gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/hypothesis-generation/references/experimental_design_patterns.md

# Experimental Design Patterns

## Common Approaches to Testing Scientific Hypotheses

This reference provides patterns and frameworks for designing experiments across scientific domains. Use these patterns to develop rigorous tests for generated hypotheses.

## Design Selection Framework

Choose experimental approaches based on:
- **Nature of hypothesis:** Mechanistic, causal, correlational, descriptive
- **System studied:** In vitro, in vivo, computational, observational
- **Feasibility:** Time, cost, ethics, technical capabilities
- **Evidence needed:** Proof-of-concept, causal demonstration, quantitative relationship

## Laboratory Experimental Designs

### In Vitro Experiments

**When to use:** Testing molecular, cellular, or biochemical mechanisms in controlled systems.

**Common patterns:**

#### 1. Dose-Response Studies
- **Purpose:** Establish quantitative relationship between input and effect
- **Design:** Test multiple concentrations/doses of intervention
- **Key elements:**
  - Negative control (no treatment)
  - Positive control (known effective treatment)
  - Multiple dose levels (typically 5-8 points)
  - Technical replicates (≥3 per condition)
  - Appropriate statistical analysis (curve fitting, IC50/EC50 determination)

**Example application:**
"To test if compound X inhibits enzyme Y, measure enzyme activity at 0, 1, 10, 100, 1000 nM compound X concentrations with n=3 replicates per dose."

#### 2. Gain/Loss of Function Studies
- **Purpose:** Establish causal role of specific component
- **Design:** Add (overexpression) or remove (knockout/knockdown) component
- **Key elements:**
  - Wild-type control
  - Gain-of-function condition (overexpression, constitutive activation)
  - Loss-of-function condition (knockout, knockdown, inhibition)
  - Rescue experiment (restore function to loss-of-function)
  - Measure downstream effects

**Example application:**
"Test if protein X causes phenotype Y by: (1) knocking out X and observing phenotype loss, (2) overexpressing X and observing phenotype enhancement, (3) rescuing knockout with X re-expression."

#### 3. Time-Course Studies
- **Purpose:** Understand temporal dynamics and sequence of events
- **Design:** Measure outcomes at multiple time points
- **Key elements:**
  - Time 0 baseline
  - Early time points (capture rapid changes)
  - Intermediate time points
  - Late time points (steady state)
  - Sufficient replication at each time point

**Example application:**
"Measure protein phosphorylation at 0, 5, 15, 30, 60, 120 minutes after stimulus to determine peak activation timing."

### In Vivo Experiments

**When to use:** Testing hypotheses in whole organisms to assess systemic, physiological, or behavioral effects.

**Common patterns:**

#### 4. Between-Subjects Designs
- **Purpose:** Compare different groups receiving different treatments
- **Design:** Randomly assign subjects to treatment groups
- **Key elements:**
  - Random assignment to groups
  - Appropriate sample size (power analysis)
  - Control group (vehicle, sham, or standard treatment)
  - Blinding (single or double-blind)
  - Standardized conditions across groups

**Example application:**
"Randomly assign 20 mice each to vehicle control or drug treatment groups, measure tumor size weekly for 8 weeks, with experimenters blinded to group assignment."

#### 5. Within-Subjects (Repeated Measures) Designs
- **Purpose:** Each subject serves as own control, reducing inter-subject variability
- **Design:** Same subjects measured across multiple conditions/time points
- **Key elements:**
  - Baseline measurements
  - Counterbalancing (if order effects possible)
  - Washout periods (for sequential treatments)
  - Appropriate repeated-measures statistics

**Example application:**
"Measure cognitive performance in same participants at baseline, after training intervention, and at 3-month follow-up."

#### 6. Factorial Designs
- **Purpose:** Test multiple factors and their interactions simultaneously
- **Design:** Cross all levels of multiple independent variables
- **Key elements:**
  - Clear main effects and interactions
  - Sufficient power for interaction tests
  - Full factorial or fractional factorial as appropriate

**Example application:**
"2×2 design crossing genotype (WT vs. mutant) × treatment (vehicle vs. drug) to test whether drug effect depends on genotype."

### Computational/Modeling Experiments

**When to use:** Testing hypotheses about complex systems, making predictions, or when physical experiments are infeasible.

#### 7. In Silico Simulations
- **Purpose:** Model complex systems, test theoretical predictions
- **Design:** Implement computational model and vary parameters
- **Key elements:**
  - Well-defined model with explicit assumptions
  - Parameter sensitivity analysis
  - Validation against known data
  - Prediction generation for experimental testing

**Example application:**
"Build agent-based model of disease spread, vary transmission rate and intervention timing, compare predictions to empirical epidemic data."

#### 8. Bioinformatics/Meta-Analysis
- **Purpose:** Test hypotheses using existing datasets
- **Design:** Analyze large-scale data or aggregate multiple studies
- **Key elements:**
  - Appropriate statistical corrections (multiple testing)
  - Validation in independent datasets
  - Control for confounds and batch effects
  - Clear inclusion/exclusion criteria

**Example application:**
"Test if gene X expression correlates with survival across 15 cancer datasets (n>5000 patients total), using Cox regression with clinical covariates."

## Observational Study Designs

### When Physical Manipulation is Impossible or Unethical

#### 9. Cross-Sectional Studies
- **Purpose:** Examine associations at a single time point
- **Design:** Measure variables of interest in population at one time
- **Strengths:** Fast, inexpensive, can establish prevalence
- **Limitations:** Cannot establish temporality or causation
- **Key elements:**
  - Representative sampling
  - Standardized measurements
  - Control for confounding variables
  - Appropriate statistical analysis

**Example application:**
"Survey 1000 adults to test association between diet pattern and biomarker X, controlling for age, sex, BMI, and physical activity."

#### 10. Cohort Studies (Prospective/Longitudinal)
- **Purpose:** Establish temporal relationships and potentially causal associations
- **Design:** Follow group over time, measuring exposures and outcomes
- **Strengths:** Can establish temporality, calculate incidence
- **Limitations:** Time-consuming, expensive, subject attrition
- **Key elements:**
  - Baseline exposure assessment
  - Follow-up at defined intervals
  - Minimize loss to follow-up
  - Account for time-varying confounders

**Example application:**
"Follow 5000 initially healthy individuals for 10 years, testing if baseline vitamin D levels predict cardiovascular disease incidence."

#### 11. Case-Control Studies
- **Purpose:** Efficiently study rare outcomes by comparing cases to controls
- **Design:** Identify cases with outcome, select matched controls, compare exposures
- **Strengths:** Efficient for rare diseases, relatively quick
- **Limitations:** Recall bias, selection bias, cannot calculate incidence
- **Key elements:**
  - Clear case definition
  - Appropriate control selection (matching or statistical adjustment)
  - Retrospective exposure assessment
  - Control for confounding

**Example application:**
"Compare 200 patients with rare disease X to 400 matched controls without X, testing if early-life exposure Y differs between groups."

## Clinical Trial Designs

#### 12. Randomized Controlled Trials (RCTs)
- **Purpose:** Gold standard for testing interventions in humans
- **Design:** Randomly assign participants to treatment or control
- **Key elements:**
  - Randomization (simple, block, or stratified)
  - Concealment of allocation
  - Blinding (participants, providers, assessors)
  - Intention-to-treat analysis
  - Pre-registered protocol and analysis plan

**Example application:**
"Double-blind RCT: randomly assign 300 patients to receive drug X or placebo for 12 weeks, measure primary outcome of symptom improvement."

#### 13. Crossover Trials
- **Purpose:** Each participant receives all treatments in sequence
- **Design:** Participants crossed over between treatments with washout
- **Strengths:** Reduces inter-subject variability, requires fewer participants
- **Limitations:** Order effects, requires reversible conditions, longer duration
- **Key elements:**
  - Adequate washout period
  - Randomized treatment order
  - Carryover effect assessment

**Example application:**
"Crossover trial: participants receive treatment A for 4 weeks, 2-week washout, then treatment B for 4 weeks (randomized order)."

## Advanced Design Considerations

### Sample Size and Statistical Power

**Key questions:**
- What effect size is meaningful to detect?
- What statistical test will be used?
- What alpha (significance level) and beta (power) are appropriate?
- What is expected variability in the measurement?

**General guidelines:**
- Conduct formal power analysis before experiment
- For pilot studies, n≥10 per group minimum
- For definitive studies, aim for ≥80% power
- Account for potential attrition in longitudinal studies

### Controls

**Types of controls:**
- **Negative control:** No intervention (baseline)
- **Positive control:** Known effective intervention (validates system)
- **Vehicle control:** Delivery method without active ingredient
- **Sham control:** Mimics intervention without active component (surgery, etc.)
- **Historical control:** Prior data (weakest, avoid if possible)

### Blinding

**Levels:**
- **Open-label:** No blinding (acceptable for objective measures)
- **Single-blind:** Participants blinded (reduces placebo effects)
- **Double-blind:** Participants and experimenters blinded (reduces bias in assessment)
- **Triple-blind:** Participants, experimenters, and analysts blinded (strongest)

### Replication

**Technical replicates:** Repeated measurements on same sample
- Reduce measurement error
- Typically 2-3 replicates sufficient

**Biological replicates:** Independent samples/subjects
- Address biological variability
- Critical for generalization
- Minimum: n≥3, preferably n≥5-10 per group

**Experimental replicates:** Repeat entire experiment
- Validate findings across time, equipment, operators
- Gold standard for confirming results

### Confound Control

**Strategies:**
- **Randomization:** Distribute confounds evenly across groups
- **Matching:** Pair similar subjects across conditions
- **Blocking:** Group by confound, then randomize within blocks
- **Statistical adjustment:** Measure confounds and adjust in analysis
- **Standardization:** Keep conditions constant across groups

## Selecting Appropriate Design

**Decision tree:**

1. **Can variables be manipulated?**
   - Yes → Experimental design (RCT, lab experiment)
   - No → Observational design (cohort, case-control, cross-sectional)

2. **What is the system?**
   - Cells/molecules → In vitro experiments
   - Whole organisms → In vivo experiments
   - Humans → Clinical trials or observational studies
   - Complex systems → Computational modeling

3. **What is the primary goal?**
   - Mechanism → Gain/loss of function, dose-response
   - Causation → RCT, cohort study with good controls
   - Association → Cross-sectional, case-control
   - Prediction → Modeling, machine learning
   - Temporal dynamics → Time-course, longitudinal

4. **What are the constraints?**
   - Time limited → Cross-sectional, in vitro
   - Budget limited → Computational, observational
   - Ethical concerns → Observational, in vitro
   - Rare outcome → Case-control, meta-analysis

## Integrating Multiple Approaches

Strong hypothesis testing often combines multiple designs:

**Example: Testing if microbiome affects cognitive function**
1. **Observational:** Cohort study showing association between microbiome composition and cognition
2. **Animal model:** Germ-free mice receiving microbiome transplants show cognitive changes
3. **Mechanism:** In vitro studies showing microbial metabolites affect neuronal function
4. **Clinical trial:** RCT of probiotic intervention improving cognitive scores
5. **Computational:** Model predicting which microbiome profiles should affect cognition

**Triangulation approach:**
- Each design addresses different aspects/limitations
- Convergent evidence from multiple approaches strengthens causal claims
- Start with observational/in vitro, then move to definitive causal tests

## Common Pitfalls

- Insufficient sample size (underpowered)
- Lack of appropriate controls
- Confounding variables not accounted for
- Inappropriate statistical tests
- P-hacking or multiple testing without correction
- Lack of blinding when subjective assessments involved
- Failure to replicate findings
- Not pre-registering analysis plans (clinical trials)

## Practical Application for Hypothesis Testing

When designing experiments to test hypotheses:

1. **Match design to hypothesis specifics:** Causal claims require experimental manipulation; associations can use observational designs
2. **Start simple, then elaborate:** Pilot with simple design, then add complexity
3. **Plan controls carefully:** Controls validate the system and isolate the specific effect
4. **Consider feasibility:** Balance ideal design with practical constraints
5. **Plan for multiple experiments:** Rarely does one experiment definitively test a hypothesis
6. **Pre-specify analysis:** Decide statistical tests before data collection
7. **Build in validation:** Independent replication, orthogonal methods, convergent evidence