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A case-control study starts by identifying people with an outcome (cases) and a comparable group without it (controls), then looks backward to see how their exposures differ. This design is typically retrospective. For example, researchers might take 100 patients with lung cancer (cases) and 100 similar patients without cancer (controls) to investigate past exposures (like asbestos). The key feature is that the study goes from outcome (disease) to exposure. Case-control studies are efficient for rare diseases and can study multiple exposures, but they require careful control selection to avoid bias.

A cohort study follows a group of people (a “cohort”) over time to see how certain exposures (like smoking or a new drug) affect outcomes (like developing lung cancer or symptom relief). There are two main types: prospective (starting now and following forward) and retrospective (using past data to look back). Cohorts are powerful for establishing temporal sequences – you can see if exposure happened before the outcome, which helps infer causation. However, they can be expensive and time-consuming, especially prospective ones. They’re often used to study rare exposures or multiple outcomes from one exposure.

A cross-sectional study examines a population at one specific point in time – like taking a “snapshot” of a group’s health status. These studies measure both exposures (e.g. smoking status) and outcomes (e.g. respiratory symptoms) simultaneously. Cross-sectional designs are often used to estimate the prevalence of a condition or to look for associations (e.g. between diet and obesity) in a sample. However, because exposure and outcome are assessed together, one cannot be sure which came first. This means cross-sectional studies can suggest correlations but usually cannot prove causation.

A randomized controlled trial is an experimental study design in which participants are randomly assigned to receive either an intervention (treatment) or a control (placebo or standard treatment). RCTs are considered the gold standard for testing causal effects of treatments or interventions because randomization, if done properly, balances both known and unknown confounders between groups. Key aspects of RCTs include random sequence generation, allocation concealment, blinding, and intention-to-treat analysis. When appraising an RCT, we focus on how well it prevented bias through these methods.

A systematic review is a structured summary of all relevant studies on a topic, using explicit methods to identify, select, and critically appraise the evidence. A meta-analysis goes further by statistically combining results from similar studies to get a pooled estimate (e.g. overall odds ratio). These are at the top of the evidence hierarchy because they synthesize data from multiple sources, reducing bias and increasing precision. However, their quality depends on the included studies and the review methods. Appraisal focuses on whether the review was comprehensive, transparent, and free of bias (using tools like AMSTAR or PRISMA).

A simple overview of levels of evidence.