Presentation on theme: "Creating a Cohort of Cases – ICTR Workshop on Clinical Registries Josef Coresh, MD, PhD Professor of Epidemiology, Biostatistics & Medicine Johns Hopkins."— Presentation transcript:
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 centuriescohort (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.”
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 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 (?)
*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
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
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 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 1987-891990-921993-951996-992004-06 2011-13 Aim 1 Prevalence X Stage 2 Eval 2637 Aim 4 ARIC-NCS Study Design Overview Exam 1 Exam 2Exam 3Exam 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 XXXX XXXX X X X RR 15,79214,34812,887 11,656 8220 examined more incl. phone (n) Median follow-up,y 03691725 1,134 Vascular risk factors Vascular markers Age range,y 45-6448-6751-7054-7362-8268-89 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 10 6 SNPs spanning the genome.
Type of contactContentSample for Stages 2 & 3 AFU Call Clinic visitStage 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 LTCAbbreviated examAbbreviated – 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 1 st 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 & Modality Outcomes21
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 -70 o Baseline questionnaire CLUE II (1989) N=32,894 Plasma, RBC, DNA -70 o 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 DeathICD10*Clue IClue II Clue I & IITotal Heart DiseaseI20 – I51 12617137772751 CancerC00 -C97 9296686722269 CerebrovascularI60 – I69254144170 568 Chronic Lower Respiratory Disease J40 –J47222125121 468 Influenza, PneumoniaJ10 –J181496172 282 AccidentV01- X59, Y85, Y86 835952194 Nephritis, Nephritic syndrome, Nephrosis N00 -N07, N17 -N19 N25 -N27 533033 116 Total 58232379247610678 All deaths82994855 * 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-EpiStein Hallan
Comparison of Cystatin Assays: 200 Swedish Participants (A Grubb) R 2 = 0.96 Measurement error adjusted calibration equation: CCF Dade Scys = -0.554 + 1.598* Lund Dako Scys 5 6 7 8 Dade Cystatin C (mg/L) 012345678 Dako Cystatin C (mg/L) Data Regression Line Identity Line
G.W. Comstock Center for Public Health Research and Prevention (est 1962) 1916 JHSPH established with urban and rural field training centers –Washington County as rural site 1930-40s Dr. Carroll Palmer Bureau of Child Hygiene, USPHS –normal growth, dental caries, and tuberculosis –DMF (decayed, missing, filled) index developed 1957 NCI Environmental Cancer Field Research Project in a building adjacent to the Health Department donated by local philanthropists Andrew and Gladys Coffman 1962 Dr. George W. Comstock recruited –Washington County Advisory Board of Health, the Washington County Commissioners, and representatives of the Maryland Department of Health and Mental Hygiene and the Johns Hopkins School of Hygiene and Public Health decide to establish The Training Center for Public Health Research –2005 naming: George W. Comstock Center for Public Health Research & Prevention 1963 “Private” census (98% participation;n=91,909 ) 1964 Training grant 1974 CLUE I (n~26,147) + 1975 “Private” census (n=90,225) 1986 Johns Hopkins Research Center in Downtown Hagerstown –“loose confederation” 1989 CLUE II (n~32,894; Repository: plasma, RBC, DNA, toenails) 2003 Dr. Helzlsouer named as director –Sandra Hoffman, MA, MPH Assistant Director 2008 Dr. Coresh named as director GW Comstock1915-2007