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 Population Risk Factor Disease Prevalence Risk Factor Disease Prevalence Sample Same Sample PresentFuture 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) 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:

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:

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 = n 1 / n 2 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 = n 2 (so that r = 1) then the fomula is reduced to:

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:

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