Study design: Observational Study Designs: Introduction learnonline
Table Of Content
When designing or evaluating a study it may be helpful to review the applicable standards prior to executing and publishing the study. All published standards and guidelines are available on the web, and are updated based on current best practices as biomedical research evolves. Assessment timing can play an important role in the impact of interventions, particularly if intervention effects are acute and short lived (26–29,33). The specific timing of assessments are unique to each intervention, however, studies that allow for meaningfully different timing of assessments are subject to erroneous results. By tracking differences in assessment times, researchers can address the potential scope of this problem, and try to address it using statistical or other methods (26–28,33).
Limitations of cohort studies
The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants. The appropriate selection of a study design is only one element in successful research. Reviewing appropriate published standards when designing a study can substantially strengthen the execution and interpretation of study results. Co-interventions, interventions that impact the outcome other than the primary intervention of the study, can also allow for erroneous conclusions in clinical trials (26–28). If there are differences between treatment arms in the amount or type of additional therapeutic elements then the study conclusions may be incorrect (29). For example, if a placebo treatment arm utilizes more over-the-counter medication than the experimental treatment arm, both treatment arms may have the same therapeutic improvement and show no effect of the experimental treatment.
Study design
Observational studies draw inferences about the effect of an “exposure” or intervention on subjects, where the assignment of subjects to groups is observed rather than manipulated (e.g., through randomization) by the investigator. Observational research involves the direct observation of individuals in their natural setting. As such, who does or does not receive an intervention is determined by individual preferences, practice patterns, or policy decisions.3 It is therefore important for readers of observational research to consider if alternative explanations for study results exist. This issue (known as “confounding”) is a primary challenge of observational research and will be discussed in detail in the next paper in this series.
Outcome
First, the primary limitation of the cross-sectional study design is that because the exposure and outcome are simultaneously assessed, there is generally no evidence of a temporal relationship between exposure and outcome. That is, although the investigator may determine that there is an association between an exposure and an outcome, there is generally no evidence that the exposure caused the outcome. Second, a cross-sectional study evaluates prevalent rather than incident outcomes and thus excludes people who develop the outcome but die before the study. The measured association in a cross-sectional study is between exposure and having the outcome as opposed to exposure and developing the outcome.
Advantages and Disadvantages of Observational Study Design
New and ongoing developments in data and analytical technology, such as data linkage and propensity score matching, offer a promising future for observational studies. However, no study design or statistical method can account for confounders and bias in the way that randomised controlled trials can. Observational studies in clinical research can be classified as either analytic or descriptive (Table 12–1). Analytic observational studies are similar to randomized, controlled clinical trials in that the goal is to estimate the causal effect of an exposure on an outcome. Also similar to trials, analytic observational studies always include some type of comparison group, against which the experience of the exposed group is compared.
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Instead, these studies are often used to generate study questions that can then be tested by more rigorous methods. Study design plays an important role in the quality, execution, and interpretation of biomedical and public health research (1–12). Each study design has their own inherent strengths and weaknesses, and there can be a general hierarchy in study designs, however, any hierarchy cannot be applied uniformly across study design types (3,5,6,9).
Step 2: Choose your observation type and technique
Randomised trials using MALO as an outcome might be unfeasible, motivating further large observational studies using appropriate methodology to further delineate the effect of GLP1 agonists on the risk of MALO, complementing future phase III trials of GLP1 agonists. The selection of a study design is the most critical step in the research methodology. Crucial factors should be considered during the selection of the study design, which is the formulated research question, as well as the method of participant selection. Observational design occupies the middle and lower parts of the hierarchy of evidence-based pyramid.
This is when a cohort might share other characteristics that affect the outcome versus the outcome stated in the study. An example would be that people who practice good sleeping habits have less heart disease. But, maybe those who practice effective sleeping habits also, in general, eat better and exercise more.
Methods
Randomisation in clinical trials also minimises selection bias, while blinding (masking) controls for information bias. Hence, for questions regarding drug efficacy, randomised controlled trials provide the most robust evidence. Randomized controlled trials (RCTs) are the most common type of interventional study, and can have many modifications (26–28).
The exposure is cell phone use during the exposure periods, both before the crash and during the control period. Additionally, the reliance upon prior exposure time requires that the exposure not have an additive or cumulative effect over time (1,5). Case-crossover study designs are at higher risk for having recall bias as compared with other study designs (12). Study participants are more likely to remember an exposure prior to becoming a case, as compared to not becoming a case.
Gold standard diagnostic procedures are the current best-practice for diagnosing a disease. An example is comparing a new rapid test for a cancer with the gold standard method of biopsy. There are many intricacies to diagnostic testing study designs that should be considered. The proper selection of the gold standard evaluation is important for defining the true measures of accuracy for the new diagnostic procedure. Evaluations of diagnostic test results should be blinded to the case status of the participant.
Typically, cases are identified from hospital records, death certificates or disease registries. Large cohort studies, such as the Framingham Heart Study or the Nurses' Health Study, have yielded extremely useful information about risk factors for several chronic diseases. Clinical registries can also be used to undertake clinical trials which are nested within the registry architecture. Patients within a registry are randomised to interventions and comparators of interest.
The measure of exposure in epidemiologic studies may be tobacco use (“Yes” vs. “No”) to define the two groups and may be the treatment (Active drug vs. placebo) in interventional studies. Health outcome(s) can be the development of a disease or symptom (e.g. lung cancer) or curing a disease or symptom (e.g. reduction of pain). Descriptive studies, which are not epidemiological or interventional, lack one or more of these elements and have limited application. High quality epidemiological and interventional studies contain detailed information on the design, execution and interpretation of results, with methodology clearly written and able to be reproduced by other researchers. Interventional studies are often prospective and are specifically tailored to evaluate direct impacts of treatment or preventive measures on disease. Correlational studies (ecologic studies) explore the statistical relationships between the outcome of interest in population and estimate the exposures.
Case-control and cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a temporal dimension (i.e. prospective or retrospective study design). In this review, we will primarily discuss cohort and case-control study designs and related methodologic issues. Observational study designs, also called epidemiologic study designs, are often retrospective and are used to assess potential causation in exposure-outcome relationships and therefore influence preventive methods. These include diagnostic accuracy designs, diagnostic cohort designs, and diagnostic randomized controlled trials. The use of observational research methods in the field of palliative care is vital to building the evidence base, identifying best practices, and understanding disparities in access to and delivery of palliative care services.
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