0.64 (0.05, 1.23)

0.04 (-0.16, 0.24)

0.23 (0.01, 0.46)

Change in FEV1 (L) from Baseline

-0.5 0.5 1.0 1.5

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

Hutler, 2002

0



Combined

0.20 (-0.01, 0.41)

0.60 (-0.05, 1.25)

0.64 (0.05, 1.23)

0.04 (-0.16, 0.24)

0.23 (0.01, 0.46)Change in FVC (L) from Baseline

0.75 1.50 2.25

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

0

I2 = 55%Egger’s p-value = NA


Combined

1.00 (0.32, 1.68)

0.90 (0.25, 1.55)

0.40 (0.19, 0.61)

0.67 (0.24, 1.09)

Change in FVC (L) from Baseline

0.75 1.50 2.25

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

0

I2 = 55%Egger’s p-value = NA


Combined

1.00 (0.32, 1.68)

0.90 (0.25, 1.55)

0.40 (0.19, 0.61)

0.67 (0.24, 1.09)

Absolute FEV1 improves 0.28 L more with rhGH

Absolute FVC improves 0.67 L more with rhGH

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rhGH in CF: Pulmonary FunctionChange in %FEV1 from Baseline

-20 -10 10 20 30

Schnabel, 2007B

Schnabel, 2007A

Hardin, 2005c

Schibler, 2003

Hardin, 2001

0



Combined

2.50 (-9.56, 14.56)

3.30 (-9.55, 16.15)

2.00 (-17.21, 21.21)

1.20 (-9.88, 12.28)

4.00 (-10.83, 18.83)

2.43 (-3.99, 8.85)

Change in %FEV1 from Baseline

-20 -10 10 20 30

Schnabel, 2007B

Schnabel, 2007A

Hardin, 2005c

Schibler, 2003

Hardin, 2001

0



Combined

2.50 (-9.56, 14.56)

3.30 (-9.55, 16.15)

2.00 (-17.21, 21.21)

1.20 (-9.88, 12.28)

4.00 (-10.83, 18.83)

2.43 (-3.99, 8.85)

% Predicted FEV1 nonsignificantly improves 2.43% more with rhGH

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rhGH: Improves Bone Mineralization

Change in Bone Mineral Content (g) from Baseline

300 600 900

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

Hardin, 2005a

0



Combined

223 (203, 243)

650 (427, 873)

142 (125, 159)

59 (12, 106)

192 (110, 273)

Change in Bone Mineral Content (g) from Baseline

300 600 900

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

Hardin, 2005a

0



Combined

223 (203, 243)

650 (427, 873)

142 (125, 159)

59 (12, 106)

192 (110, 273)

Bone mineral content improves 192g more with rhGH

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rhGH: HospitalizationsHospitalization Rate (per year) During Therapy

-4 -3 -2 -1

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

Hardin, 2001

0



Combined

-1.30 (-2.20, -0.40)

-1.81 (-2.38, -1.24)

-1.90 (-3.36, -0.44)

-1.50 (-2.07, -0.93)

-1.62 (-1.98, -1.26)

Hospitalization Rate (per year) During Therapy

-4 -3 -2 -1

Hardin, 2006

Hardin, 2005c

Hardin, 2005b

Hardin, 2001

0



Combined

-1.30 (-2.20, -0.40)

-1.81 (-2.38, -1.24)

-1.90 (-3.36, -0.44)

-1.50 (-2.07, -0.93)

-1.62 (-1.98, -1.26)

1.62 fewer hospitalizations per year (studies 6-12 mo)

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rhGH on Final Health Outcomes

Mortality: no data IV antibiotic use: no data Osteopenia/porosis: no data Pneumonia: no data HRQoL: no data

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rhGH: Harms

Serum glucose increases HbA1c does not change

IGF-I concentrations significantly increase >100ng/mL with rhGH than with control Found to be a marker of neoplasm in past

observational studies Cancer: no data

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Balance of Benefits to Harms rhGH reduces hospitalizations (SOE:

Moderate) - an important intermediate outcome

rhGh therapy improves height, weight, and pulmonary function (SOE: Moderate) but may or may not impact final health outcomes Epidemiologic links controversial Absolute vs. % Predicted FEV1

rhGH improves BMC (SOE: Low) NaFl supplements improve bone mineral content

but not fractures Does rhGH reduce fractures?

Risk of DM low but possible risk of neoplasm (SOE: Low)

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Grab Bag Some misc results from our meta-

analyses

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What a Difference a Y-Chromosome Makes! ICD Survival

0.2 0.5 1 2 5

combined 0.88 (0.63, 1.22)

DEFINITE 1.12 (0.49, 2.62)

MADIT-II 0.58 (0.30, 1.15)

SCD-HeFT 0.96 (0.58, 1.61)

DINAMIT 1.00 (0.49, 2.14)

Hazard Ratio (95% confidence interval)

0.2 0.5 1 2

combined 0.74 (0.60, 0.91)

COMPANION 0.65 (0.45, 0.92)

DEFINITE 0.49 (0.27, 0.90)

MADIT-II 0.70 (0.55, 0.93)

SCD-HeFT 0.73 (0.57, 0.93)

DINAMIT 1.16 (0.78, 1.69)

Hazard Ratio (95% confidence interval)

Men = 36% HR Reduction Significant

Women = 12% HR Reduction, Not Significant

Henyan N. J Intern Med 2006;260:467-73.

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Combined Cardiovascular Events by Gender with Statins

Summary meta-analysis plot [random effects]

0.2 0.5 1 2

combined 0.76 (0.70, 0.81)

ALLHAT LLT 0.84 (0.71, 1.00)

LIPS 0.79 (0.64, 0.98)

ASCOT LLA 0.36 (0.21, 0.49)

HPS 0.78 (0.74, 0.83)

LIPID 0.76 (0.68, 0.85)

4S 0.66 (0.58, 0.76)

CARE 0.82 (0.72, 0.92)

PROSPER 0.77 (0.65, 0.92)

AFCAPS/TexCAPS 0.77 (0.64, 0.93)

CIS 0.77 (0.41, 1.44)

CCAIT 0.66 (0.31, 1.42)


Dale KM. CMRO 2007;23:565-74.

Summary meta-analysis plot [random effects]

0.2 0.5 1 2 5

combined 0.79 (0.69, 0.90)

ALLHAT LLT 1.02 (0.81, 1.28)

LIPS 0.66 (0.38, 1.14)

GREACE 0.46 (0.27, 0.72)

HPS 0.81 (0.72, 0.92)

PROSPER 0.96 (0.79, 1.18)

LIPID 0.87 (0.67, 1.13)

4S 0.65 (0.47, 0.91)

CARE 0.58 (0.42, 0.81)

AFCAPS/TexCAPS 0.67 (0.34, 1.31)

PLAC I 1.09 (0.34, 3.52)

CCAIT 1.07 (0.23, 4.88)


Men = 30% RR Reduction Women 30% RR Reduction

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Any Questions?

uc/hh epc comparative effectiveness reviews and evidence- based practice c. michael white, pharmd,...

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