cross sectional studies son hee jung 2013/03/25

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