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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

9.3.5. Data analysis

The focus of data analysis should be on the measure of effect modification (see section 4.2.4 of this Guide on Effect Modification). Attention should be given to whether the mode of inheritance (e.g. dominant, recessive or additive) is defined a priori based on prior knowledge from functional studies. However, investigators are usually naïve regarding the underlying mode of inheritance. A solution might be to undertake several analyses, each under a different assumption, though the approach to analysing data raises the problem of multiple testing (see Methodological quality of pharmacogenetic studies: issues of concern. Stat Med 2008;27(30):6547-69). The problem of multiple testing and the increased risk of type I error is in general a problem in pharmacogenetic studies evaluating multiple SNPs, multiple exposures and multiple interactions. The most common approach to correct for multiple testing is to use the Bonferroni correction. This correction may be considered too conservative and runs the risk of producing many pharmacogenetic studies with a null result. Other approaches to adjust for multiple testing include permutation testing and false discovery rate (FDR) control, which are less conservative. The FDR, described in Statistical significance for genomewide studies (Proc Natl Acad Sci USA 2003;100(16):9440-5), estimates the expected proportion of false-positives among associations that are declared significant, which is expressed as a q-value.

 

Alternative innovative methods are under development and may be used in the future, such as the systems biology approach, a Bayesian approach, or data mining (see Methodological and statistical issues in pharmacogenomics. J Pharm Pharmacol 2010;62(2):161-6).

Important complementary approaches include the conduct of individual patient data meta-analyses and/or replication studies to avoid the risk of false-positive findings.

An important step in analysis of genome-wide association studies data that needs to be considered is the conduct of rigorous quality control procedures before conducting the final association analyses. Relevant guidelines include Guideline for data analysis of genomewide association studies (Cancer Genomics Proteomics 2007;4(1):27-34) and Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis (Curr Pharmacogenomics Person Med 2011;9(1):41-66).

 

Individual Chapters:

 

1. General aspects of study protocol

2. Research question

3. Approaches to data collection

3.1. Primary data collection

3.2. Secondary use of data

3.3. Research networks

3.4. Spontaneous report database

3.5. Using data from social media and electronic devices as a data source

3.5.1. General considerations

4. Study design and methods

4.1. General considerations

4.2. Challenges and lessons learned

4.2.1. Definition and validation of drug exposure, outcomes and covariates

4.2.1.1. Assessment of exposure

4.2.1.2. Assessment of outcomes

4.2.1.3. Assessment of covariates

4.2.1.4. Validation

4.2.2. Bias and confounding

4.2.2.1. Choice of exposure risk windows

4.2.2.2. Time-related bias

4.2.2.2.1. Immortal time bias

4.2.2.2.2. Other forms of time-related bias

4.2.2.3. Confounding by indication

4.2.2.4. Protopathic bias

4.2.2.5. Surveillance bias

4.2.2.6. Unmeasured confounding

4.2.3. Methods to handle bias and confounding

4.2.3.1. New-user designs

4.2.3.2. Case-only designs

4.2.3.3. Disease risk scores

4.2.3.4. Propensity scores

4.2.3.5. Instrumental variables

4.2.3.6. Prior event rate ratios

4.2.3.7. Handling time-dependent confounding in the analysis

4.2.4. Effect modification

4.3. Ecological analyses and case-population studies

4.4. Hybrid studies

4.4.1. Pragmatic trials

4.4.2. Large simple trials

4.4.3. Randomised database studies

4.5. Systematic review and meta-analysis

4.6. Signal detection methodology and application

5. The statistical analysis plan

5.1. General considerations

5.2. Statistical plan

5.3. Handling of missing data

6. Quality management

7. Communication

7.1. Principles of communication

7.2. Guidelines on communication of studies

8. Legal context

8.1. Ethical conduct, patient and data protection

8.2. Pharmacovigilance legislation

8.3. Reporting of adverse events/reactions

9. Specific topics

9.1. Comparative effectiveness research

9.1.1. Introduction

9.1.2. General aspects

9.1.3. Prominent issues in CER

9.1.3.1. Randomised clinical trials vs. observational studies

9.1.3.2. Use of electronic healthcare databases

9.1.3.3. Bias and confounding in observational CER

9.2. Vaccine safety and effectiveness

9.2.1. Vaccine safety

9.2.1.1. General aspects

9.2.1.2. Signal detection

9.2.1.3. Signal refinement

9.2.1.4. Hypothesis testing studies

9.2.1.5. Meta-analyses

9.2.1.6. Studies on vaccine safety in special populations

9.2.2. Vaccine effectiveness

9.2.2.1. Definitions

9.2.2.2. Traditional cohort and case-control studies

9.2.2.3. Screening method

9.2.2.4. Indirect cohort (Broome) method

9.2.2.5. Density case-control design

9.2.2.6. Test negative design

9.2.2.7. Case coverage design

9.2.2.8. Impact assessment

9.2.2.9. Methods to study waning immunity

9.3. Design and analysis of pharmacogenetic studies

9.3.1. Introduction

9.3.2. Identification of genetic variants

9.3.3. Study designs

9.3.4. Data collection

9.3.5. Data analysis

9.3.6. Reporting

9.3.7. Clinical practice guidelines

9.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes