Summary of Introduction to Medical Science
Introduction to Medical Science: Your Guide to Evidence-Based Practice
Introduction
Clinical & Medical Research Methods teach how to plan, run and interpret studies involving people. For a not-attending student, this guide breaks down core concepts, common study designs, threats to validity, practical examples, and a short exercise to practice designing a trial.
Definition: Clinical research methods are systematic approaches to studying health, disease, and interventions in humans to generate reliable evidence for clinical practice.
Core concepts, step by step
1. Observational vs Interventional studies
- Observational studies: researchers observe exposures and outcomes without assigning treatments. Useful to identify associations and natural history.
- Interventional studies (clinical trials): researchers assign an intervention to evaluate its effect on outcomes. Best for establishing causality.
Definition: Observational study — a study where investigators do not intervene but record exposures and outcomes as they occur.
Definition: Interventional (clinical trial) — a study where researchers allocate treatments to evaluate their effects on outcomes.
2. Common observational designs
- Cross-sectional: snapshot measuring exposure and outcome at one time point. Good for prevalence estimates; cannot establish temporality.
- Cohort (longitudinal): groups defined by exposure followed forward to observe outcomes. Can be prospective or retrospective; useful for incidence and risk estimates.
- Case-control: participants selected by outcome status (cases vs controls) and exposure history is compared; efficient for rare outcomes.
Table: Observational designs comparison
| Design | Timing | Best for | Main limitation |
|---|---|---|---|
| Cross-sectional | One time | Prevalence | No temporality |
| Cohort | Prospective/retrospective | Incidence, risk | Time-consuming, loss to follow-up |
| Case-control | Retrospective | Rare outcomes | Recall and selection bias |
3. Randomized Controlled Trials (RCTs)
- Randomization: assigns participants to groups by chance to balance known and unknown confounders.
- Blinding: single-, double-, or triple-blind methods reduce measurement and expectation biases.
- Placebo control: isolates specific effects of the intervention from psychological/placebo effects.
Definition: Randomization — assignment by chance to treatment groups to reduce bias and confounding.
Practical RCT features:
- Eligibility criteria to define who can join
- Allocation concealment to prevent selection bias
- Intention-to-treat analysis to preserve benefits of randomization
4. Outcomes and endpoints
- Primary outcome: the main variable used to judge effectiveness (must be defined before starting the study).
- Secondary outcomes: additional measures of effect or safety (quality of life, biomarkers, adverse events).
- Objective outcomes (e.g., mortality) are less prone to measurement bias than subjective outcomes (e.g., pain scores).
5. Biases and confounding — threats to validity
- Selection bias: non-random selection or differential enrollment; avoid with randomization and clear inclusion criteria.
- Information bias: measurement error or inconsistent data collection; avoid with standardized instruments and training.
- Confounding: external variable associated with both exposure and outcome; address with design (randomization, matching) or analysis (multivariable models).
Definition: Confounder — a variable that is associated with both the exposure and the outcome and can distort the true association.
Table: Typical bias examples and mitigation
| Bias | Example | Mitigation |
|---|---|---|
| Selection bias | Recruiting only young patients | Broaden inclusion, randomiz |
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Clinical Research Methods
Klíčová slova: Medical Publication, Medical Education: Science & Research Training, Clinical & Medical Research Methods, Public Health Epidemiology, Evidence-based Medicine principles, Clinical reasoning and methods, Medical Education: Pedagogy & Critical Thinking, Literature Searching, Research Metrics, Scientific Publishing: Article Types & Formats, Scientific Publishing: Peer Review & Quality Control, Biostatistics, Clinical Tools, Preclinical & Experimental Design, Epidemiology & Study Design, Clinical Trials & Ethics, Evidence Synthesis & EBM, Research Integrity, Scientific Publishing: Communication, Writing & Guidance, Clinical & Medical Research Ethics, Public Health Surveillance & Prevention, Translational Research, Artificial Intelligence for Scientific Research, Artificial Intelligence for Scientific Communication
Klíčové pojmy: Observational vs interventional: observation for associations, trials for causality, Cross-sectional studies estimate prevalence but cannot determine temporality, Cohort studies measure incidence and risk by following exposed and unexposed groups, Case-control studies are efficient for rare outcomes but prone to recall bias, Randomization balances known and unknown confounders across groups, Blinding and placebo controls reduce expectation and measurement biases, Sample size needs effect size, $\alpha$, power, and loss-to-follow-up assumptions, Selection, information bias, and confounding must be anticipated and mitigated, Primary outcome must be pre-specified and clearly defined, Ethical approval, informed consent, and data safety monitoring are mandatory, Use validated measurement tools to reduce information bias, Present design strengths and potential biases and propose mitigation strategies