Design and Analysis of Clinical Study 8. Cross-sectional Study

Dr. Tuan V. Nguyen

Garvan Institute of Medical Research

Sydney, Australia

Cross-sectional Study

• Cross-sectional studies are studies of prevalence. Proportion with an attribute or disease / Number of subjects = Prevalence.

• 3 important questions to consider:– Definition of Case

– Definition of the Population

– Are cases and non-cases from an unbiased sample of the population?

Preparing Cross-sectional Study

• In Cross-sectional studies think of:– Sampling Procedures.

– Clear definition of Target Population.

– Clear definition of outcome.

– Clear definition of risk factors.

– Remember Confounders.

– Remember seasonal variations.

Uses of Cross-sectional Study

• Identify and describe a problem

• Collect information for planning e.g. surveys of immunisation, antenatal care, coverage

• Evaluate utilisation rates of services

• Monitoring health status of a community by regular repeated surveys

Using Cross-sectional Studies for Hypotheses Formulation

Method of Difference. If frequency of a disease is markedly different between two groups then

it is likely to be caused by a particular factor that differs between them.

Method of Agreement. If a factor commonly occurs in which a disease occurs with high

frequency then the factor is very likely associated with the disease.

Concomitant variation. Frequency of a factor varies in

proportion to frequency of disease.

Surveys

Surveys are a form of cross-sectional studies used for:

• Assessing attitudes, opinions or beliefs

• To study characteristics of populations regarding behaviour e.g. health service utilisation; drug use; smoking; alcohol consumption etc.

• Information about socio-demographic characteristics

Modification of Cross-sectional Study I

Trend Design

Population

Disease Prevalence

Risk Factor

Present

Sampling

Future

Risk Factor

Disease Prevalence

Sampling

Modification of Cross-sectional Study II

PopulationRisk

Factor

Disease Prevalence

Risk Factor

Disease Prevalence

Sample Same Sample

Present Future

Panel Design

Sample Size

Constant C associated with Type I and Type II Errors

= = 0.20(Power = 0.80)

= 0.10 (Power = 0.90)

= 0.05(Power = 0.95)

0.10 6.15 8.53 10.79

0.05 7.85 10.51 13.00

0.01 13.33 16.74 19.84

2/

Cn

22

/

Cn

General formula of sample size for 1 group:

General formula of sample size for 2 groups:

Sample Size for One Parameter

• Case 1. We want to estimate the average height of Vietnamese men.

– We accept an error of 1 cm (d = 1)

– 95% confidence interval (or =0.05) and power = 0.8 ( = 0.2).

– Previous data suggest that the standard deviation of height was 4.6 cm.

• The sample size is:

2 2

7.85166

/ 1/ 4.6

Cn

Sample Size for One Parameter

• Case 2. We want to estimate the prevalence of smokers in the population.

– We accept an error of 2%

– Previous data suggest that the prevalence is around 70%

• The minimal sample size is 2017:

21.96

0.7 0.30.02

n

Sample Size for Comparing Two Groups

• In case-control study the data are usually summarized by an odds ratio (OR), rather then difference between two proportions.

• If p1 and p2 are the proportions of cases and controls, respectively, exposed to a risk factor, then:

12

21

1

1

pp

ppOR

ppORr

ZZrN

1ln

12

2

1

2

• If we know the proportion of exposure in the general population (p), the total sample size N for estimating an OR is:

• Where r = n1 / n2 is the ratio of sample sizes for group 1 and group2; p is the prevalence of exposure in the controls; and OR is the hypothetical odds ratio. If n1 = n2 (so that r = 1) then the fomula is reduced to:

ppOR

ZZN

1ln

42

2

1

Sample Size for Comparing Two Groups

• Example: The prevalence of vertebral fracture in a population is 25%. It is interested to estimate the effect of smoking on the fracture, with an odds ratio of 2, at the significance level of 5% (one-sided test) and power of 80%.

• The total sample size for the study can be estimated by:

27575.025.02ln

85.064.142

2

N

Advantages and Disadvantages of Cross- sectional Studies

Advantages

• Useful for descriptive studies

• Rapid, inexpensive, can provide analytic clues.

• Less prone to error about exposure recall and bias

Disadvantages

• Unable to sort out what came first exposure or outcome

• Prone to sample distortion bias.

Analysis of Cross-sectional Studies

• Descriptive analyses• Analysis of differences• Analysis of association / relationship• Multivariable analysis