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Cross Sectional Studies
Son Hee Jung
2013/03/25
Type of Epidemiological StudiesType of study Alternative name Unit
ExperimentalRCT clinical trial individuals
ObservationalEcological correlational popu-
lationCross sectional prevalence individualsCase-control case-reference indi-
vidualsCohort follow up individuals
Study Designs & Corresponding Ques-tions
• Cross-sectional How common is this disease
or condition?• Ecologic What explains
differences between groups?
• Case-control What factors are associated
with having a disease?
• Prospective How many people will get the disease?
What factors predict development?
Contents
• Definition• Basic approach• Advantage & disadvantage• Sampling• Measures of disease – Prevalence
• Bias
Cross-sectional study-definition
연구대상 집단
요인 노출과 질환에 관한 정보 수집한 시점
연구 진행
Cross Sectional Study
Cross-sectional study- Characteristics
Basic approach
• Include a sample of all persons in a popula-tion at a given time without regard to ex-posure or disease status
• Typically exposure and diseases assessed at that one time
• Exposure subpopulations can be compared with respect to disease prevalence
Basic approach
• For some questions, temporal ordering be-tween exposure and disease is clear and cross sectional studies can test hypothesis– Example: genotype, blood type
• When temporal ordering is not clear can be used to examine relations between expo-sure and outcomes descriptively, and to generate hypotheses
• Can combine a cross sectional study with follow up to create a cohort study
Basic approach
• Issues with addressing etiology– Temporal ordering between exposure
and outcome cannot be assured– Length biased sampling• Cases with long duration will be over
represented
Cross -Sectional Studies: Advantages
• Inexpensive for common diseases• Should be able to get a better response
rate than other study designs• Relatively short study duration• Can be addressed to specific populations
of interest
Cross-Sectional Studies : Disadvan-tages
• Unsuitable for rare or short duration dis-eases
• High refusal rate may make accurate prevalence estimates impossible
• More expensive and time consuming than case-control studies
• No data on temporal relationship between risk factors and disease development
Why sample?
Sampling from the source population
Non-probability sampling• Common convenience sampling methods– Street surveys• Use convenient place such as mall,
hospital–Mail-out questionnaires• Most dangerous• Feel very strongly about the issue-
>bias– Volunteer call• Selection bias
Non-probability sampling-Convenience sampling• Select a sample through an easy, simple or
inexpensive method• Problem– High risk of creating a bias–May provide misleading information– Can be accepted, but…• Be careful in assessing• And the results they produce
Basic probability sampling
• Simple random sampling– Each sample of the chosen size has the
same probability of being selected
Basic probability sampling
• Systematic sampling– Obtain a lost of an available population,
ordered according to an unrelated factor– Pick a number n as step size– Pick every n-th subject of the list
Stratified random sampling
Cluster random sampling
Multistage sampling
The National Health and Nutrition Ex-amination Survey (NHANES)
NHANES Interviews & Examinations
• ㅍ
NHANES Sample Design
Analyses of NHANES Data
Weighting in NHANES
• ㅍ
NHANES base probability of selection
• ㅍ
Oversampling
Sample Weights
Why weight?
Probability weight – simple example
• Imagine 100 male & 100 female in sample
• But only 80 males & 75 females respond
• Male respondent will get weight of – 100/80->1/(80/100)=1.25
• Female respondent will get weight of– 100/75->1/(75/100)=1.33
Example of weighting
국민건강영양조사의 표본추출방법 예
다단계 표본추출
• 단순무작위 표본추출의 실제적 어려움을 해결하기 위해 고안된 방법–전국 규모의 여론조사에 이용– “series” of simple random samples in
stages
• 국민건강영양조사
국가
시도
시군구
읍면동
random sampling
random sampling
random sampling
유병률 산출 : 가중치 적용
• 목적 : 국민건강영양조사의 표본이 우리나라 국민을 대표하도록 가중치를 사용
Direct age adjustment-before
A B
Age group populationNo. of death
Death rates per 100,000
population No. of deathDeath rates per 100,000
All ages 900,000 862 96 900,000 1,130 126
30-49 500,000 60 12 300,000 30 10
50-69 300,000 396 132 400,000 400 100
70+ 100,000 406 406 200,000 700 350
A B
population No. of deathDeath rates per 100,000
population No. of deathDeath rates per 100,000
900,000 862 96 900,000 1,130 126
Direct age adjustment-after
Age groupStandard population
“A" age-specific mortality rates
per 100,000
Expected No. of deaths using
“A" rates
“B" age-specific mortality rates per
100,000
Expected No. of deaths using
“B" rates
All ages 1,800,00030-49 800,000 12 96 10 8050-69 700,000 132 924 100 70070+ 300,000 406 1,218 350 1,050Total 2,238 1,830
Age-adjusted rates 124.3 101.7
Age-adjusted rates: 2238/1800000=124.3 1830/1800000=101.7
A B
population No. of deathDeath rates per 100,000
population No. of deathDeath rates per 100,000
900,000 862 96 900,000 1,130 126
Indirect age adjustment (Standardized Mortality Ratio) • When – number of deaths for each age-specific strata
are not available– Study mortality in an occupational exposure
population
• DefinedObserved number of deaths per year
Expected number of deaths per year
• SMR of 100 • Observed number of deaths is the same as expected
number of deaths
SMR= X100
Sampling, Inference, and generaliza-tion
Sampling, Inference, and generaliza-tion
Sampling, Inference, and generaliza-tion
If you tell the truth you don't have to remember anything. by Mark Twain 1894
Why do we measure disease preva-lence?
Measuring burden: prevalence
Prevalence
Measuring burden: prevalence
Person-time at risk: exposed and un-exposed
Censored individuals
Censoring
Measuring of prevalence
Point and period prevalence: example
Point prevalence at several time points
Period prevalence
Lifetime prevalence
Life time prevalence 4/5
Prevalence of diabetes
Utility of prevalence
Sloppy use of risk
Sloppy use of rate
Classic example of rate that is not a rate
Case fatality(rate?)
Proportional mortality (rate?)
Total deaths united states 2004
Deaths , U.S. 2004 ages 20-24 Years
What ‘s a possible inferential problem with proportional mortality?
Measuring risk: cumulative incidence
Measuring risk: cumulative incidence
Cumulative incidence is a proportion
Calculating the cumulative incidence
Odds
Odds
Odds
Odds
Odds and probabilities
• The higher the incidence, the higher the discrepancy.
Prevalence, Incidence, disease dura-tion
Disease prevalence depends on
Incidence rates can be calculated for each transition in health status
Incidence rates can be calculated for each transition in health status
Relationship among prevalence, inci-dence rate, disease duration at steady state
Relationship among prevalence, inci-dence rate, disease duration at steady state
Relationship among prevalence, inci-dence rate, disease duration at steady state
Mean duration of disease
Relationship among prevalence, inci-dence rate, disease duration at steady state
Relationship among prevalence, inci-dence rate, disease duration at steady state
Relationship among prevalence, inci-dence rate, disease duration at steady state
What does steady state mean in the context of estimating P from I and D?
Example varying prevalence, incidence rates and duration of disease
Cross-sectional Bias
• Incidence-Prevalence bias– Type of selection bias– If exposed cases have different duration that no-exposed
prevalent cases, prevalence ratio will be biased– E.g., cases with severe emphysema more likely to
smoke, have higher fatality than cases with less severe emphysema, so the prevalence of emphysema in smok-ers will be underestimated compare to incidence
– Solution-use incident cases – Duration ratio bias– Point prevalence complement ratio bias
• Temporal bias– Information bias
Incidence-Prevalence bias
• PR 과 IRR 의 관계– Prev= incidence X duration X (1-prev)
* Duration ratio bias * Point prevalence complement ratio bias
PR
Duration ratio bias
• Type of selection bias• 드문 질환에서 이환기간이 노출여부와 상관없이
동일하다면 비뚤림 발생하지 않음• 노출여부에 따라 질병 이환기간이 다를 때 발생• 만성질환의 경우 질병의 duration 이
생존기간과 관련이 있기 때문에 이런 경우 생기는 bias 가 survival bias
Point prevalence complement ratio bias
• 이환기간이 동일하다면 , PR 이 IRR 을 과소측정하는 경향이 발생
• 노출그룹의 유병률 : 0.04, 비노출그룹 유병률 : 0.01 PR : 4 Point prevalence complement
ratio=0.96/0.99=0.97• 노출그룹의 유병률 : 0.4, 비노출그룹 유병률 : 0.1 PR : 4 Point prevalence complement
ratio=0.6/0.9=0.67• PR, 유병률 크면 → bias 크기 커짐
Selection bias -- Berkson’s bias
• Admission-rate bias • Cases and/or controls selected from hospitals• Result from differential rates of hospital admission for cases
and controls• If hospital based cases and controls have different expo-
sures that population based, OR will be biased.• E.g., If hospital controls are less likely to have exposures, OR
will be over-estimated. • E.g., Case control for pancreatic cancer and coffee drinking:
Controls were selected from GI patients. However, GI pa-tients are less likely to drink coffee that population. OR was artificially increased.
• Solution: use population based control, or controls with dis-ease not related to the exposure
Temporal bias• 시간적 선후관계가 모호– 질병의 위험요인 검정 측면에서의 결정적 단점– 예 : 영양결핍과 우울증 연구– 시간적 경과에 따른 변동이 없는 노출요인의
경우에는 이러한 제한점에 구애 받지 않음 – 유전적 요인
• 시간적 선후관계가 뒤집어져 있는 연구는 비추– 예 : 가설 ) 식이요인이 초경나이에 미치는 영향 대상 ) 중년여성을 대상으로 초경나이와 최근
의 식이습관 조사
• 전체 유병환자 중 Incident cases 만 포함하여 분석함으로 단점을 최소화 또 다른 bias ?
• Historical information 으로 단점 최소화
screening is most likely to pick up less aggres-sive cancers, because they have a longer inter-val of being visible on scans while remaining asymptomatic
you find out something earlier but don’t actu-ally change the outcome, and therefore the apparent survival after diagnosis is longer without better survival
Simpson’s paradox
aggregated
disaggregated
Simpson’s paradox
• Aggregated and disaggregated data tell two different sto-ries
치료 종류 환자 수 성 공 실 패 성공률 (%)
합계 (n=700)
개복술 350 273 77 78
경피술 350 289 61 83
돌의 크기 < 2cm (n=357)
개복술 87 81 6 93
경피술 270 234 36 87
돌의 크기 ≥ 2cm (n=343)
개복술 263 192 71 73
경피술 80 55 25 69
단면조사연구 정리
특정 시점 또는 짧은 기간 동안 표본 추출조사 – “스냅 사진”
장점 편리하고 비용 효과적 여러 노출과 질병 연구 가능 가설 생성 가능 일반적 인구집단을 대표
단점 시간적 선후관계 모호 생존자만 연구 , 비뚤림 가능 짧은 이환 기간의 질환은 과소측정
Any question?
If you tell the truth you don't have to remember any-thing. by Mark Twain 1894