Analysis of Large“Population-based” Databases

for Clinical Research

John Kwagyan, PhD

jkwagyan@howard.edu

Design, Biostatistics & Population StudiesGeorgetown-Howard Center Clinical Translational Science

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………… …………

That we are in the midst of crisis is now well understood. Our nation is at war,…………. Our economy is badly weakened, ……….. Homes have been lost; jobs shed; businesses shuttered. Our health care is too costly; our schools fail too many;..

These are the indicators of crisis, subject to data and statistics.

………………………………

Pres. Barack Obama (Inaugural speech)

Sequence of Steps in a Research Project

• Conceptualization

• Planning/Design

• Execution

• Interpretation

• Reporting

- Abstracts, Presentation, Publication

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Data Collection & Processing

Data Analysis

Outline

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• Types, Uses & Opportunities

• National & Institutional Databases

• Access

• Analysis & Statistical Issues

Types, Uses & Opportunities

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Types of Large Databases

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• (Health) Survey Databases

NHANES

• (Health) Administrative Databases

HCUP

Discharge & Mortality Databases

Specialty Databases- e.g. stroke

• Clinical trials

AASK, ALLHAT

Uses of Large Databases

• Secondary Analysis

~ publications

• Pilot Data for grant proposals

• Power Exploration

• Hypothesis Generation & Testing

• Estimate of Summary Statistics

-prevalence, incidence, mortality, etc

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Advantages using large databases

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• Large Sample• Fast & Easily (Some) Accessible• Provide population Estimates• Can test trend over time • Observational, cross-sectional, longitudinal

Limitations & Challenges

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• Non-Experimental: (Survey & Administrative) • Most are cross sectional• May require special skills -special statistical techniques & software usage• Statistical Issues to address• May involve long bureaucracy -Written request or proposal - IRB approval• May cost a fee & travel

Funding Opportunities Secondary Analysis

R03, R21 mechanisms----• Obtain data collected by the parent study or by Ancillary Studies to prepare a scientific manuscript for publication on a topic (aims) that has not yet been addressed.

• Receive limited preliminary study data summaries, to prepare a proposal for funding of secondary analyses of data .

• Obtain specimens (e.g. blood, urine, imaging scans) for new assays or analyses to be conducted using an outside funding source.

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Nces.ed.org/nationsreportcard/researchcenter/funding.asp

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Funding Opportunities

National Databases

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National Health & Nutrition Examination Survey (NHANES): www.cdc.org/nchs/nhanes.html

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• Population : Adult & Children

• Method: Face-Face Interview, Physical Exams

• Content: Anthropometry, Respiratory disease, chronic & infectious disease, mental health & cognitive functioning, reproductive history & sexual behavior

• Data: N~5000/yr since 1999; Initiated in 1960

• Notes: Supplemental food survey, online tutorial

National Health Interview Survey (NHIS) : www.cdc.org/nchs/nhis.html

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• Population : Household (Families) Adult & Children

• Method: Face-Face Interview, Physical Exams

• Content: Health conditions & behaviors, access to & use of health services; Genetic testing,

• Data: N ~35,000 Households (~87,500 persons) Initiated in 1957

• Notes: Data used widely by the DHHS to monitor trends in illness and disability and to track progress toward achieving national health objectives.

Surveillance Epidemiology and End Results (SEER): http://seer.cancer.org

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• Population : Children to Adult

• Method: Data collected from cancer registries that cover ~28% of the US population; follow-up with individual cases until death

• Content: Cancer incidence, prevalence, and survival data; limited demographics (age, race/ethnicity, region)

• Data: Cancer cases in registries, >6Million cases

• Notes: Need specialized software to analyze (SEER*Stat or SEER*Prep) downloaded from website; Must sign user agreement to obtain.

Healthcare Cost & Utilization Project (HCUP) http://www.ahrq.org/data/hcup

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• Population : All ages

• Method: A family of healthcare databases and tools

• Content: Databases enable research on a broad range of health policy issues, including cost and quality of health services, medical practice patterns, access to health care programs, and outcomes of treatments.

• Data: Cancer cases in registries, • Notes: Databases are available for purchase through a

central distributor

African America Study of Kidney Disease & Hypertension(AASK):www.niddkrepository.org/

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• Population : Adult African Americans, 18-70 years

• Method: Participants followed for 2years to measure the long-term effects of blood pressure control in patients with kidney disease attributed to high blood pressure.

• Content: BP, markers of kidney function

• Data: 1094 • Notes: Largest and longest study of chronic kidney disease

in African Americans

CDC Wonder wonder.cdc.org

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• Wide-ranging Online Health related Datasets for Epidemiologic Research

• Each data set can be queried using a series of menus

• Provides an online tool for retrieving and analyzing data

CDC Wonder

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Institutional (GHUCCTS) Databases

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• Obesity Project - HU • Family Genetics Study of Prostate Cancer-HU• HIV in DC – HU• Memory Disorder Study - HU• Spinal Cord Disease Database - MRI• Stroke Database - MRI/NRH• Brain Injury Database- MRI/NRH• National Capital Spinal Cord Injury Model System – MRI/NRH• Strong Heart Study- MRI• The VA Decision Support System Database (DSS) – VA• ……..• ………

Institutional (GHUCCTS) Databases

Access/Retrieval

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Data Access/Retrieval

• May require special request or proposals

- aims, etc

-preparation of detailed analysis plans• Understand the database structure• Extraction of requisite data for specific objectives• Application of appropriate linkage techniques for

multiple data sources• Process & Storage

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Database Structure

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• Relational Structure: (1-to-1)

represented by a table of rows & columns ~ attributes are listed in columns

ID, AGE, GENDER, …..

~unique identifiers

• Hierarchical (Nested) Structure: (1-to-many)

allows for multiplicity of attributes whiles preserving relationships

Data Structure

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RELATIONAL

PID Age genderdisease_status100 45 Male 0101 56 female 1102 67 female 0

HIERARCHICAL/NESTEDWARD PID WARD FAMID PID

1 100 1 1 1001 101 1 1 1011 102 1 2 1002 100 1 2 1012 101 2 1 1002 102 2 1 101

2 2 1002 2 101

Data Analysis Methods

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Types of Data Endpoints

• Continuous Data - BP, BMI, TC, LDL, HDL, Blood Sugar

• Categorical Data - Hypertension, Obese, Dyslipidemia, Diabetes

• Count Data 0, 1, 2, 3

• Survival (Time-to-Event) Data - time-to-cardiac event, time-to-death

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Partition Data Into Subsets

Core partitioning ~ arises naturally• Race• Gender• Age Group• Geographic Region

Time partitioning• 2000-2010• 1995-2000; 2000-05

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Descriptive AnalysisBy Partition

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Measures of Central Tendency Means, Median, Mode, etc Rates – Prevalence, Incidence, Survival, Mortality Variability SD, range, IQR

Visualization MethodsExploratory Analysis

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Apply visualization methods by subsets

Charts Scatter Plot matrix ~ continuous measures Trellis plot ~ all measures

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Trellis Plot

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Inference Statistical Tests

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The method used depends on

1. Outcome measure Univariate Multivariate 2. Study design

Continuous Data

Parametric Tests• Paired T-tests ~ non-

comparative open-label studies (pre-post studies)

• Two Sample T-test ~ comparative studies (eg. parallel-group designs )

• ANOVA (F-Test) ~ comparing multiple groups (eg, parallel-groups designs, factorial designs)

Non-Parametric Equivalent• Wilcoxon Signed Rank

Test

• Wilcoxon Rank Sum Test

• Kruskal-Wallis Test

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Categorical Data

What is the question?

Compare rates:

prevalence, incidence, mortality!

• Chi-square Test• McNemar Test (pre-post designs)• Mantel-Haenzel test- heterogeneity

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Survival Data

Question? Compare survival rates!

Survival curves, hazard ratios

• Kaplan-Meier Estimator

• Log- Rank Test

• Likelihood Ratio Test

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Regression Methods

• used when it is necessary to adjust for different covariate/confounding effects

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Cholesterol level ~ gender, age, diet

Regression Methods

• Continuous Data

~ Linear Regression Models

• Categorical Data

~ Logistic Regression Models

~ Conditional Regression Models

• Survival Data

~ Proportional Hazard Regression

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Multi-Level Models Hierarchical (Nested) Models

• Multilevel Regression

• Mixed Effect Models

• Nested Models

-GEE

-Proc Nested• Bayesian Approaches

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Multivariable Methods

TC ~ gender, age, diet

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[HDL, LDL, TG] ~ gender, age, diet

Use to analyze multiple outcomes jointly

univariate

Multivariable

Risk factors

Multivariable Methods

• MANOVA

• Discriminant Analysis

• Factor Analysis

• Cluster Analysis

• Principal Component Analysis

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Statistical Issues

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Statistical Issues

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• Sampling error• Missing data• high likelihood of finding a significant difference due to chance alone • Potential for bias result is substantial

Recommendations for Health Survey Data

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• Statistical weights • Stratification• Clustering• Variance Estimation

Use ~

Use of Statistical Weights

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• The statistical weight of a sampled person is the number of people in the population that the person represents. • If sampling rate is 1/1000 Each sampled person represents 1000 people Each sampled person would have a sample weight of 1000• Weights derived from selection probabilities response rates post-stratification adjustments (e.g. gender, education, etc)

Stratification

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• Population divided before sampling into disjoint, exhaustive groups (strata) Members termed primary sampling units (PSUs) Independent samples are taken in each strata

• Strata formed by similar demographic areas

Clustering Hierarchical (Nested) Data

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• Persons residing in a small area (cluster) may have similar characteristics• Responses of subjects in clusters may be correlated • Dependence between subjects leads to inflate variance• Correlation must be accounted for in the analysis

Variance Estimation

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Use appropriate variance estimation methods:

Linearization: Uses a Taylor series expansion to estimate variance of non-linear estimators Default method for most stats programs

Replication methods: Calculates different parameter estimates for each replicate and combines these to estimate variance. Jackknife, etc

Summary

• Fast and easily accessible• Provides several uses and opportunities• Large databases will continue to provide important findings

for clinical research• Mindful of statistical issues• Use weighting, clustering or stratification when appropriate

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Thank you

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