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COMMON METHODOLOGICAL ISSUES FOR CER IN BIG DATA

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(1)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

COMMON METHODOLOGICAL ISSUES FOR CER IN BIG DATA

Sharon-Lise Normand

Harvard Medical School and Harvard School of Public Health sharon@hcp.med.harvard.edu

December 2013

(2)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

OUTLINE

UNCERTAINTY AND SELECTIVE INFERENCE

1

Context

2

Methodological Approaches

3

Concluding Remarks

(3)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

TRANSRADIAL VS TRANSFEMORAL PCI

CONTEXT

Radial artery access permits easier access and easier closure Large number of patients undergoing both procedures Not particularly well studied and of growing importance in the US

Marked heterogeneity in predisposition to bleeding Significant treatment selection (healthier patients undergo transradial procedures)

MASSACHUSETTS

1 2 3 4 5 6 7 8 9 10 11 12

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Quarter

Radial Artery Access & Complications (%)

10/2008 4/2009 10/2009 4/2010 10/2009 4/2011

Treatment = Radial Artery Access (vs Femoral) Outcome = Bleeding/Vascular Complication

130,000 PCIs in MA adults

(4)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

TRANSRADIAL VS TRANSFEMORAL PCI

Does radial artery access cause fewer complications compared to femoral artery access?

If so, then shorter LOS and money is saved; patients ambulatory quicker

Large data registry containing detailed clinical information on patients undergoing PCI

More than 300 variables measured on each person Gets larger when considering treatment specific information (multiple lesions)

Introduces selective inference issues

Drawing inference on a selected subset of the

parameters, a subset that is selected because the

parameters within seem interesting after viewing the

data

(5)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

SELECTIVE INFERENCE

An old issue becoming a big problem because:

Better data acquisition technologies More interconnectivity

Increasing focus on use of observational databases for comparing the effectiveness of treatments

More perspectives:

Payer: Coverage with Evidence Development

Patient: Services that are high value for some may be low value for others (e.g., STEMI versus NSTEMI patients) Health care provider: Adoption of value-enhancing technologies

Two issues:

Uncertainty - which is the correct model?

Bias - causal parameters

(6)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

SELECTIVE INFERENCE

Many decisions required:

Select outcome(s) Defining treatments Identify confounders

Inclusion/exclusion criteria for subjects Causal framework

I will focus on confounders and causal framework

(7)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

MOST COMMONLY EMPLOYED APPROACH

1: Methods that limit number of confounders based on perceived clinical relevance and estimate a single model

Identify confounders based on statistical testing and conduct inferences using the identified confounders More than one model may fit the data well

All Subjects Intervention Radial Femoral

No. of Procedures 5192 35022

Mean Age [SD] 63 [12] 65 [12]

Female 25.3 29.8

Race

White 89.6 89.4

Black 3.3 3.2

Hispanic 4.3 3.5

Asian 1.8 1.7

Native American 0.02 0.07

Other 1.0 2.2

Health Insurance

Government 46.0 50.3

Commercial 4.8 13.4

Other 49.2 36.3

Comorbidities

Diabetes 33.1 32.7

Prior CHF 9.4 12.7

Prior PCI 32.0 34.3

Prior myocardial

infarction (MI) 28.7 30.1 Prior bypass surgery 8.4 15.7

Hypertension 79.6 80.7

Peripheral vascular

disease 12.1 12.8

Smoker 24.8 23.1

Lung disease 13.7 14.4

All Subjects Intervention Radial Femoral

No. of Procedures 5192 35022

Cardiac Presentation

Multi-vessel Disease 10.3 10.9 Number of Vessels >

70% stenosis 1.49 1.58

Left main Disease 3.7 7.2

ST-elevated MI 38.9 42.6

Shock 0.44 1.8

Drugs Prior to Procedure

Heparin (unfractionated) 87.3 61.7 Heparin (low weight

molecular) 3.83 4.27

Thrombin 25.5 54.9

G2B3A inhibitors 26.7 26.8 Platelet Aggregate

inhibitors 85.8 86.6

Intra-Aortic Balloon Pump 0.10 0.55 In-Hospital Complication, % 0.69 2.73 Mean Difference, % (95% CI) -2.04 (-2.30, -1.80)

(8)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

BIG DATA SETTING

Methods that limit number of confounders based on perceived clinical relevance and estimate a single model

Main problems:

Exact confounders required to satisfy no unmeasured confounding assumptions are rarely known

Subgroups exhibiting heterogeneous treatment effects are rarely known

Increasing uncertainty amid the availability of

high-dimensional covariate information

How to reduce the dimension of the problem?

(9)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

DIMENSION REDUCTION TECHNIQUES

2a: Methods relying upon sparseness – only a small number of variables are required to parsimoniously represent the underlying data structure

Y

i

= X

0i

β + 

i

where β is of low dimension

Main idea: assume many model parameters are 0 by imposing a penalty on including too many variables Tools: penalized least squares; least absolute shrinkage and selection (LASSO) methods; and sparse additive models

No special attention to causality

(10)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

DIMENSION REDUCTION TECHNIQUES

2b: Methods relying upon denseness – shrink estimates to a common mean and permit a small number of variables to have distinct coefficients

Tool: Kernel Regularized least squares

2c: Methods relying upon both denseness and sparseness – shrink estimates to a common mean and to zero so that there are two penalty terms

Tool: Elastic Net

No special attention to causality

(11)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

DIMENSION REDUCTION TECHNIQUES

3: Model averaging approaches

p(∆) =

M

X

m=1

p(∆ | M

k

)p(M

k

)

M

k

indexes model and ∆ a parameter of interest

∆ = bleeding risk in radial artery access patients − bleeding risk in femoral artery access patients

M

1

may be a polynomial regression model; M

2

a logistic model with many interactions, etc

Difficult to define the space of models over which to average

No real link to causality in development

(12)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

DIMENSION REDUCTION TECHNIQUES

Estimate the treatment assignment model (propensity score) &

the outcome model simultaneously then average More in line with causal thinking

Y = observed outcome; X observed confounders; T binary treatment (1 = new; 0 = standard)

Assume you have ”all the measured confounders”

logitP (T

i

= 1) = γ

0

+

p

X

j=1

α

Xj

 γ

j

X

ij

Y

i

= β

0αY

+ β

TαY

T

i

+

p

X

j=1

α

Yj

 β

jαY

X

ij

+ 

Yi

α

Yj

and α

Xj

= ”inclusion” probabilities

Confounders: those with large values of both α

Yj

and α

Xj

(13)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

GENERAL IDEA

BUT Model Averaging

Little evidence of use in clinical and policy literature since its introduction in late 1990s

Major paradigm shift if adopted for causal inference However

Meta-analysis acknowledged as providing valid evidence of treatment effectiveness

Approach is transparent

A solution in presence of high dimensional data

(14)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

OBSERVATIONS

1

Plenty of methodology being developed for BIG DATA Need a focus on causal rather than predictive inference

2

Causal inference for CER has constraints different from predictive inference

No unmeasured confounder assumption

Subjects have a chance of getting the treatment Treatment groups are balanced in terms of observables Constant or non-constant treatment effect

3

Non-parametric approach for outcome equation may be

more robust

(15)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

OBSERVATIONS

Compared to transfemoral artery access, transradial access causes:

1.58% (1.12, 2.05) absolute reduction in complications (regression adjusted using perceived clinical importance) 1.40% (0.90, 1.80) (propensity score matched)

2.56% (0.35, 4.75) (2SLS approach)

(16)

PCERC 2013 B&W Sharon-Lise

Normand

Outline Context

Approaches for Big Data Closing Remarks

SOME RECENT RECOMMENDATIONS

References

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