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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

 

4.2.3.5. Instrumental variables

Instrumental variable (IV) methods were invented over 70 years ago but were used by epidemiologists only recently. Over the past decade or so, non-parametric versions of IV methods have appeared that connect IV methods to causal and measurement-error models important in epidemiological applications. An introduction to instrumental variables for epidemiologists (Int J Epidemiol 2000;29:722-9) presents those developments, illustrated by an application of IV methods to non-parametric adjustment for non-compliance in randomised trials. The author mentions a number of caveats but concludes that IV corrections can be valuable in many situations. Where IV assumptions are questionable, the corrections can still serve as part of the sensitivity analysis or external adjustment. Where the assumptions are more defensible, as in field trials and in studies that obtain validation or reliability data, IV methods can form an integral part of the analysis. A review of IV analysis for observational comparative effectiveness studies suggested that in the large majority of studies, in which IV analysis was applied, one of the assumption could be violated (Potential bias of instrumental variable analyses for observational comparative effectiveness research, Ann Intern Med. 2014;161(2):131-8).

A proposal for reporting instrumental variable analyses has been suggested in Commentary: how to report instrumental variable analyses (suggestions welcome) (Epidemiology. 2013;24(3):370-4). In particular the type of treatment effect (average treatment effect/homogeneity condition or local average treatment effect/monotonicity condition) and the testing of critical assumptions for valid IV analyses should be reported. In support of these guidelines, the standardized difference has been proposed to falsify the assumption that confounders are not related to the instrumental variable (Quantitative falsification of instrumental variables assumption using balance measures, Epidemiology. 2014;25(5):770-2).

 

The complexity of the issues associated with confounding by indication, channelling and selective prescribing is explored in Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable (Epidemiology 2006;17(3):268-75). Contrary to results from randomised controlled trials showing that COX-2 inhibitors lead to a reduced risk of gastro-intestinal toxicity relative to non-selective NSAIDs, the author’s conventional multivariable analysis found no evidence of a gastro-protective effect attributable to COX-2 inhibitor use. However, a physician-level instrumental variable approach (a time-varying estimate of a physician’s relative preference for a given drug, where at least two therapeutic alternatives exist) yielded evidence of a protective effect due to COX-2 exposure, particularly for shorter term drug exposures. Despite the potential benefits of physician-level IVs their performance can vary across databases and strongly depends on the definition of IV used as discussed in Evaluating different physician's prescribing preference based instrumental variables in two primary care databases: a study of inhaled long-acting beta2-agonist use and the risk of myocardial infarction (Pharmacoepidemiol Drug Saf. 2016;25 Suppl 1:132-41).

 

Instrumental variable methods in comparative safety and effectiveness research (Pharmacoepidemiol Drug Saf 2010;19:537–54) is a practical guidance on IV analyses in pharmacoepidemiology. Instrumental variable methods for causal inference (Stat Med. 2014;33(13):2297-340) is a tutorial, including statistical code for performing IV analysis.

 

An important limitation of IV analysis is that weak instruments (small association between IV and exposure) lead to decreased statistical efficiency and biased IV estimates as detailed in Instrumental variables: application and limitations (Epidemiology 2006;17:260-7). For example, in the above mentioned study on non-selective NSAIDs and COX-2-inhibitors, the confidence intervals for IV estimates were in the order of five times wider than with conventional analysis. Performance of instrumental variable methods in cohort and nested case-control studies: a simulation study (Pharmacoepidemiol Drug Saf 2014; 2014;23(2):165-77) demonstrated that a stronger IV-exposure association is needed in nested case-control studies compared to cohort studies in order to achieve the same bias reduction. Increasing the number of controls reduces this bias from IV analysis with relatively weak instruments.

Selecting on treatment: a pervasive form of bias in instrumental variable analyses (Am J Epidemiol. 2015;181(3):191-7) warns against bias in IV analysis by including only a subset of possible treatment options.

 

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