Observational Studies Unit of observation Group (eg, geographic area) Individual Cross-sectional Cohort Case-Control Ecological Studies

Three Keys to Study Design Using Observation of Individuals Identify the population that is the Study Base Determine how the experience of the Study Base population will be sampled Consider the timing of measurements relative to the time period of the experience of the Study Base

Concept of the Study Base The study base is the population who experience the disease outcomes you will observe in your study In a cohort study, the study base is an explicitly defined cohort In a cross-sectional study, the study base is a hypothetical cohort sampled at one point in time In a case-control study, the study base is the cohort, either explicit or hypothetical, that gave rise to the cases

Cohort as the Basis of Design All study design is best thought of as ways of sampling the disease experience of a cohort

Cohort Study Design Mimics individual’s progress through life and accompanying disease risk Gold standard because exposure/risk factor is observed before the outcome occurs Randomized trial is a cohort design with exposure assigned rather than observed Case-control design, in particular, is best understood by considering how the experience of a cohort is sampled

Cohort study design Minimum loss to follow-up (1%) D = disease occurrence; arrow = losses to follow-up Minimum loss to follow-up (1%)

Framingham Cohort Study The impact of diabetes on survival following myocardial infarction in men vs women. The Framingham Study. Abbott RD, Donahue RP, Kannel WB, Wilson PW. The impact of diabetes on recurrent myocardial infarction (MI) and fatal coronary heart disease was examined in survivors of an initial MI using 34-year follow-up data in the Framingham Study. Among nondiabetic patients, the risk of fatal coronary heart disease was significantly lower in women compared with men (relative risk, 0.6). In the presence of diabetes, however, the risk of recurrent MI in women was twice the risk in men. In addition, the effect of diabetes doubled the risk of recurrent MI in women (relative risk, 2.1) but had an insignificant effect in men. JAMA, 1989

Main Threat to Validity of a Cohort Study Subjects lost during follow-up Prospective cohort thought of as best study design but poor follow-up can change that Equally true of clinical trials and observational cohorts Number of losses is less important than how losses are related to outcome and risk factor

Subjects lost during follow-up If losses are random, only power is affected If disease incidence is research question, losses related to outcome bias results If association of risk factor to disease is focus, losses bias results only if related to both outcome and the risk factor

Crucial issue is who is leaving cohort: What bias do the losses to follow-up introduce? Are disease diagnoses being missed? Are those with a risk factor more likely to leave and then be diagnosed? ? ? ? ? ? ? ?

Not All Cohorts Are Equally Strong in Design Part of the strength of many clinical trials as a form of cohort study is the effort put into monitoring and retaining subjects in the study Some cohort studies have passive follow-up of subjects; e.g., cohort studies done from a clinic base may rely on patients to return

Two Cohort Studies of HCV/HIV Coinfection and Risk of AIDS Swiss HIV Cohort 3111 patients, ‘96-’99 At least two visits Med. follow-up 28 mos HCV+ more rapid disease progression Adj RH = 1.7 (95% CI = 1.3 - 2.3) No loss to follow-up info (Greub, Lancet, 2000) Johns Hopkins Cohort 1955 patients, ‘95-’01 At least two visits Med. follow-up 25 mos HCV not associated with disease progression Adj RH = 1.0 (95% CI = 0.9 - 1.2) No loss to follow-up info (Sulkowski, JAMA, 2002)

Cross-Sectional Study Design

Cross-Sectional Design Measures prevalence of disease at one point in time. Two types: Point prevalence: Do you currently have a backache? (study takes 4 months) Period prevalence: Have you had a backache in the past 6 months? (study takes 4 months) Comparison of diseased (cases) and non-diseased (controls) in a cross-sectional design is like using prevalent cases and controls in a case-control study

Weakness of Cross-Sectional Design Cannot determine whether putative cause preceded the disease outcome Stronger design if probability sampling of a defined population is used

Case-Control Design: Concept of the Study Base Study Base = the population that gave rise to the cases (Szklo and Nieto call it the “reference population”) Key concept that shows the link between case-control design and cohort design

Case-Control Key Concept #1 Think of the selection of cases and controls as occurring from a cohort

D = disease occurrence; arrow = losses to follow-up Cohort study design D = disease occurrence; arrow = losses to follow-up Given that all the cases are diagnosed, how would you sample controls from this cohort for a case-control study?

Three Ways to Sample Controls within a Cohort At time each case is diagnosed = incidence density sampling A random sample of the cohort baseline = case-cohort design From persons without disease at the end of follow-up = prevalent controls design

Nested Case-Control Study within a Cohort Study Study Base = Cohort In this example, controls are sampled each time a case is diagnosed.

Nested Case-control Study In text example, 4 cases occur at 4 different points in time giving rise to 4 risk sets of cases and controls Controls for each case are selected at random in each risk set from cohort subjects under follow-up at the time (called incidence density sampling) It follows from the random selection, that a control can later become a case Results can be just as valid as using entire cohort.

Case-cohort design: sample baseline of cohort

Case-control design using prevalent controls at end of follow-up.

Definition of a Primary Study Base Primary Study Base = population that gives rise to cases that can be defined before cases appear by a geographical area or some other identifiable entity like a health delivery system or a cohort study Nested within a cohort study is just a special case

Case-Control Key Concept #2 A well defined population can be thought of as a cohort that continues to recruit new subjects during the time period of the study

Examples of Primary Study Bases Residents of San Francisco during 2002 Members of the Kaiser Permanente system in the Bay Area during 2002 Military personnel stationed at California bases during 2002 Participants in Women’s HIV Study

Case-Control Incidence Density Sampling in a Primary Study Base Use a population-based disease registry to identify all new cases of disease during a defined time period and at the time each new case is reported sample controls from current residents Use the rolls of a health care organization and proceed as above

Incidence Density Sampling in a Primary Study Base (e.g., San Francisco County) Primary Study Base New residents Nested case-control in a specified population with new subjects entering

Example of case-control study with incidence density sampling ...In a population-based case-control study in Germany, the authors determined the effect of alcohol consumption at low-to-moderate levels on breast cancer risk among women up to age 50 years. The study included 706 case women whose breast cancer had been newly diagnosed in 1992-1995 and 1,381 residence- and age-matched controls. In multivariate conditional logistic regression analysis, the adjusted odds ratios for breast cancer were 0.71 (95% confidence interval (CI): 0.54, 0.91) for average ethanol intake of 1-5 g/day, 0.67 (95% CI: 0.50, 0.91) for intake of 6-11 g/day, 0.73 (95% CI: 0.51, 1.05) for 12-18 g/day, 1.10 (95% CI: 0.73, 1.65) for 19-30 g/day, and 1.94 (95% CI: 1.18, 3.20) for > or = 31 g/day. . . These data suggest that low-level consumption of alcohol does not increase breast cancer risk in premenopausal women. Kropp, S; Becher, H; Nieters, A; Chang-Claude, J. Low-to-moderate alcohol consumption and breast cancer risk by age 50 years among women in Germany. Am J Epidemiol 2001 Oct 1, 154(7):624-34.

Case-Control Studies from a Secondary Study Base Secondary Study Base = population that gave rise to cases=those persons who would have been cases if they had disease diagnosed during the time period of study Start with cases and then attempt to identify hypothetical cohort that gave rise to them Difficult concept but crucial to case-control design outside a well defined population

Case Control Studies from a Secondary Study Base Source of cases is often one or more hospitals or other medical facilities Problem is identifying who would come to the facility if diagnosed with the disease Careful consideration has to be given to factors causing someone to show up at that institution with that diagnosis

Primary vs. Secondary Base Main problem with a primary base is often ascertainment of all cases eg, no registry of all cases for many diseases by geographic area Main problem with a secondary base is the definition of the base eg, hospital-based case-control studies common but very difficult to determine the study base

Primary vs. Secondary Study Base Important, under-emphasized aspect of case-control design Primary study base case-control studies can be very strong design Secondary study base often not explicitly recognized by researchers Even when recognized is still source of many bad case-control studies

Secondary Study Base Example: glioma cases seen at UCSF Difficult because referrals come from many areas One possible control group might be UCSF patients with a different neurologic disease Patients from a similar tertiary referral clinic are another possible control group

Confusion in the Epidemiology Textbooks about Controls “if hospitalized cases are to be used, it is desirable to select the cases from several hospitals in the community” Gordis, Epidemiology, 1996

Case-Control Key Concept #3 A biased control group is usually the result of the inability to identify a well defined secondary study base (or the result of ignoring the study base concept entirely)

Two Concepts to Distinguish Primary versus Secondary study base focuses on identifying the source of the cases and controls Incident versus Prevalent sampling refers to how the cases and controls are sampled (both types of sampling can be done either in a primary or a secondary study base)

Text example of hospital-based case-control study shows sampling prevalent controls Secondary Study Base

Example of a case-control design using prevalent cases Sampling glioma patients under treatment in a hospital during study period Poor survival so patients in treatment will over-represent those who live longest Nature of bias variable and not predictable

Case Control Key Concept #4 Incident sampling of both cases and controls is preferable to prevalent sampling

Secondary study base can also be sampled using incidence density sampling Secondary study base Problem is that secondary study base usually not clearly defined

A Comment on the Terms Prospective and Retrospective Prospective and retrospective refer to when the study is done in relation to the study base experience (text uses concurrent and non-concurrent) But the key issue for the strength of the design is when were the measurements made in relation to the study base experience

Case Control Key Concept #5 Strength of design rests on accurate measurements made prior to the outcome, not whether it is cohort or case-control sampling

Example of Study Design Choice Kaiser Research Division 1990 Question: Does screening sigmoidoscopy prevent colon cancer deaths? Design choices Prospective cohort: incidence about 100 deaths per yr but only about 15% of colon cancers detectable by sigmoidoscopy—10 to 20 yrs Retrospective cohort: Kaiser members in 18-year period--100,000’s of records to review Case-control

Case-Control Design Colon cancer deaths 1971-1988: 1712 Cases=colon ca deaths detectable by sigmoidoscopy: 261 4 controls per case Controls = alive and in Kaiser at time of matched CA death (incidence-density) Blinded review of prior 10 years of medical records Predictor=screening sigmoidoscopy (not incl sigmoidoscopy for indication) 8.8% of cases vs. 24.2% had prior screening sigmoidoscopy

Critical Features of Good Case-Control Design Clearly identifiable study base (preferably a primary study base) Cases: all, or random sample, of incident diagnoses in the study base Controls: an unbiased sample of study base to estimate exposure prevalence in non-cases Measurements preferably based on records or stored biological samples rather than recall

Studies where unit of observation is a group (group-level data) We don’t have time to review in lecture, so read text carefully as there will be an exercise in the homework Key point is that no measurements are available on individuals either because data source has grouped them (e.g., mortality rates) or they are inherently not individual level (e.g., air quality)