Statistics

Observational studies

date
13.09.2016

Evidence provided by observational studies is reporting an association between factors, not cause and effect.

  1. Ecological studies look for associations between the occurrence of disease and exposure to known or suspected causes. In ecological studies the unit of observation is the population or community. Disease rates and exposures are measured in each of a series of populations and their relation is examined. Often the information about disease and exposure is abstracted from published statistics and therefore does not require expensive or time consuming data collection. The populations compared may be defined in various ways.
  2. In a longitudinal study subjects are followed over time with continuous or repeated monitoring of risk factors or health outcomes, or both. Such investigations vary enormously in their size and complexity. Most longitudinal studies examine associations between exposure to known or suspected causes of disease and subsequent morbidity or mortality. In the simplest design a sample or cohort of subjects exposed to a risk factor is identified along with a sample of unexposed controls. The two groups are then followed up prospectively, and the incidence of disease in each is measured. By comparing the incidence rates, relative risks can be estimated.
  3. A cross sectional study measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. Such information can be used to explore etiology. However, associations must be interpreted with caution. Bias may arise because of selection into or out of the study population. A cross sectional design may also make it difficult to establish what is cause and what is effect.
  4. One of the drawbacks of using a longitudinal approach to investigate the causes of disease with low incidence is that large and lengthy studies may be required to give adequate statistical power. In a case-control study dietary information is collected from a group of people suffering from a disease and this is compared to a matched group of healthy people, for example of a similar age, gender. This permits estimation of odds ratios (but not of relative risks). Allowance is made for potential confounding factors by measuring them and making appropriate adjustments in the analysis. This statistical adjustment may be rendered more efficient by matching cases and controls for exposure to confounders, either on an individual basis (for example by pairing each case with a control of the same age and sex) or in groups (for example, choosing a control group with an overall age and sex distribution similar to that of the cases).
author
- Alpro Foundation

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