Types of observational studies
Case-Control
Retrospective
Subjects with disease of interest are compared to an otherwise similar group that is disease free
Information about risk factor exposure is collected
Meant to determine associations between risk factors and disease occurrence
Can determine an ODDS RATIO but not incidence
Very popular in exploring an exposure-disease association, because they are relatively cheap and less time-consuming than cohort studies.
One major drawback is the fact that risk cannot be derived directly from the results.
The odds ratio IS NOT the same as relative risk, although it can sometimes be approximately equal.
Relative risk can be calculated in follow-up studies by comparing the risk in exposed individuals to the risk in unexposed individuals.
Direct calculation of the relative risk is not possible in case-control studies, because the study design DOES NOT include FOLLOWING PEOPLE OVER TIME.
If the PREVALENCE OF THE DISEASE IS LOW, the odds ratio APPROXIMATES the relative risk. This statement is called the "rare disease assumption". In other words, if the outcome of a case-control study is not common in the population, the odds ratio is close to the relative risk.
Case series
Can be helpful in determining the natural history of uncommon conditions but provides no information about disease incidence
Cohort
Prospective observational
Groups are selected based upon presence or absence of risk factors
Subjects are followed over time to determine the INCIDENCE of something
Cross-sectional
Takes a sample of individuals from a population at one point in time
Allows determination of a disease's PREVALENCE (total number of cases in a population at a given time).
Types of study designs
Factorial design
Two or more different interventions, each with different variables or endpoints (see figure)
Cluster analysis
Grouping of different data points into similar categories
Usually involves randomization at the level of groups rather than at the level of individuals
Cross-over study
A group of participants is randomized to one treatment for a period of time and the other group is given an alternative treatment for the same period of time
At the end of the time period, the two groups then switch treatments for another set period of time
Parallel study
Randomizes one treatment to one group and a different treatment to the other group
Types of bias
Hawthorne effect
The tendency of a study population to affect the outcome because these people are aware that they are being studied. This awareness leads to a consequent change in behavior while under observation, thereby seriously affecting the validity of the study. Hawthorne effect is commonly seen in studies that concern behavioral outcomes or outcomes that can be influenced by behavioral changes. In order to minimize the potential of the Hawthorne effect, studied subjects can be kept unaware that they are being studied; however, this may pose ethical problems. Randomized control trials have a sense of uncertainty and risk due to randomization, which may be more potent behavior modifiers than mere observation.
Confounding
The process of MATCHING is an efficient method to control confounding and is frequently used in case-control studies.
Initial step involes selection of matching variables, which should always be the potential confounders (e.g. age, race).
Cases and controls are then selected based on the matching variables such that both groups have a similar distribution in accordance with the variables.
Selection bias
Obtaining patients that DO NOT REFLECT the populations of interest
Susceptibility bias
A subgroup of selection bias that occurs when the treatment regimen selected for a patient depends on the severity of the patient's condition.
This type of bias fails to take into account other confounding variables that may be accounting for the patient's condition.
This bias also negates the benefits of randomization, which usually avoids selection bias and confounding variables.
Information bias
Occurs due to the imperfect assessment of association between the exposure and outcome as a result of errors in the measurement of exposure and outcome status.
It can be minimized by using standardized techniques for surveillance and measurement of outcomes, as well as trained observers to measure the exposure and outcome.
Lead time bias
Happens when two interventions are compared to diagnose a disease, and one intervention diagnoses the disease earlier than the other without an effect on the outcome (such as survival).
This would make it appear that the intervention prolonged survival when it really just diagnosed th disease sooner.
Measurement bias
Occurs from poor data collection with inaccurate results, which is not what is described in this case.
Ascertainment bias
Results from mislabeling exposed/unexposed or cases/controls
Recall bias
Occurs when a study participant is affected by prior knowledge to answer a question.
More common in case-control studies rather than randomization.
Observer's bias
Occurs when the observer may be influenced by prior knowledge or details of the study that can affect the results.
May be avoided by BLINDING or measuring objective outcomes (such as death).
Statistical Tests
Two sample t-test
Commonly employed to compare two means
Requires the two mean values, the sample variances, and the sample size.
Two sample z-test
Can be used to compare two means
Population (not sample) variances are employed, but since population variances are usually not known, this test is not often used.
ANOVA (i.e. analysis of variance)
Used to compare three or more means
Chi-square test
Appropriate for categorical data and proportions
Meta-analysis
Epidemiologic method of pooling data from several studies to do an analysis having relative big statistical power.
Other issues
Latent period
A natural phenomenon, NOT a bias
Chronic diseases may have a very long latent period, and extended periods of continuous exposure may be necessary to affect the outcome.
