# Evidence Synthesis and Guideline Integration Guide

## Overview

Evidence synthesis involves systematically reviewing, analyzing, and integrating research findings to inform clinical recommendations. This guide covers guideline sources, evidence hierarchies, systematic reviews, meta-analyses, and integration of multiple evidence streams for clinical decision support.

## Major Clinical Practice Guidelines

### Oncology Guidelines

**NCCN (National Comprehensive Cancer Network)**
- **Scope**: 60+ cancer types, supportive care guidelines
- **Update Frequency**: Continuous (online), 1-3 updates per year per guideline
- **Evidence Categories**:
  - **Category 1**: High-level evidence, uniform NCCN consensus
  - **Category 2A**: Lower-level evidence, uniform consensus (appropriate)
  - **Category 2B**: Lower-level evidence, non-uniform consensus (appropriate)
  - **Category 3**: Major disagreement or insufficient evidence
- **Access**: Free for patients, subscription for providers (institutional access common)
- **Application**: US-focused, most widely used in clinical practice

**ASCO (American Society of Clinical Oncology)**
- **Scope**: Evidence-based clinical practice guidelines
- **Methodology**: Systematic review, GRADE-style evidence tables
- **Endorsements**: Often endorses NCCN, ESMO, or other guidelines
- **Focused Topics**: Specific clinical questions (e.g., biomarker testing, supportive care)
- **Guideline Products**: Full guidelines, rapid recommendations, endorsements
- **Quality**: Rigorous methodology, peer-reviewed publication

**ESMO (European Society for Medical Oncology)**
- **Scope**: European guidelines for cancer management
- **Evidence Levels**:
  - **I**: Evidence from at least one large RCT or meta-analysis
  - **II**: Evidence from at least one well-designed non-randomized trial, cohort study
  - **III**: Evidence from well-designed non-experimental study
  - **IV**: Evidence from expert committee reports or opinions
  - **V**: Evidence from case series, case reports
- **Recommendation Grades**:
  - **A**: Strong evidence for efficacy, substantial clinical benefit (strongly recommended)
  - **B**: Strong or moderate evidence, limited clinical benefit (generally recommended)
  - **C**: Insufficient evidence, benefit not sufficiently well established
  - **D**: Moderate evidence against efficacy or for adverse effects (not recommended)
  - **E**: Strong evidence against efficacy (never recommended)
- **ESMO-MCBS**: Magnitude of Clinical Benefit Scale (grades 1-5 for meaningful benefit)

### Cardiovascular Guidelines

**AHA/ACC (American Heart Association / American College of Cardiology)**
- **Scope**: Cardiovascular disease prevention, diagnosis, management
- **Class of Recommendation (COR)**:
  - **Class I**: Strong recommendation - should be performed/administered
  - **Class IIa**: Moderate recommendation - is reasonable
  - **Class IIb**: Weak recommendation - may be considered
  - **Class III - No Benefit**: Not recommended
  - **Class III - Harm**: Potentially harmful
- **Level of Evidence (LOE)**:
  - **A**: High-quality evidence from >1 RCT, meta-analyses
  - **B-R**: Moderate-quality evidence from ≥1 RCT
  - **B-NR**: Moderate-quality evidence from non-randomized studies
  - **C-LD**: Limited data from observational studies, registries
  - **C-EO**: Expert opinion based on clinical experience
- **Example**: "Statin therapy is recommended for adults with LDL-C ≥190 mg/dL (Class I, LOE A)"

**ESC (European Society of Cardiology)**
- **Scope**: European cardiovascular guidelines
- **Class of Recommendation**:
  - **I**: Recommended or indicated
  - **II**: Should be considered
  - **III**: Not recommended
- **Level of Evidence**: A (RCTs), B (single RCT or observational), C (expert opinion)

### Other Specialties

**IDSA (Infectious Diseases Society of America)**
- Antimicrobial guidelines, infection management
- GRADE methodology
- Strong vs weak recommendations

**ATS/ERS (American Thoracic Society / European Respiratory Society)**
- Respiratory disease management
- GRADE methodology

**ACR (American College of Rheumatology)**
- Rheumatic disease guidelines
- Conditionally recommended vs strongly recommended

**KDIGO (Kidney Disease: Improving Global Outcomes)**
- Chronic kidney disease, dialysis, transplant
- GRADE-based recommendations

## GRADE Methodology

### Assessing Quality of Evidence

**Initial Quality Assignment**

**Randomized Controlled Trials**: Start at HIGH quality (⊕⊕⊕⊕)

**Observational Studies**: Start at LOW quality (⊕⊕○○)

### Factors Decreasing Quality (Downgrade)

**Risk of Bias** (-1 or -2 levels)
- Lack of allocation concealment
- Lack of blinding
- Incomplete outcome data
- Selective outcome reporting
- Other sources of bias

**Inconsistency** (-1 or -2 levels)
- Unexplained heterogeneity in results across studies
- Wide variation in effect estimates
- Non-overlapping confidence intervals
- High I² statistic in meta-analysis (>50-75%)

**Indirectness** (-1 or -2 levels)
- Different population than target (younger patients in trials, applying to elderly)
- Different intervention (higher dose in trial than used in practice)
- Different comparator (placebo in trial, comparing to active treatment)
- Surrogate outcomes (PFS) when interested in survival (OS)

**Imprecision** (-1 or -2 levels)
- Wide confidence intervals crossing threshold of benefit/harm
- Small sample size, few events
- Optimal information size (OIS) not met
- Rule of thumb: <300 events for continuous outcomes, <200 events for dichotomous

**Publication Bias** (-1 level)
- Funnel plot asymmetry (if ≥10 studies)
- Known unpublished studies with negative results
- Selective outcome reporting
- Industry-sponsored studies only

### Factors Increasing Quality (Upgrade - Observational Only)

**Large Magnitude of Effect** (+1 or +2 levels)
- +1: RR >2 or <0.5 (moderate effect)
- +2: RR >5 or <0.2 (large effect)
- No plausible confounders would reduce effect

**Dose-Response Gradient** (+1 level)
- Clear dose-response or duration-response relationship
- Strengthens causal inference

**All Plausible Confounders Would Reduce Effect** (+1 level)
- Observed effect despite confounders biasing toward null
- Rare, requires careful justification

### Final Quality Rating

After adjustments, assign final quality:
- **High (⊕⊕⊕⊕)**: Very confident in effect estimate
- **Moderate (⊕⊕⊕○)**: Moderately confident; true effect likely close to estimate
- **Low (⊕⊕○○)**: Limited confidence; true effect may be substantially different
- **Very Low (⊕○○○)**: Very little confidence; true effect likely substantially different

## Systematic Reviews and Meta-Analyses

### PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)

**Search Strategy**
- **Databases**: PubMed/MEDLINE, Embase, Cochrane Library, Web of Science
- **Search Terms**: PICO (Population, Intervention, Comparator, Outcome)
- **Date Range**: Typically last 10-20 years or comprehensive
- **Language**: English only or all languages with translation
- **Grey Literature**: Conference abstracts, trial registries, unpublished data

**Study Selection**
```
PRISMA Flow Diagram:

Records identified through database searching (n=2,450)
Additional records through other sources (n=15)
                ↓
Records after duplicates removed (n=1,823)
                ↓
Records screened (title/abstract) (n=1,823)  → Excluded (n=1,652)
                ↓                                 - Not relevant topic (n=1,120)
Full-text articles assessed (n=171)              - Animal studies (n=332)
                ↓                                 - Reviews (n=200)
Studies included in qualitative synthesis (n=38) → Excluded (n=133)
                ↓                                 - Wrong population (n=42)
Studies included in meta-analysis (n=24)          - Wrong intervention (n=35)
                                                  - No outcomes reported (n=28)
                                                  - Duplicate data (n=18)
                                                  - Poor quality (n=10)
```

**Data Extraction**
- Study characteristics: Design, sample size, population, intervention
- Results: Outcomes, effect sizes, confidence intervals, p-values
- Quality assessment: Risk of bias tool (Cochrane RoB 2.0 for RCTs)
- Dual extraction: Two reviewers independently, resolve disagreements

### Meta-Analysis Methods

**Fixed-Effect Model**
- **Assumption**: Single true effect size shared by all studies
- **Weighting**: By inverse variance (larger studies have more weight)
- **Application**: When heterogeneity is low (I² <25%)
- **Interpretation**: Estimate of common effect across studies

**Random-Effects Model**
- **Assumption**: True effect varies across studies (distribution of effects)
- **Weighting**: By inverse variance + between-study variance
- **Application**: When heterogeneity moderate to high (I² ≥25%)
- **Interpretation**: Estimate of average effect (center of distribution)
- **Wider CI**: Accounts for heterogeneity, more conservative

**Heterogeneity Assessment**

**I² Statistic**
- Percentage of variability due to heterogeneity rather than chance
- I² = 0-25%: Low heterogeneity
- I² = 25-50%: Moderate heterogeneity
- I² = 50-75%: Substantial heterogeneity
- I² = 75-100%: Considerable heterogeneity

**Q Test (Cochran's Q)**
- Test for heterogeneity
- p<0.10 suggests significant heterogeneity (liberal threshold)
- Low power when few studies, use I² as primary measure

**Tau² (τ²)**
- Estimate of between-study variance
- Used in random-effects weighting

**Subgroup Analysis**
- Explore sources of heterogeneity
- Pre-specified subgroups: Disease stage, biomarker status, treatment regimen
- Test for interaction between subgroups

**Forest Plot Interpretation**
```
Study               n     HR (95% CI)          Weight
─────────────────────────────────────────────────────────────
Trial A 2018        450   0.62 (0.45-0.85)     ●───┤      28%
Trial B 2019        320   0.71 (0.49-1.02)      ●────┤     22%
Trial C 2020        580   0.55 (0.41-0.74)    ●──┤       32%
Trial D 2021        210   0.88 (0.56-1.38)        ●──────┤  18%

Overall (RE model)  1560  0.65 (0.53-0.80)      ◆──┤
Heterogeneity: I²=42%, p=0.16

                          0.25  0.5  1.0  2.0  4.0
                                Favors Treatment  Favors Control
```

## Guideline Integration

### Concordance Checking

**Multi-Guideline Comparison**
```
Recommendation: First-line treatment for advanced NSCLC, PD-L1 ≥50%

Guideline    Version   Recommendation                               Strength
─────────────────────────────────────────────────────────────────────────────
NCCN         v4.2024   Pembrolizumab monotherapy (preferred)       Category 1
ESMO         2023      Pembrolizumab monotherapy (preferred)       I, A
ASCO         2022      Endorses NCCN guidelines                    Strong
NICE (UK)    2023      Pembrolizumab approved                      Recommended

Synthesis: Strong consensus across guidelines for pembrolizumab monotherapy.
Alternative: Pembrolizumab + chemotherapy also Category 1/I-A recommended.
```

**Discordance Resolution**
- Identify differences and reasons (geography, cost, access, evidence interpretation)
- Note date of each guideline (newer may incorporate recent trials)
- Consider regional applicability
- Favor guidelines with most rigorous methodology (GRADE-based)

### Regulatory Approval Landscape

**FDA Approvals**
- Track indication-specific approvals
- Accelerated approval vs full approval
- Post-marketing requirements
- Contraindications and warnings

**EMA (European Medicines Agency)**
- May differ from FDA in approved indications
- Conditional marketing authorization
- Additional monitoring (black triangle)

**Regional Variations**
- Health Technology Assessment (HTA) agencies
- NICE (UK): Cost-effectiveness analysis, QALY thresholds
- CADTH (Canada): Therapeutic review and recommendations
- PBAC (Australia): Reimbursement decisions

## Real-World Evidence (RWE)

### Sources of RWE

**Electronic Health Records (EHR)**
- Clinical data from routine practice
- Large patient numbers
- Heterogeneous populations (more generalizable than RCTs)
- Limitations: Missing data, inconsistent documentation, selection bias

**Claims Databases**
- Administrative claims for billing/reimbursement
- Large scale (millions of patients)
- Outcomes: Mortality, hospitalizations, procedures
- Limitations: Lack clinical detail (labs, imaging, biomarkers)

**Cancer Registries**
- **SEER (Surveillance, Epidemiology, and End Results)**: US cancer registry
- **NCDB (National Cancer Database)**: Hospital registry data
- Population-level survival, treatment patterns
- Limited treatment detail, no toxicity data

**Prospective Cohorts**
- Framingham Heart Study, Nurses' Health Study
- Long-term follow-up, rich covariate data
- Expensive, time-consuming

### RWE Applications

**Comparative Effectiveness**
- Compare treatments in real-world settings (less strict eligibility than RCTs)
- Complement RCT data with broader populations
- Example: Effectiveness of immunotherapy in elderly, poor PS patients excluded from trials

**Safety Signal Detection**
- Rare adverse events not detected in trials
- Long-term toxicities
- Drug-drug interactions in polypharmacy
- Postmarketing surveillance

**Treatment Patterns and Access**
- Guideline adherence in community practice
- Time to treatment initiation
- Disparities in care delivery
- Off-label use prevalence

**Limitations of RWE**
- **Confounding by indication**: Sicker patients receive more aggressive treatment
- **Immortal time bias**: Time between events affecting survival estimates
- **Missing data**: Incomplete or inconsistent data collection
- **Causality**: Association does not prove causation without randomization

**Strengthening RWE**
- **Propensity score matching**: Balance baseline characteristics between groups
- **Multivariable adjustment**: Adjust for measured confounders in Cox model
- **Sensitivity analyses**: Test robustness to unmeasured confounding
- **Instrumental variables**: Use natural experiments to approximate randomization

## Meta-Analysis Techniques

### Binary Outcomes (Response Rate, Event Rate)

**Effect Measures**
- **Risk Ratio (RR)**: Ratio of event probabilities
- **Odds Ratio (OR)**: Ratio of odds (less intuitive)
- **Risk Difference (RD)**: Absolute difference in event rates

**Example Calculation**
```
Study 1:
- Treatment A: 30/100 responded (30%)
- Treatment B: 15/100 responded (15%)
- RR = 0.30/0.15 = 2.0 (95% CI 1.15-3.48)
- RD = 0.30 - 0.15 = 0.15 or 15% (95% CI 4.2%-25.8%)
- NNT = 1/RD = 1/0.15 = 6.7 (treat 7 patients to get 1 additional response)
```

**Pooling Methods**
- **Mantel-Haenszel**: Common fixed-effect method
- **DerSimonian-Laird**: Random-effects method
- **Peto**: For rare events (event rate <1%)

### Time-to-Event Outcomes (Survival, PFS)

**Hazard Ratio Pooling**
- Extract HR and 95% CI (or log(HR) and SE) from each study
- Weight by inverse variance
- Pool using generic inverse variance method
- Report pooled HR with 95% CI, heterogeneity statistics

**When HR Not Reported**
- Extract from Kaplan-Meier curves (Parmar method, digitizing software)
- Calculate from log-rank p-value and event counts
- Request from study authors

### Continuous Outcomes (Quality of Life, Lab Values)

**Standardized Mean Difference (SMD)**
- Application: Different scales used across studies
- SMD = (Mean₁ - Mean₂) / Pooled SD
- Interpretation: Cohen's d effect size (0.2 small, 0.5 medium, 0.8 large)

**Mean Difference (MD)**
- Application: Same scale/unit used across studies
- MD = Mean₁ - Mean₂
- More directly interpretable than SMD

## Network Meta-Analysis

### Purpose

Compare multiple treatments simultaneously when no head-to-head trials exist

**Example Scenario**
- Drug A vs placebo (Trial 1)
- Drug B vs placebo (Trial 2)  
- Drug C vs Drug A (Trial 3)
- **Question**: How does Drug B compare to Drug C? (no direct comparison)

### Methods

**Fixed-Effect Network Meta-Analysis**
- Assumes consistency (transitivity): A vs B effect = (A vs C effect) - (B vs C effect)
- Provides indirect comparison estimates
- Ranks treatments by P-score or SUCRA

**Random-Effects Network Meta-Analysis**
- Allows heterogeneity between studies
- More conservative estimates

**Consistency Checking**
- Compare direct vs indirect evidence for same comparison
- Node-splitting analysis
- Loop consistency (if closed loops in network)

### Interpretation Cautions

- **Transitivity assumption**: May not hold if studies differ in important ways
- **Indirect evidence**: Less reliable than direct head-to-head trials
- **Rankings**: Probabilistic, not definitive ordering
- **Clinical judgment**: Consider beyond statistical rankings

## Evidence Tables

### Constructing Evidence Summary Tables

**PICO Framework**
- **P (Population)**: Patient characteristics, disease stage, biomarker status
- **I (Intervention)**: Treatment regimen, dose, schedule
- **C (Comparator)**: Control arm (placebo, standard of care)
- **O (Outcomes)**: Primary and secondary endpoints

**Evidence Table Template**
```
Study         Design  n    Population      Intervention vs Comparator   Outcome            Result                Quality
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Smith 2020    RCT     450  Advanced NSCLC  Drug A 10mg vs               Median PFS         12 vs 6 months        High
                           EGFR+           standard chemo               (95% CI)           (10-14 vs 5-7)        ⊕⊕⊕⊕
                                                                        HR (95% CI)        0.48 (0.36-0.64)
                                                                        p-value            p<0.001

                                                                        ORR                65% vs 35%  
                                                                        Grade 3-4 AEs      42% vs 38%

Jones 2021    RCT     380  Advanced NSCLC  Drug A 10mg vs               Median PFS         10 vs 5.5 months      High
                           EGFR+           placebo                      HR (95% CI)        0.42 (0.30-0.58)      ⊕⊕⊕⊕
                                                                        p-value            p<0.001

Pooled Effect                                                          Pooled HR          0.45 (0.36-0.57)      High
(Meta-analysis)                                                        I²                 12% (low heterogeneity) ⊕⊕⊕⊕
```

### Evidence to Decision Framework

**Benefits and Harms**
- Magnitude of desirable effects (ORR, PFS, OS improvement)
- Magnitude of undesirable effects (toxicity, quality of life impact)
- Balance of benefits and harms
- Net benefit calculation

**Values and Preferences**
- How do patients value outcomes? (survival vs quality of life)
- Variability in patient values
- Shared decision-making importance

**Resource Considerations**
- Cost of intervention
- Cost-effectiveness ($/QALY)
- Budget impact
- Equity and access

**Feasibility and Acceptability**
- Is treatment available in practice settings?
- Route of administration feasible? (oral vs IV vs subcutaneous)
- Monitoring requirements realistic?
- Patient and provider acceptability

## Guideline Concordance Documentation

### Synthesizing Multiple Guidelines

**Concordant Recommendations**
```
Clinical Question: Treatment for HER2+ metastatic breast cancer, first-line

Guideline Summary:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
NCCN v3.2024 (Category 1):
  Preferred: Pertuzumab + trastuzumab + taxane
  Alternative: T-DM1, other HER2-targeted combinations

ESMO 2022 (Grade I, A):
  Preferred: Pertuzumab + trastuzumab + docetaxel
  Alternative: Trastuzumab + chemotherapy (if pertuzumab unavailable)

ASCO 2020 Endorsement:
  Endorses NCCN guidelines, recommends pertuzumab-based first-line

Synthesis:
  Strong consensus for pertuzumab + trastuzumab + taxane as first-line standard.
  Evidence: CLEOPATRA trial (Swain 2015): median OS 56.5 vs 40.8 months (HR 0.68, p<0.001)

Recommendation:
  Pertuzumab 840 mg IV loading then 420 mg + trastuzumab 8 mg/kg loading then 6 mg/kg
  + docetaxel 75 mg/m² every 3 weeks until progression.

  Strength: Strong (GRADE 1A)
  Evidence: High-quality, multiple RCTs, guideline concordance
```

**Discordant Recommendations**
```
Clinical Question: Adjuvant osimertinib for resected EGFR+ NSCLC

NCCN v4.2024 (Category 1):
  Osimertinib 80 mg daily × 3 years after adjuvant chemotherapy
  Evidence: ADAURA trial (median DFS not reached vs 28 months, HR 0.17)

ESMO 2023 (II, B):
  Osimertinib may be considered
  Note: Cost-effectiveness concerns, OS data immature

NICE (UK) 2022:
  Not recommended for routine use
  Reason: QALY analysis unfavorable at current pricing

Synthesis:
  Efficacy demonstrated in phase 3 trial (ADAURA), FDA/EMA approved.
  Guideline discordance based on cost-effectiveness, not clinical efficacy.

  US practice: NCCN Category 1, widely adopted
  European/UK: Variable adoption based on national HTA decisions

Recommendation Context-Dependent:
  US: Strong recommendation if accessible (GRADE 1B)
  Countries with cost constraints: Conditional recommendation (GRADE 2B)
```

## Quality Assessment Tools

### RCT Quality Assessment (Cochrane Risk of Bias 2.0)

**Domains**
1. **Bias from randomization process**: Sequence generation, allocation concealment
2. **Bias from deviations from intended interventions**: Blinding, protocol adherence
3. **Bias from missing outcome data**: Attrition, intention-to-treat analysis
4. **Bias in outcome measurement**: Blinded assessment, objective outcomes
5. **Bias in selection of reported result**: Selective reporting, outcome switching

**Judgment**: Low risk, some concerns, high risk (for each domain)

**Overall Risk of Bias**: Based on highest-risk domain

### Observational Study Quality (Newcastle-Ottawa Scale)

**Selection (max 4 stars)**
- Representativeness of exposed cohort
- Selection of non-exposed cohort
- Ascertainment of exposure
- Outcome not present at start

**Comparability (max 2 stars)**
- Comparability of cohorts (design/analysis adjustment for confounders)

**Outcome (max 3 stars)**
- Assessment of outcome
- Follow-up duration adequate
- Adequacy of follow-up (low attrition)

**Total Score**: 0-9 stars
- **High quality**: 7-9 stars
- **Moderate quality**: 4-6 stars
- **Low quality**: 0-3 stars

## Translating Evidence to Recommendations

### Recommendation Development Process

**Step 1: PICO Question Formulation**
```
Example PICO:
P - Population: Adults with type 2 diabetes and cardiovascular disease
I - Intervention: SGLT2 inhibitor (empagliflozin)
C - Comparator: Placebo (added to standard care)
O - Outcomes: Major adverse cardiovascular events (3P-MACE), hospitalization for heart failure
```

**Step 2: Systematic Evidence Review**
- Identify all relevant studies
- Assess quality using standardized tools
- Extract outcome data
- Synthesize findings (narrative or meta-analysis)

**Step 3: GRADE Evidence Rating**
- Start at high (RCTs) or low (observational)
- Downgrade for risk of bias, inconsistency, indirectness, imprecision, publication bias
- Upgrade for large effect, dose-response, confounders reducing effect (observational only)
- Assign final quality rating

**Step 4: Recommendation Strength Determination**

**Strong Recommendation (Grade 1)**
- Desirable effects clearly outweigh undesirable effects
- High or moderate quality evidence
- Little variability in patient values
- Intervention cost-effective

**Conditional Recommendation (Grade 2)**
- Trade-offs: Desirable and undesirable effects closely balanced
- Low or very low quality evidence
- Substantial variability in patient values/preferences
- Uncertain cost-effectiveness

**Step 5: Wording the Recommendation**
```
Strong: "We recommend..."
  Example: "We recommend SGLT2 inhibitor therapy for adults with type 2 diabetes and
  established cardiovascular disease to reduce risk of hospitalization for heart failure
  and cardiovascular death (Strong recommendation, high-quality evidence - GRADE 1A)."

Conditional: "We suggest..."
  Example: "We suggest considering GLP-1 receptor agonist therapy for adults with type 2
  diabetes and CKD to reduce risk of kidney disease progression (Conditional recommendation,
  moderate-quality evidence - GRADE 2B)."
```

## Incorporating Emerging Evidence

### Early-Phase Trial Data

**Phase 1 Trials**
- Purpose: Dose-finding, safety
- Outcomes: Maximum tolerated dose (MTD), dose-limiting toxicities (DLTs), pharmacokinetics
- Evidence level: Very low (expert opinion, case series)
- Clinical application: Investigational only, clinical trial enrollment

**Phase 2 Trials**
- Purpose: Preliminary efficacy signal
- Design: Single-arm (ORR primary endpoint) or randomized (PFS comparison)
- Evidence level: Low to moderate
- Clinical application: May support off-label use in refractory settings, clinical trial enrollment preferred

**Phase 3 Trials**
- Purpose: Confirmatory efficacy and safety
- Design: Randomized controlled trial, OS or PFS primary endpoint
- Evidence level: High (if well-designed and executed)
- Clinical application: Regulatory approval basis, guideline recommendations

**Phase 4 Trials**
- Purpose: Post-marketing surveillance, additional indications
- Evidence level: Variable (depends on design)
- Clinical application: Safety monitoring, expanded usage

### Breakthrough Therapy Designation

**FDA Fast-Track Programs**
- **Breakthrough Therapy**: Preliminary evidence of substantial improvement over existing therapy
- **Accelerated Approval**: Approval based on surrogate endpoint (PFS, ORR)
  - Post-marketing requirement: Confirmatory OS trial
- **Priority Review**: Shortened FDA review time (6 vs 10 months)

**Implications for Guidelines**
- May receive NCCN Category 2A before phase 3 data mature
- Upgrade to Category 1 when confirmatory data published
- Monitor for post-market confirmatory trial results

### Updating Recommendations

**Triggers for Update**
- New phase 3 trial results (major journal publication)
- FDA/EMA approval for new indication or agent
- Guideline update from NCCN, ASCO, ESMO
- Safety alert or drug withdrawal
- Meta-analysis changing effect estimates

**Rapid Update Process**
- Critical appraisal of new evidence
- Assess impact on current recommendations
- Revise evidence grade and recommendation strength if needed
- Disseminate update to users
- Version control and change log

## Conflicts of Interest and Bias

### Identifying Potential Bias

**Study Sponsorship**
- **Industry-sponsored**: May favor sponsor's product (publication bias, outcome selection)
- **Academic**: May favor investigator's hypothesis
- **Independent**: Government funding (NIH, PCORI)

**Author Conflicts of Interest**
- Consulting fees, research funding, stock ownership
- Disclosure statements required by journals
- ICMJE Form for Disclosure of Potential COI

**Mitigating Bias**
- Register trials prospectively (ClinicalTrials.gov)
- Pre-specify primary endpoint and analysis plan
- Independent data monitoring committee (IDMC)
- Blinding of outcome assessors
- Intention-to-treat analysis

### Transparency in Evidence Synthesis

**Pre-Registration**
- PROSPERO for systematic reviews
- Pre-specify PICO, search strategy, outcomes, analysis plan
- Prevents post-hoc changes to avoid negative findings

**Reporting Checklists**
- PRISMA for systematic reviews/meta-analyses
- CONSORT for RCTs
- STROBE for observational studies

**Data Availability**
- Individual patient data (IPD) sharing increases transparency
- Repositories: ClinicalTrials.gov results database, journal supplements

## Practical Application

### Evidence Summary for Clinical Document

```
EVIDENCE SYNTHESIS: Osimertinib for EGFR-Mutated NSCLC

Clinical Question:
Should adults with treatment-naïve advanced NSCLC harboring EGFR exon 19 deletion
or L858R mutation receive osimertinib versus first-generation EGFR TKIs?

Evidence Review:
┌──────────────────────────────────────────────────────────────────────┐
│ FLAURA Trial (Soria et al., NEJM 2018)                              │
├──────────────────────────────────────────────────────────────────────┤
│ Design: Phase 3 RCT, double-blind, 1:1 randomization                │
│ Population: EGFR exon 19 del or L858R, stage IIIB/IV, ECOG 0-1      │
│ Sample Size: n=556 (279 osimertinib, 277 comparator)                │
│ Intervention: Osimertinib 80 mg PO daily                            │
│ Comparator: Gefitinib 250 mg or erlotinib 150 mg PO daily           │
│ Primary Endpoint: PFS by investigator assessment                     │
│ Secondary: OS, ORR, DOR, CNS progression, safety                     │
│                                                                       │
│ Results:                                                             │
│ - Median PFS: 18.9 vs 10.2 months (HR 0.46, 95% CI 0.37-0.57, p<0.001)│
│ - Median OS: 38.6 vs 31.8 months (HR 0.80, 95% CI 0.64-1.00, p=0.046)│
│ - ORR: 80% vs 76% (p=0.24)                                          │
│ - Grade ≥3 AEs: 34% vs 45%                                          │
│ - Quality: High (well-designed RCT, low risk of bias)               │
└──────────────────────────────────────────────────────────────────────┘

Guideline Recommendations:
  NCCN v4.2024: Category 1 preferred
  ESMO 2022: Grade I, A
  ASCO 2022: Endorsed

GRADE Assessment:
  Quality of Evidence: ⊕⊕⊕⊕ HIGH
    - Randomized controlled trial
    - Low risk of bias (allocation concealment, blinding, ITT analysis)
    - Consistent results (single large trial, consistent with phase 2 data)
    - Direct evidence (target population and outcomes)
    - Precise estimate (narrow CI, sufficient events)
    - No publication bias concerns

  Balance of Benefits and Harms:
    - Large PFS benefit (8.7 month improvement, HR 0.46)
    - OS benefit (6.8 month improvement, HR 0.80)
    - Similar ORR, improved tolerability (lower grade 3-4 AEs)
    - Desirable effects clearly outweigh undesirable effects

  Patient Values: Little variability (most patients value survival extension)

  Cost: Higher cost than first-gen TKIs, but widely accessible in developed countries

FINAL RECOMMENDATION:
  Osimertinib 80 mg PO daily is recommended as first-line therapy for adults with
  advanced NSCLC harboring EGFR exon 19 deletion or L858R mutation.

  Strength: STRONG (Grade 1)
  Quality of Evidence: HIGH (⊕⊕⊕⊕)
  GRADE: 1A
```

## Keeping Current

### Literature Surveillance

**Automated Alerts**
- PubMed My NCBI (save searches, email alerts)
- Google Scholar alerts for specific topics
- Journal table of contents alerts (NEJM, Lancet, JCO)
- Guideline update notifications (NCCN, ASCO, ESMO email lists)

**Conference Monitoring**
- ASCO Annual Meeting (June)
- ESMO Congress (September)
- ASH Annual Meeting (December, hematology)
- AHA Scientific Sessions (November, cardiology)
- Plenary and press releases for practice-changing trials

**Trial Results Databases**
- ClinicalTrials.gov results database
- FDA approval letters and reviews
- EMA European public assessment reports (EPARs)

### Critical Appraisal Workflow

**Weekly Review**
1. Screen new publications (title/abstract)
2. Full-text review of relevant studies
3. Quality assessment using checklists
4. Extract key findings
5. Assess impact on current recommendations

**Monthly Synthesis**
1. Review accumulated evidence
2. Identify practice-changing findings
3. Update evidence tables
4. Revise recommendations if warranted
5. Disseminate updates to clinical teams

**Annual Comprehensive Review**
1. Systematic review of guideline updates
2. Re-assess all recommendations
3. Incorporate year's evidence
4. Major version release
5. Continuing education activities
