EPI-546 Block I Lecture 8 Study Design I

EPI-546 Block I Lecture 8  Study Design I

EPI-546 Block I Lecture 8 Study Design I XS and Cohort Studies Mathew J. Reeves BVSc, PhD Associate Professor, Epidemiology Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 1 Objectives - Concepts

Uses of risk factor information Association vs. causation Architecture of study designs (Grimes I) Cross sectional (XS) studies Cohort studies (Grimes II) Measures of association RR, PAR, PARF Selection and confounding bias Advantages and disadvantages of cohort studies Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 2 Objectives - Skills Recognize different study designs Define a cohort study Explain the organization of a cohort study

Distinguish prospective from retrospective Define, calculate, interpret RR, PAR and PARF Understand and detect selection and confounding bias Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 3 Risk Factor heard almost daily Cholesterol and heart disease HPV infection and cervical CA Cell phones and brain cancer TV watching and childhood obesity However, association does not mean causation! Mathew J. Reeves, PhD

Dept. of Epidemiology, MSU 4 Why care about risk factors? Fletcher lists the ways risk factors can be used: Identifying individuals/groups at-risk But ability to predict future disease in individual patients is very limited even for well established risk factors e.g., cholesterol and CHD Causation (causative agent vs. marker) Establish pretest probability (Bayes theorem) Risk stratification to identify target population Example: Age > 40 for mammography screening Prevention Remove causative agent & prevent disease

Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 5 Predicting disease in individual patients Fig. Percentage distribution of serum cholesterol levels (mg/dl) in men aged 50-62 who did or did not subsequently develop coronary heart disease (Framingham Study) Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 6 Causation vs. Association An association between a risk factor and disease can be due to: the risk factor being a cause of the disease (= a causative agent) OR

the risk factor is NOT a cause but is merely associated with the disease (= a marker) Must guard against thinking that A causes B when really B causes A (reverse causation). e.g. sedentariness and obesity. Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 7 A B A B

A B Mathew J. Reeves, PhD Dept. of Epidemiology, MSU C A B 8 Prevention Removing a true cause disease incidence. Decrease aspirin use Reyes Syndrome Discourage prone position Back to Sleep SIDS

Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 9 Back-To-Sleep Campaign Began in 1992 Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 10 Architecture of study designs Experimental vs. observational Experimental studies Randomization? RCT vs. quasi-randomized or natural experiments

Observational studies Analytical vs. descriptive Analytical XS, Cohort, CCS Descriptive Case report, case series Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 11 Grimes DA and Schulz KF 2002. An overview of clinical research. Lancet 359:57-61. Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 12

Grimes DA and Schulz KF 2002. An overview of clinical research. Lancet 359:57-61. Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 13 Cross-sectional studies Also called a prevalence study Prevalence measured by conducting a survey of the population of interest e.g.,

Interview of clinic patients Random-digit-dialing telephone survey Mainstay of descriptive epidemiology patterns of occurrence by time, place and person estimate disease frequency (prevalence) and time trends Useful for: program planning resource allocation generate hypotheses

Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 14 Cross-sectional Studies Select sample of individual subjects and report disease prevalence (%) Can also simultaneously classify subjects according to exposure and disease status to draw inferences Describe association between exposure and disease prevalence using the Odds Ratio (OR) Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 15

Cross-sectional Studies Examples: Prevalence of Asthma in School-aged Children in Michigan Trends and changing epidemiology of hepatitis in Italy Characteristics of teenage smokers in Michigan Prevalence of stroke in Olmstead County, MN Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 16 Concept of the Prevalence Pool New cases Recovery Death Mathew J. Reeves, PhD

Dept. of Epidemiology, MSU 17 Cross-sectional Studies Advantages: quick, inexpensive, useful Disadvantages: uncertain temporal relationships survivor effect low prevalence due to rare disease short duration Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 18

Cohort Studies A cohort is a group with something in common e.g., an exposure Start with disease-free at-risk population i.e., susceptible to the disease of interest Determine eligibility and exposure status Follow-up and count incident events

a.k.a prospective, follow-up, incidence or longitudinal Similar in many ways to the RCT except that exposures are chosen by nature rather than by randomization Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 19 Types of Cohort Studies Population-based (one-sample) select entire popl (N) or known fraction of popl (n) p (Exposed) in population can be determined Multi-sample

select subgroups with known exposures e.g., smokers and non-smokers e.g., coal miners and uranium miners p (Exposed) in population cannot be determined Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 20 Prospective Cohort Study Populationbased Design (select entire pop) Disease + - Exp +

a b n1 Exp - c d n0 N Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 21

Prospective Cohort Study Multi-sample Design (select specific exposure groups) Disease + - Exp + a b n1 Exp - c

d n0 Mathew J. Reeves, PhD Dept. of Epidemiology, MSU Rate= a/ n 1 Rate= c/ n 0 22 NOW FUTURE Disease Exposed No disease Eligible

subjects Disease Unexposed No disease Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 23 Relative Risk Cohort Study RR = Incidence rate in exposed Incidence rate in non-exposed The RR is the standard measure of association for cohort studies

RR describes magnitude and direction of the association Incidence can be measured as the IDR or CIR RR = a / ni or a / (a + b) c / no c / (c + d) Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 24 Example - Smoking and Myocardial Infarction (MI) Study: Desert island, pop= 2,000 people, smoking prevalence= 50% Population-based cohort study. Followed for one year.

What is the risk of MI among smokers compared to non-smokers? MI + - Smk + 30 970 Rate = 30 / 1000 Smk - 10

990 Rate = 10 / 1000 RR= 3 Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 25 RR - Interpretation RR = 1.0 indicates the rate (risk) of disease among exposed and nonexposed (= referent category) are identical (= null value) RR = 2.0 rate (risk) is twice as high in exposed versus non-exposed RR = 0.5 rate (risk) in exposed is half that in non-exposed

Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 26 RR - Interpretation RR = > 5.0 or < 0.2 BIG RR = 2.0 5.0 or 0.5 0.2 MODERATE RR = <2.0 or >0.5 SMALL Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 27

Sources of Cohorts Geographically defined groups: Framingham, MA (sampled 6,500 of 28,000, 30-50 yrs of age) Tecumseh, MI (8,641 persons, 88% of population) Special resource groups Medical plans e.g., Kaiser Permanente Medical professionals e.g., Physicians Health Study, Nurses Health Study Veterans College graduates e.g., Harvard Alumni Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 28 Sources of Cohorts

Special exposure groups Occupational exposures e.g., pb workers, U miners If everyone exposed then need an external cohort non-exposed cohort for comparison purposes e.g., compare pb workers to car assembly workers Specific risk factor groups e.g., smokers, IV drug users, HIV+ Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 29 Cohort Design Options variation in timing of E and D measurement Design

Past Prospective Present E Retrospective E D Historical/pros. E E Mathew J. Reeves, PhD

Dept. of Epidemiology, MSU Future D D 30 Retrospective Cohort Study Design Go back and determine exposure status based on historical information and then classify subjects according to their current disease status Disease + -

Exp + a b n1 Exp - c d n0 Mathew J. Reeves, PhD Dept. of Epidemiology, MSU Rate= a/ n 1

Rate= c/ n 0 31 PAST RECORDS NOW Disease Exposed No disease Eligible subjects Disease Unexposed No disease Mathew J. Reeves, PhD Dept. of Epidemiology, MSU

32 Examples retrospective cohort Aware of cases of fibromyalgia in women in a large HMO. Go back and determine who had silicone breast implants (past exposure). Compare incidence of disease in exposed and non-exposed. Framingham study: use frozen blood bank to determine baseline level of hs-CRP and then measure incidence of CHD by risk groups (quartile) Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 33 PAR and PARF Important question for public health

How much can we lower disease incidence if we intervene to remove this risk factor? Want to know how much disease an exposure causes in a population. PAR and PARF assume that the risk factor in question is causal See Lecture 3 course notes Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 34 Venous thromboemolic disease (VTE) and oral contraceptives (OC) in woman of reproductive age Incidence of VTE: OC users: 16 per 10,000 person-years

non-OC users: 4 per 10,000 person-years Total population: 7 per 10,000 person-years RR = 16/4 = 4 Prevalence of exposure to OC: 25% of woman of reproductive age Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 35 Population attributable risk (PAR) The incidence of disease in a population that is associated with a risk factor. Calculated from the Attributable risk (or RD) and the prevalence (P) of the risk factor in the population PAR = Attributable risk x P PAR = (16-4) x 0.25 PAR = 3 per 10,000 person years

Equals the excess incidence of VTE in the population due to OC use Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 36 Population attributable risk fraction (PARF) The fraction of disease in a population that is attributed to a risk factor. PARF = PAR/Total incidence PARF = 3/7per 10,000 person years PARF = 43% Represents the maximum potential impact on disease incidence if risk factor was removed So, remove OCs and incidence of VTE drops 43% in women of repro age (assuming OC is a cause of VTE)

Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 37 NOW FUTURE Disease Exposed No disease Eligible subjects Disease Unexposed No disease PARF =

- Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 38 PARF Calculation PARF = P(RR-1)/ [1 + P(RR-1)] where: P = prevalence, RR = relative risk PARF = 0.25(4-1)/ [1 + 0.25(4-1)] PARF = 43% Note that a factor with a small RR but a large P can cause more disease in a population than a factor with a big RR and a small P. Mathew J. Reeves, PhD Dept. of Epidemiology, MSU

39 Selection Bias Selection bias can occur at the time the cohort is first assembled: Patients assembled for the study differ in ways other than the exposure under study and these factors may determine the outcome e.g., Only the Uranium miners at highest risk of lung cancer (i.e., smokers, prior family history) agree to participate Selection bias can occur during the study e.g., differential loss to follow-up in exposed and un-exposed groups (same issue as per RCT design) Loss to follow-up does not occur at random

To some degree selection bias is almost inevitable. Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 40 Confounding Bias Confounding bias can occur in cohort studies because the exposure of interest is not assigned at random and other risk factors may be associated with both the exposure and disease. Example: cohort study of lecture attendance Attendance -

Exam success - + Baseline epi proficiency Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 41 Cohort Studies - Advantages

Can measure disease incidence Can study the natural history Provides strong evidence of casual association between E and D (time order is known) Provides information on time lag between E and D Multiple diseases can be examined Good choice if exposure is rare (assemble special exposure cohort) Generally less susceptible to bias vs. CCS Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 42 Cohort Studies - Disadvantages

Takes time, need large samples, expensive Complicated to implement and conduct Not useful for rare diseases/outcomes Problems of selection bias At start = assembling the cohort During study = loss to follow-up With prolonged time period: loss-to-follow up exposures change (misclassification) Confounding Exposures not assigned at random Mathew J. Reeves, PhD

Dept. of Epidemiology, MSU 43 Prognostic Studies - predicting outcomes in those with disease Also measured using a cohort design (of affected individuals) Factors that predict outcomes among those with disease are called prognostic factors and may be different from risk factors. Discussed further in Epi-547 Mathew J. Reeves, PhD Dept. of Epidemiology, MSU 44

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