Study Classification

• Descriptive: Describes characteristics or events.

_Example_: Prevalence of TBI in a population.

• Analytical: Tests hypotheses and looks for associations.

_Example_: Smoking and glioblastoma correlation.

• Exploratory: Investigates new or poorly understood areas.

_Example_: Unusual symptoms in post-COVID patients.

• Explanatory: Attempts to explain mechanisms or causation.

_Example_: Role of IDH mutation in glioma prognosis.

• Cross-sectional: One-time snapshot.

_Pros_: Fast, low-cost.

  
  _Cons_: No temporal or causal inference.
  
* **Case-control**: Retrospective, comparing affected vs. unaffected.  
* 
  _Pros_: Good for rare diseases.  
  
  _Cons_: Recall and selection bias.
  
* **Cohort**: Follows exposed vs. unexposed over time.  
* 
  _Pros_: Strong evidence for causality.  
  
  _Cons_: Expensive, long-term.
  
* **Ecological**: Based on group/population data.  
* 
  _Note_: Risk of ecological fallacy.

• Randomized Controlled Trial (RCT): Gold standard for intervention studies.
• Non-randomized Trial: Allocation not random; higher risk of bias.
• Crossover Trial: Same subjects receive all interventions in sequence.

• Prospective: Follows subjects into the future.
• Retrospective: Uses past data to analyze outcomes.
• Ambispective: Combines both.

• Quantitative: Numerical data (e.g., lab results, scores).
• Qualitative: Textual/descriptive (e.g., interviews, observations).
• Mixed Methods: Combination of both.