Intermediate methods in observational epidemiology 2008 Instructor: Moyses Szklo Study Designs in Observational Epidemiology Epidemiologic reasoning • To determine whether a statistical association exists between a presumed risk factor and disease • To derive inferences regarding a possible causal relationship from the patterns of the statistical associations To determine whether a statistical association exists between a presumed risk factor and a disease • Studies using populations or groups of individuals as units of observation – Descriptive studies (prevalence, incidence, trends) – Analysis of birth cohorts (cohort, age, period effects) – Ecological studies • Studies using individuals as units of observation – – – – – Randomized clinical trials Cohort studies Case-control studies Cross-sectional studies Other (nested case-control, case-crossover study) Studies using groups as units of observation • ECOLOGIC STUDIES – To assess the correlation between a presumed risk factor and an outcome, mean values of the outcome (e.g., rate, mean) are plotted against mean values of the factor (e.g., average per capita fat intake), using groups as units of observation – Groups could be defined by place (geographical comparisons) or time (temporal trends). A plot of the population of Oldenburg at the end of each year against the number of storks observed in that year, 1930-1936. Ornitholigische Monatsberichte 1936;44(2) Relation between Anopheles inoculation and incidence of Plasmodium Falciparum parasitemia in cohorts of children in Western Kenya McElroy et al: Am J Epidemiol 1997;145:945-56. Ecological fallacy “The bias that may occur because an association observed between variables on aggregate levels does no necessarily represent the association that exists at the individual level.” Last: Dictionary of Epidemiology, 1995 Example of ecological bias* Population A $10.5K $34.5K $28.5K $12.2K $45.6K $17.5K $19.8K Traffic injuries: 4/7=47% Mean income: $23,940 Population B $12.5K $32.5K $24.3K $10.0K $14.3K $38.0K $26.4K Traffic injuries: 3/7=43% Mean income: $22,430 Population C $28.7K $30.2K $13.5K $23.5K Mean income: $21,410 *Based on: Diez-Roux, Am J Public Health 1998;88:216. $10.8K $22.7K $20.5K Traffic injuries: 2/7=29% Traffic injuries (%) 60 Ecologic analysis 50 40 30 Higher income is associated with higher injury rate 20 10 0 21 22 23 24 Mean income (US$, in 1000) 25 Example of ecological bias* Population A $10.5K $34.5K $28.5K $12.2K $45.6K $17.5K $19.8K Traffic injuries: 4/7=47% Mean income: $23,940 Population B $12.5K $32.5K $24.3K $10.0K $14.3K $38.0K $26.4K Traffic injuries: 3/7=43% Mean income: $22,430 Population C $28.7K $30.2K $13.5K $23.5K Mean income: $21,410 *Based on: Diez-Roux, Am J Public Health 1998;88:216. $10.8K $22.7K $20.5K Traffic injuries: 2/7=29% Traffic injuries (%) 60 Ecologic analysis 50 40 30 Higher income is associated with higher injury rate 20 10 0 21 22 23 24 25 Mean income (US$, in 1000) Individual-based analysis Non cases Injury cases have lower mean income than non cases Injury cases 0 10 20 30 Mean income (1000 US$) 40 • Which of the two levels of inference is wrong? – Concluding that high income is a risk factor for injuries (based on the ecologic data) is subject to ecologic fallacy. – BUT … concluding that, because injury cases tend to have lower income, communities with higher average income should have lower injury rates is also wrong! • The real problem is cross-level reference* – Using ecologic data to make inference at the individual level (ecologic fallacy). – Or using the individual data to make inferences at the group (population level). • When used to make inferences at the proper level, both approaches might be right: – Individuals with a lower income are more likely to be injured. – In communities with higher average incomes, there is a greater number of cars, thus exposing lower income individuals to injuries. *Morgenstern: Ann Rev Public Health 1995;16:61-81. Types of ecologic variables • Analogs of individual-level characteristics – Aggregate measures (proportion, mean) • Prevalence of disease • Mean saturated fat intake • Percentage with less than high school education – Environmental measures • Air pollution • Global measures • Health care system • Gun control law • Herd immunity Ecologic studies are the design of choice in certain situations: • When the level of inference of interest is at the population level – Food availability (e.g., Goldberger et al: Public Health Rep 1916;35:2673-714). – Effects of tax hikes in cigarette sales • When the variability of exposure within the population is limited – Salt intake and hypertension (Elliot, 1992) – Fat intake and breast cancer (Wynder et al, 1997) Hypothetical data on individuals from a World-wide population Strong positive (linear) association Usual daily salt intake Hypothetical data on individuals from a World-wide population Individuals from country A Usual daily salt intake No association Usual daily salt intake Hypothetical data on individuals from a World-wide population Country A Country B Country C Country D Country E Country F Country G Usual daily salt intake Hypothetical ecologic data from 7 countries Country A Country B Country C Country D Country E Country F Country G Strong positive (linear) association Mean usual daily salt intake Relation between sodium (Na) excretion and age increase in systolic blood pressure (SBP) in centers in the INTERSALT cohort* *Elliot, in Marmot and Elliot (eds.): Coronary Heart Disease Epidemiology, Oxford, 1992, pp.166-78. Studies based on individuals: Prospective Studies Experimental (Randomized clinical trial) Study Population Random allocation Intervention Control Follow-up Outcome Outcome Studies based on individuals: Prospective Studies Experimental Non-experimental (Randomized clinical trial) (observational*) Study Population Study Population Random allocation Non-random allocation Intervention Control Follow-up Outcome Outcome Intervention Control Follow-up Outcome Outcome *Cohort Study Studies based on individuals: Prospective Studies Experimental Non-experimental (Randomized clinical trial) (observational*) Study Population Study Population Random allocation Non-random allocation Intervention Control Follow-up Outcome Outcome Intervention Control Follow-up Outcome Outcome *Cohort Study Studies based on individuals 1.- Cohort studies Cohort Outcome Death Disease Recurrence Recovery Suspected Exposure Time Studies based on individuals 1.- Cohort studies Diseased Non diseased Ince Exposed RR Non Exposed Incē Time Cohort study Losses to follow-up Events Initial pop time Final pop Cohort study Losses to follow-up Events EXPOSED INCIDENCEEXP Initial pop Final pop time = RR Losses to follow-up Events UNEXPOSED INCIDENCEUNEXP Initial pop time Final pop Cohort Studies Strengths • Allows calculation of incidence • Time sequence is clear (exposure →outcome) Reduces potential for bias • • • • • Allows calculation of all measures of association Multiple outcomes can be assessed Multiple exposures can be assessed New hypothesis can be tested as time goes by Efficient ways to evaluate associations Stored specimens can be analyzed later for new analytes / risk factors Cohort Studies Additional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done Cohort Studies Additional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done Cohort Studies Additional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done Cohort Studies Additional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done Cohort Studies Additional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done Rich database for analyses Studies based on individuals 2.- Case-control studies Non Diseased diseased Exposed Non Exposed Odds expD Odds expD- OR Case-control study Losses Cases Controls Hypothetical pop time Case-control study Losses Cases Controls Hypothetical Recruiting only cases with longest survival (Prevalent cases) pop Risk of durationtime (incidence-prevalence) bias INCIDENCE-PREVALENCE BIAS Prevalence Prevalence Odds Incidence Duration 1 Prevalence Prev exposed Prevalence Odds Ratio Prev unexposed 1 Prev exposed 1 Prev unexposed Incid exposed Durexposed Incid unexposed Durunexposed Relative Risk Incidence-Prevalence Bias or Duration bias or Survival bias or Selection bias CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES AND NON-CASES* Losses Cases EXPOSED (n= 100) Controls Exposed? Cases Controls* Yes 4 96 No 4 96 8 192 OR (4 96) (96 4) 10 . Hypothetical pop time Losses Cases UNEXPOSED (n= 100) Hypothetical pop Controls time Assumption: All non-cases survive through the end of the follow-up CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES AND NON-CASES* Losses Cases EXPOSED (n= 100) Exposed? Cases Controls* Yes 4 96 No 4 96 8 192 Controls OR (4 96) (96 4) 10 . Hypothetical pop time Losses Cases UNEXPOSED (n= 100) Hypothetical pop Controls CASE-CONTROL STUDY INCLUDING ONLY POINT PREVALENT CASES, BUT ALL NON-CASES Exposed? Cases Controls* Yes 1 96 No 4 96 5 192 OR (1 96) (96 4) 0.25 time Assumption: All non-cases survive through the end of the follow-up SELECTION/SURVIVAL BIAS (ALSO KNOWN AS PREVALENCEINCIDENCE BIAS) Results from cross-sectional surveys can be analyzed in a prospective or case-control mode Disease Exposure Yes No Exposure Yes No Odds exp Yes a c No b d Disease Yes No a c a/c b d b/d Prevalencedis a/a+b c/c+d CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES AND NON-CASES* Losses Cases EXPOSED (n= 100) Exposed? Cases Controls Yes 4 96 No 4 96 8 192 Controls OR (4 96) (96 4) 10 . Hypothetical pop time Losses Cases UNEXPOSED (n= 100) Hypothetical pop Controls PREVALENCE OF DISEASE BY EXPOSURE Disease? Exposed? Yes No Total Yes 1 96 97 No 4 96 100 PR= 1/97 ÷ 4/100= 0.26 time Assumption: All non-cases survive through the end of the follow-up SELECTION/SURVIVAL BIAS (ALSO KNOWN AS PREVALENCEINCIDENCE BIAS) Cross-Sectional Vs. “Retrospective” Case-Control Studies Key concept: How “caseness” and exposure are ascertained ANOTHER TYPE OF CROSS-SECTIONAL BIAS: REVERSE CAUSALITY Cross-Sectional Exposure Assessment: Association of Low Serum Carotenoids with Age-Related Macular Degeneration Total Carotenoids Cases Controls ≤ 1.024 μmol/L (exposed) 107 115 > 1.024 μmol/L (unexposed) 284 462 Total 391 577 0.38:1.0 0.25:1.0 Exposure Odds Odds Ratio= 1.5 IT IS NOT POSSIBLE TO DETERMINE WHAT CAME FIRST (EXPOSURE OR OUTCOME). THUS, INDIVIDUALS WITH AGE-RELATED MACULAR DEGENERATION MAY CHANGE THEIR DIETS, WHICH IN TURN MAY RESULT IN LOW CONCENTRATIONS OF TOTAL CAROTENOIDS ‘REVERSE CAUSALITY’ Cross-sectional Studies National Center for Health Statistics (NCHS) • National Health and Nutrition Examination Survey (NHANES) • 20,000+ individuals • Oversampled children, age>65, minorities • Questionnaires, physical exam, laboratory data • National Health Interview Survey (NHIS) • National Immunization Survey (NIS) • National Survey of Family Growth (NSFG) www.cdc.gov/nchs Cross-sectional survey Point prevalence= Snapshot of prevalence at time of a cross-sectional survey Cross-sectional Studies What can we learn? Descriptions / Distributions: Standardized centile curves of body mass index for Japanese children and adolescents based on the 1978-1981 national survey data. Ann Hum Biol. 2006 Jul-Aug;33(4):444-53. Prevalence: The prevalence of oral mucosal lesions in U.S. adults: data from the Third National Health and Nutrition Examination Survey, 1988-1994. J Am Dent Assoc. 2004 Sep;135(9):1279-86 Trends in prevalence: Thirty-year trends in cardiovascular risk factor levels among US adults with diabetes: National Health and Nutrition Examination Surveys, 1971- 2000 Am J Epidemiol. 2004 Sep 15;160(6):531-9 Association of exposure with prevalence of disease: Prevalence of urinary schistosomiasis and HIV in females living in a rural community of Zimbabwe: does age matter? Trans R Soc Trop Med Hyg. 2006 Oct 23 Cross-sectional Studies • Baseline examination of randomized trials • Cross-sectional study of health-related quality of life in African Americans with chronic renal insufficiency: the African American Study of Kidney Disease and Hypertension Trial. • Am J Kidney Dis. 2002 Mar;39(3):513-24. • Baseline examination of cohort studies • Association of kidney function and hemoglobin with left ventricular morphology among African Americans: the Atherosclerosis Risk in Communities (ARIC) study. • Am J Kidney Dis. 2004 May;43(5):836-45. Cross-sectional Studies Strengths and Limitations • Strengths • Primary method of estimating prevalence • Logistically efficient Relatively fast (no follow-up required) Can enroll large numbers of participants • Large surveys can be used for many exposures and diseases • Often generalizable – can oversample smaller subpopulations • Limitations • Large numbers needed for rare exposures / outcomes • No information on timing of outcome relative to exposure (temporality) • Includes only those individuals alive at the time of the study Prevalence-incidence bias Case-control studies within a defined cohort • Case-Cohort Studies • Nested Case-Control Studies Example of case-cohort study Association between CMV antibodies and incident coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) Study (Sorlie et al: Arch Intern Med 2000;160:2027-32) Cohort: 14,170 adult individuals (45-64 yrs at baseline) from 4 US communities (Jackson, Miss; Minneapolis, MN, Forsyth Co NC; Washington Co, MD), free of CHD at baseline. Followed-up for up to 5 years. • Cases: 221 incident CHD cases • Controls: Random sample from baseline cohort, n=515 (included 10 subsequent cases). “The population with the highest antibody levels of CMV (approximately the upper 20%) showed an increased relative risk (RR) of CHD of 1.76 (95% confidence interval, 1.00-3.11), adjusting for age, sex, and race.” Case-cohort study N~14,000 Option 1= thaw serum samples of 14,000 persons, classify by CMV titer (+) or (-), and followup to calculate incidence in each group (exposed vs. unexposed) Option 2: Case-cohort study Initial pop Time (5 years) Final pop Example of case-cohort study Association between CMV antibodies and incident coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) Study (Sorlie et al: Arch Intern Med 2000;160:2027-32) Cohort: 14,170 adult individuals (45-64 yrs at baseline) from 4 US communities (Jackson, Miss; Minneapolis, MN, Forsyth Co NC; Washington Co, MD), free of CHD at baseline. Followed-up for up to 5 years. • Cases: 221 incident CHD cases • Controls: Random sample from baseline cohort, n=515 (included 10 subsequent cases). “The population with the highest antibody levels of CMV (approximately the upper 20%) showed an increased relative risk (RR) of CHD of 1.76 (95% confidence interval, 1.00-3.11), adjusting for age, sex, and race.” case loss Initial pop Nested case-control study (within a cohort) (“Incidence density sampling”) time time Final pop “Risk sets” Example of nested case-control study Inflammatory Markers and CHD Risk (Pai JK, et al, New Eng J Med 2004;351:2599-610) Cohorts: Nurses’ Health Study (30-55 yrs old, n= 121 700 nurses) and Health Professionals Follow-up Study (40-75 yrs old, n= 51 529) (follow-up: 6 years and 8 years, respectively) • Cases: 239 women and 265 men who developed an MI • Controls: Selected by risk set sampling using 2:1 ratio, matched for age, smoking, and date of blood sampling from participants free of cardiovascular disease at the time CHD was diagnosed in cases. Cases Initial pop Controls Example of nested case-control study Inflammatory Markers and CHD Risk (Pai JK, et al, New Eng J Med 2004;351:2599-610) Cohorts: Nurses’ Health Study (30-55 yrs old, n= 121 700 nurses) and Health Professionals Follow-up Study (40-75 yrs old, n= 51 529) (follow-up: 6 years and 8 years, respectively) • Cases: 239 women and 265 men who developed an MI • Controls: Selected by risk set sampling using 2:1 ratio, matched for age, smoking, and date of blood sampling from participants free of cardiovascular disease at the time CHD was diagnosed in cases. Cases Initial pop Controls Rate Ratios of Coronary Heart Disease* During Follow-up According to Quintiles of C-Reactive Protein at Baseline, Nurses Health Study (Women) and Health Professionals Study (Men) Quintile of Plasma Level of C-Reactive Protein 1 2 3 4 5 Median – mg/liter 0.50 1.18 2.20 4.02 9.14 Rate Ratios 1.0 1.23 0.89 1.22 1.61 Median – mg/liter 0.27 0.60 1.08 2.05 5.24 Rate Ratios 1.0 1.75 1.74 2.14 2.55 Women Men *Adjusted for socio-demographic and cardiovascular risk factors (Pai JK, et al, New Eng J Med 2004;351:2599-610) Rate Ratios of Coronary Heart Disease* During Follow-up According to Quintiles of C-Reactive Protein at Baseline, Nurses Health Study (Women) and Health Professionals Study (Men) Quintile of Plasma Level of C-Reactive Protein 1 2 3 4 5 Median – mg/liter 0.50 1.18 2.20 4.02 9.14 Rate Ratios 1.0 1.23 0.89 1.22 1.61 Median – mg/liter 0.27 0.60 1.08 2.05 5.24 Rate Ratios 1.0 1.75 1.74 2.14 2.55 Women Men *Adjusted for socio-demographic and cardiovascular risk factors (Pai JK, et al, New Eng J Med 2004;351:2599-610) Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Initial pop Final pop “Risk set” • It is best for time-dependent exposures Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Initial pop Final pop “Risk set” • It is best for time-dependent exposures Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Initial pop Final pop “Risk set” • It is best for time-dependent exposures Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Initial pop Final pop “Risk set” • It is best for time-dependent exposures Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and Initial pop Final pop “Risk set” • It is best for time-dependent exposures; • It automatically matches for length of follow (and for previous losses). (Disadvantage: for each case, a different matched control sample must be chosen.) • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and Initial pop Final pop “Risk set” • It is best for time-dependent exposures; • It automatically matches for length of follow (and for previous losses). (Disadvantage: for each case, a different matched control sample must be chosen.) • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT Nested Case-Control Design Case-cohort Design Cohort sample Final pop Initial pop • It allows estimating the prevalence of the risk factor in the cohort, and thus the population attributable risk; ARPOP Pr ev RF ( RR 10 . ) 100 Pr ev RF ( RR 10 . ) 10 . • It allows studying correlations between risk factors in the sample for variables not measured in the whole cohort; and Initial pop Final pop “Risk set” • It is best for time-dependent exposures; • It automatically matches for length of follow (and for previous losses). (Disadvantage: for each case, a different matched control sample must be chosen.) • One control group (the cohort sample) can be used for different outcomes. ADVANTAGES OF EACH CASE-CONTROL DESIGN WITHIN THE COHORT • When are nested designs (case-cohort or nested case-control) the best choice? In well defined cohorts when additional (expensive or burdensome) information needs to be collected – Laboratory determination in samples from specimen repository (e.g., serum bank). – Additional record abstraction (e.g., medical, occupational records). • Analytical techniques (analogous to methods used in cohort studies, matched case-control studies) are available. A special type of case-control study: the case-crossover study • Useful when exposures that vary over time can precipitate acute events, such as sudden cardiac deaths, asthma episodes, etc. • Cases serve as their own controls: The subject’s time of event of interest (e.g., death) is the case period, and the subject’s other times comprise the control period •Advantages: –Each participant is considered a matched stratum in a casecontrol study (self-matching) where “cases” and “controls” are case and control times (no control selection bias) – Self-matches for confounding variables that do not change over time (sex, genetic factors, etc.) •Disadvantages: –Assumes no “carry over” (cumulative) effect of exposure of interest –Assumes no confounding or interaction by time-related variables (e.g., ambient temperature, day of the week) •Challenges: –Lag time must be taken into account (relevant exposure period) A special type of case-control study: the casecrossover study– Example: Valent et al, Pediatrics 2001;107:e23 • Objective: to evaluate the association between sleep (and wakefulness) duration and childhood unintentional injury • Sample: 292 unintentionally injured children • Case period: 24 hours preceding injury • Control period: 25-48 hours preceding injury • Definition of exposure: Child slept <10 hs • Analysis: matched-pair and conditional logistic regression • Adjustment: for day of the week (week-end vs. weekday) and activity risk level (higher vs. lower level of energy) Odds Ratios and 95% CIs for Sleeping Less than 10 Hours Study subjects n Ca+ Co+ Ca+ Co- CaCo+ CaCo- OR 95% CI All cases 292 62 26 14 190 1.86 .97, 3.55 Boys 181 40 21 9 111 2.33 1.07, 5.09 Girls 111 22 5 5 79 1.00 0.29, 3.45 (Valent et al, Pediatrics 2001;107:e23) For ascertainment of exposure: •Case period: 24 hours preceding injury •Control period: 25-48 hours preceding injury Threats of Validity in Case-Crossover Studies (Maclure M, Am J Epidemiol 1991;133:144-53) • Within-individual confounding – No confounding by the individual’s characteristics that remain constant, but there can be confounding by variables that vary over time. •Example: A person who drinks coffee only in colder days. If colder days precipitate the event (e.g., angina pectoris), the association with coffee drinking can be explained away by the fact that the day was colder. • Selection bias – Case-crossover study of incident nonfatal myocardial infarction and anger episode (Moller et al, Psychosom Med 1999;61:842-9) – “Survival bias implies that if cases being exposed to anger have a better prognosis for surviving MI than those not exposed to anger, a study of only nonfatal cases would overestimate the relative risk of MI. Likewise, if cases exposed to anger right before their MI are less inclined to participate, this would result in an underestimation.” Threats of Validity in Case-Crossover Studies (cont.) (Maclure M, Am J Epidemiol 1991;133:144-53) • Information bias – Recall bias: When interviews are done at the time of the event, quality of the information obtained from the patient (or a proxy) about the case (hazard) period may differ from that about the control period (e.g., when the case period is the 24-hr period preceding the event, and the control period is the 25 to 48hour preceding the event) • Bias can go in either direction: – Faulty memory regarding the control period – Exaggeration or denial of exposure in the case period • External validity – “In principle, generalizable to all acute-onset outcomes hypothesized to be caused by brief exposures with transient effects.” (Maclure)