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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

 

4.2.4. Effect modification

An important question that may arise when studying the effects of medicines is whether such effects differ between subgroups of patients (effect modification). To answer this question, one can stratify the study population, e.g. by gender, and compare the effects in these subgroups. In CONSORT 2010 Explanation and Elaboration: Updated guidelines for reporting parallel group randomised trials (J Clin Epidemiol 2010;63(8):e1-37) and Interaction revisited: the difference between two estimates (BMJ 2003;326:219), it is recommended to perform a formal statistical test to assess if there are statistically significant differences between subgroups for these effects. The study report should explain which method was used to examine these differences and specify which subgroup analyses were predefined in the study protocol and which ones were performed while analysing the data (Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Epidemiology 2007;18:805-35).

 

Effect modification can be measured in two ways: on an additive scale (based on risk differences [RD]), or on a multiplicative scale (based on relative risks [RR]). From the perspective of public health and clinical decision making, the additive scale is usually considered most appropriate. The standard measure for interaction on the additive scale is the relative excess risk due to interaction (RERI), as explained in the textbook Modern Epidemiology (K. Rothman, S. Greenland, T. Lash. 3rd Edition, Lippincott Williams & Wilkins, 2008). Other measures of interaction include the attributable proportion (A) and the synergy index (S). With sufficient sample size, most interaction tests perform similarly with regard to type 1 error rates and power according to Exploring interaction effects in small samples increases rates of false-positive and false-negative findings: results from a systematic review and simulation study (J Clin Epidemiol 2014; 67(7):821-9). In small samples (<250), the Breslow-Day and Tarone test performed best for interactions on the odds-ratio scale, whereas Likelihood Ratio and RERI-based tests performed better on RD scale. When exposure prevents outcome, in small samples the RERI-based test is relatively underpowered compared to other tests. Possible solutions include choosing an alternative interaction test, or recoding exposure categories taking the category with the lowest risk as reference.

 

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration (Epidemiology 2007;18:805-35) and Recommendations for presenting analyses of effect modification and interaction (Int J Epidemiol 2012;41:514-20) recommend that effect modification should be reported as follows:

 

  1. Separate effects (RRs, odds ratios or RDs, with confidence intervals) of the exposure of interest (e.g. drug), of the effect modifier (e.g. gender) and of their joint effect using one single reference category (preferably the stratum with the lowest risk of the outcome as suggested in Estimating measures of interaction on an additive scale for preventive exposures. Eur J Epidemiol 2011;26(6):433-8) as this gives enough information to the reader to calculate effect modification on an additive and multiplicative scale;

  2. Effects of the exposure within strata of the potential effect modifier;

  3. Measures of effect modification on both additive (e.g. RERI) and multiplicative (e.g. S) scales with confidence intervals;

  4. Confounders for which the association between exposure and outcome was adjusted for.

It should be kept in mind that past drug use should be considered as a potential effect modifier in studies assessing the risk of occurrence of events associated with recent drug use. This is shown in Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research (J Clin Epidemiol 1994;47(7):731-7) in the context of a hospital-based case-control study on NSAIDs and the risk of upper gastrointestinal bleeding.

 

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