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

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

• Dr. Tuan V. Nguyen
• Garvan Institute of Medical Research
• Sydney, Australia

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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?

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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.

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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

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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.

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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

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Modification of Cross-sectional Study I
Trend Design
Future
Sampling
Population
Risk Factor
Present
Disease Prevalence
Risk Factor
Sampling
Disease Prevalence
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Modification of Cross-sectional Study II
Panel Design
Present
Future
Risk Factor
Risk Factor
Population
Disease Prevalence
Disease Prevalence
Same Sample
Sample
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Sample Size
General formula of sample size for 1 group
General formula of sample size for 2 groups
Constant C associated with Type I and Type II
Errors
a b 0.20 (Power 0.80) b 0.10 (Power 0.90) b 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
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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 a0.05) and power
  0.8 (b 0.2).
• Previous data suggest that the standard deviation
  of height was 4.6 cm.
• The sample size is

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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

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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

• 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

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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

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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.

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Analysis of Cross-sectional Studies

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