Creating a Cohort of Cases – ICTR Workshop on Clinical Registries

Josef Coresh, MD, PhD Professor of Epidemiology, Biostatistics & Medicine

Johns Hopkins University Director, George W. Comstock Center for Public

Health Research and Prevention Director, Cardiovascular Epidemiology Training

Program

Outline • Cohort definition (see Gordis “Epidemiology” text for overview)

– Membership criteria (“Case” Definition in a clinical cohort of cases – but remember that case series is a weak design) - Considering Referral Pathway - Considering Precohort Factors

• Data collection – Exposures, Treatments & outcomes (mostly covered by other lectures)

• Examples of different cohorts to illustrate ideas: – ARIC

– CHOICE

– CLUE

• Discussion of planned cohorts by participants

Taxonomy of Designs

• Randomized Controlled Trial • Prospective Cohort Study

– Variations exist – non-concurrent (going back to old records etc.)

• Case-Control Study • Cross-Sectional Study • Other Designs

– Quasi-Experimental

– Ecologic

– Case Report

The basic fighting unit was a cohort, composed of six centuries (480 men plus 6 centurions). The legion itself was composed of ten cohorts, and the first cohort had many extra men—the clerks, engineers, and other specialists who did not usually fight—and the senior centurion of the legion, the primipilus, or “number one javelin.”

http://www.vroma.org/images/mcmanus_images/cohort.jpg�

pro·spec·tive Pronunciation: pr&-'spek-tiv also 'prä-", prO-',prä-' Function: adjective Date: circa 1699 1 : relating to or effective in the future 2 a : likely to come about : EXPECTED b : likely to be or become

http://www.m-w.com/cgi-bin/dictionary?book=Dictionary&va=expected�

“Prospective” in Epidemiology

• Clearly defined cohort (group, sample) of persons at risk followed through time

– For pre-defined outcomes

– And their relationship to “exposures” measured prior to the outcome (reduces bias, e.g. recall; but confounding & effect of subclinical disease remain)

• Data regarding exposures (risk factors, predictors) collected prior to data on outcomes (endpoints)

• Research-grade data collection methods used for purpose of testing hypothesis (?)

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*Adjusted to the age of 60 years, female, Whites, HD and non-smokers.

Overall

Distorted Associations – Reverse Causation? (Baseline Subclinical Disease lower Cholesterol higher CVD)

Adjusted* 3-year cardiovascular mortality in Dialysis Patients

Presence of Inflammation/Malnutrition

Absence of Inflammation/Malnutrition

Liu et al. JAMA 2004; 291(4):451-9.

Cohort - Membership

• Cohorts are defined at baseline and followed subsequently (exception: open cohorts can continue to enrol during follow-up)

• Reasons for selection: – Group of interest for follow-up (e.g. specific disease -

brain cancer, MI, ESRD, “middle age”)

• Basis for Inferences: – Internal comparisons (within the cohort) are strongest

(randomized; “exposure” measured prior to outcome)

– External comparisons are quite weak (e.g. case series)

• Selection: biases all external comparisons but only some internal comparisons.

Why Do A Cohort Study?

• Get incidence data • Study a range of possible risk factors • Establish temporal sequence (risk factor before outcome) • Get representative data (of some population) • Prepare for randomized controlled trial

– Effect size estimates

– Population of eligible participants (“registry”)

• Establish a research empire (not a good primary goal)

Types of Cohorts • Occupational (e.g. Asbestos workers) • Convenience (e.g. Precursors, Nurses) • Geographic (e.g. Framingham, ARIC) • Disease or Procedure

– Natural History (e.g. Syncope, Lupus)

– Outcomes Research (e.g. Dialysis, Cataracts)

Sources of Cohort Data

• Clinic Visits – Laboratory Assays

– Interview

– Physical Examination

– Imaging

– Physiologic tests

• Home visits • Mailed materials • Telephone Interview

• Medical Records • Administrative Data

– Medicare

– Medicaid

– Managed Care

– Veterans Admin

• Birth Records • Death Certificates • Specimen Bank

Challenges in Cohort Studies

• Possibly long duration • Possibly large sample size • Need to recruit people “at risk” • Drop outs, Deaths, Other losses • Concern about residual confounding • Multiple comparisons Type I error

How to Exploit Cohort Design When Time is Short & Money is Scarce

• Analyze existing data from another study • Piggy-back onto on-going study • Choose hospital-based cohort • Choose short-term outcome • Consider administrative data • Consider public-use data • Consider non-concurrent design

Examples – Food for Thought

Results Drift – Even in a “good” lab Serum Creatinine Compared to the Mean of All Labs:

College of American Pathologists (CAP) Data

Coresh J et al. Am J Kid Dis 2002;39:920-929

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White Sands - Mean of All MethodsCleveland Clinic - Mean of All MethodsAverage White Sands - Mean of All MethodsAverage Cleveland Clinic - Mean of All Methods

Systematic Errors can be “corrected”

• NHANES 1988-1994 data can be “calibrated” to the cleveland clinic foundation (CCF) 2006 standardized serum creatinine assay using regression

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Uncalibrated NHANES III vs 2006 CCF with identity line

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diff1 Fitted values

black lines are +/- 1.96*SDBland-Altman Plot for Creatinine

Selvin et al. Am J Kidney Dis. 2007; 50(6):918-26.

ARIC – Atherosclerosis Risk in Communities

• NHLBI cohort to study atherosclerosis – Community based sample ages 45-64

– ~5 hour examination: interview, exam, phlebotomy, carotid ultrasound (all standardized) • Baseline, 3, 6, 9 years … 25 years

– Annual telephone calls

– Chart abstraction of all hospitalizations

– Morbidity and Mortality Classification Committee review of CHD outcomes

ARIC-NCS Calendar Year 1987-89 1990-92 1993-95 1996-99 2004-06 2011-13

Aim 1 Prevalence X

Stage 2 Eval 2637

Aim 4

ARIC-NCS Study Design Overview Exam 1 Exam 2 Exam 3 Exam 4

Brain MRI

Aim 3 8,220+phone

Genetics – Aim 5

R – Retinal photography

Aim 2

X 2,000**

Cognitive testing X X (n) 14,201 11,343

Brain MRI X 1,134 X 1,929

Stage 3 MRI

** Includes 357 dementia,852 MCI, 791 normal; 547 with 2 previous brain MRIs •Numbers updated to reflect 2011 start + distant + no lower age limit

X X X X

X X X X

X X

X

R R

15,792 14,348 12,887 11,656 8220 examined more incl. phone

(n)

Median follow-up ,y 0 3 6 9 17 25

1,134

Vascular risk factors

Vascular markers

Age range,y 45-64 48-67 51-70 54-73 62-82 68-89

ARIC V5

Combined visit

X Echo-

cardiogram

X

ARIC – NCS: Aims 1) estimate the prevalence of dementia/MCI by race and sex

in participants aged 70-89, 2) determine whether midlife vascular factors (risk factors

and markers of macrovascular and microvascular disease) predict dementia, MCI and cognitive change,

3) determine whether the associations between midlife vascular factors and dementia/MCI differ by dementia/MCI subtype defined clinically or by MRI signs,

4) identify cerebral markers associated with cognitive change, including progression of MRI ischemic burden and atrophy across 3 MRI scans spanning 17 years, and

5) identify genomic regions containing susceptibility loci for cognitive decline, using 106 SNPs spanning the genome.

Type of contact Content Sample for Stages 2 & 3

AFU Call

Clinic visit

Stage 1 (n=6886) (4/d * 5 d/wk)

Stage 2 – participant + proxy (2.3/d*3d/wk)

Stage 3 (2/d * 2d/wk)

Contract V5 + NCS Cognitive Function * MRI eligibility * Schedule stage2 (+MRI for subset)?

Neuro** + retinal

MRI – same day as Stage 2 for dementia + normals (for borderline cases MRI sampling depends on Stage 2)

(6.5 hours) (~3 hours) (~1 hour)

Home or LTC

Abbreviated exam Abbreviated – done with Stage 1

No MRIs

Overview of ARIC Visit 5 + NCS Data Collection

* Only applies to sampled individuals – sampling fractions based on CF & ∆CF ** Skip the neuro exam on most (all but n=50) normals

CHOICE Cohort Choices for Healthy Outcomes in Caring for ESRD

• Study Design: national prospective cohort study (CHOICE; PI:Powe & Klag & specimen bank Coresh)

• Study Population: – 1026 incident outpatient dialysis patients – Enrolled between 10/95 and 06/98 (DCI + St. Raph) – Recruited within a median of 45 days from 1st dialysis

(98% within 4 months) – From 81 dialysis clinics in 19 States – Age 18 years or older, English or Spanish speaker – Provided informed consent

• Main research topics: Dose & ModalityOutcomes

21

CHOICE Top Papers 119 cited 2,110 by 2010 by (Fink N* AND (Coresh or Powe or Klag))

1. Association between cholesterol level and mortality in - Role of inflammation dialysis patients and malnutrition . Author(s): Liu YM, Coresh J, Eustace JA, et al. JAMA 2004 Times Cited: 209 2. Traditional cardiovascular disease risk factors in dialysis patients compared with the general population: The CHOICE study. Author(s): Longenecker JC, Coresh J, Powe NR, et al. JASN 2002 Times Cited: 180 3. The timing of specialist evaluation in chronic kidney disease and mortality Author(s): Kinchen KS, Sadler J, Fink N, et al. Ann Int Med 2002 Times Cited: 176 4. Validation of comorbid conditions on the end-stage renal disease medical evidence report: The CHOICE study. Author(s): Longenecker JC, Coresh J, Klag MJ, et al. JASN 2000 Times Cited: 141 5. Changes in serum calcium, phosphate, and PTH and the risk of death in incident dialysis patients: A longitudinal study. Author(s): Melamed ML, Eustace JA, Plantinga L, et al. Kidney Int 2006 Times Cited: 96

CHOICE Top Papers 119 cited 2,110 by 2010 by (Fink N* AND (Coresh or Powe or Klag))

6. MYH9 is associated with nondiabetic end-stage renal disease in African Americans Author(s): Kao WHL, Klag MJ, Meoni LA, et al. Nature Genetics 2008 Times Cited: 93 7. Timing of nephrologist referral and arteriovenous access use: The CHOICE study Author(s): Astor BC, Eustace JA, Powe NR, et al. Am J Kidney Dise 2001 Times Cited: 92 8. Comparing the risk for death with peritoneal dialysis and hemodialysis in a national cohort of patients with chronic kidney disease Author(s): Jaar BG, Coresh J, Plantinga LC, et al. Ann Int Med 2005 Times Cited: 86 9. Type of vascular access and survival among incident hemodialysis patients: The choices for healthy outcomes in caring for ESRD (CHOICE) study Author(s): Astor BC, Eustace JA, Powe NR, et al. J Am Soc Nephrol 2005 Times Cited: 73 10. Comorbidity and other factors associated with modality selection in incident dialysis patients: The CHOICE Study Author(s): Miskulin DC, Meyer KB, Athienites NV, et al. J Am Soc Nephrol 2002 Times Cited: 72

Research Opportunities in Washington County: From shoe-leather epidemiology to genomics

Josef Coresh, MD, PhD Professor of Epidemiology, Biostatistics & Medicine Johns Hopkins University Director, George W. Comstock Center for Public Health Research and Prevention Ana Navas-Acien, MD, PhD Assistant Professor, Environmental Health Sciences & Epidemiology Sleep

Heart Health

Washington County, MD Johns Hopkins University

CLUE I & CLUE II Studies

CLUE I (1974) N=26,147 • Serum stored at -70o • Baseline questionnaire

CLUE II (1989) N=32,894 • Plasma , RBC, DNA -70o • Toenail sample • Baseline questionnaire • Food freq. questionnaire

The CLUE Specimen Banks: A paradigm for long-term, population-based studies to evaluate cancer-related biomarkers

CLUE I (1974) N=26,147

Serum

Plasma WBC RBC

Follow-up for cancer outcomes through Washington County Cancer Registry (medical record/treatment info available)

Active follow-up of CLUE II cohort: questionnaires

Key advantages: • large, prospective • population-based • long term follow-up • specimens from multiple time points • specimens obtained prior to diagnosis • multiple health outcomes

(8297 also gave to CLUE I)

Odyssey

CLUE II (1989) N=32,894

Baseline questionnaire – FFQ included in CLUE II

1996, 1998, 2000, 2003, 2007

Number of Deaths from CLUE I and CLUE II Volunteers as of 6/30/2009

Cause of Death ICD10* Clue I Clue II Clue I

& II Total

Heart Disease I20 – I51

1261

713

777

2751 Cancer C00 -C97 929 668 672 2269 Cerebrovascular I60 – I69 254 144 170 568

Chronic Lower Respiratory Disease

J40 –J47

222

125

121

468

Influenza, Pneumonia J10 –J18 149 61 72 282 Accident V01- X59,

Y85, Y86 83 59 52 194

Nephritis, Nephritic syndrome, Nephrosis

N00 -N07, N17 -N19 N25 -N27

53

30

33

116

Total 5823 2379 2476 10678 All deaths 8299 4855

* ICD-8 and 9 used for previous years Underlying caues of death data not available for 1999 CLUE I and 23 CLUE II participants (11 in CLUE I & II)

Thank you! (it takes a team)

CKD-Epi

ARIC Staff CHOICE Study

CVD-Epi Stein Hallan

Creating a Cohort of Cases – ICTR Workshop on �Clinical RegistriesOutlineTaxonomy of DesignsSlide Number 4Slide Number 5“Prospective” in EpidemiologyDistorted Associations – Reverse Causation?�(Baseline Subclinical Disease lower Cholesterol higher CVD)�Adjusted* 3-year cardiovascular mortality in Dialysis Patients�Cohort - MembershipWhy Do A Cohort Study?Types of CohortsSources of Cohort DataChallenges in Cohort StudiesHow to Exploit Cohort Design When Time is Short & Money is ScarceExamples – Food for ThoughtResults Drift – Even in a “good” lab�Serum Creatinine Compared to the Mean of All Labs: �College of American Pathologists (CAP) DataSystematic Errors can be “corrected”ARIC – Atherosclerosis Risk in CommunitiesSlide Number 18ARIC – NCS: AimsOverview of ARIC Visit 5 + NCS Data Collection CHOICE Cohort�Choices for Healthy Outcomes in Caring for ESRDCHOICE Top Papers�119 cited 2,110 by 2010 by (Fink N* AND (Coresh or Powe or Klag))CHOICE Top Papers�119 cited 2,110 by 2010 by (Fink N* AND (Coresh or Powe or Klag))Research Opportunities in Washington County: From shoe-leather epidemiology to genomics      CLUE I & CLUE II StudiesSlide Number 26Number of Deaths �from CLUE I and CLUE II Volunteers�as of 6/30/2009Thank you! (it takes a team)