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ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

10.1.3. Prominent issues in CER

10.1.3.1. Randomised clincial trials vs. observational studies

 

While RCTs are considered to provide the most robust evidence of the efficacy of therapeutic options, they are affected by well-recognised qualitative and quantitative limitations that may not reflect how the drug of interest will perform in real-life. Moreover, relatively few RCTs are traditionally designed using an alternative therapeutic strategy as a comparator, which limits the utility of the resulting data in establishing recommendations for treatment choices. For these reasons, other research methodologies such as pragmatic trials and observational studies may complement traditional explanatory RCTs in CER.

 

Explanatory and Pragmatic Attitudes in Therapeutic Trials (J Chron Dis 1967; republished in J Clin Epidemiol. 2009;62(5):499-505) distinguishes between two approaches in designing clinical trials: the ‘explanatory’ approach, which seeks to understand differences between the effects of treatments administered in experimental conditions, and the ‘pragmatic’ approach which seeks to answer the practical question of choosing the best treatment administered in normal conditions of use. The two approaches affect the definition of the treatments, the assessment of results, the choice of subjects and the way in which the treatments are compared. A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers (CMAJ 2009; 180 (10):E47-57) quantifies distinguishing characteristics between pragmatic and explanatory trials and has been updated in The Precis-2 tool: designing trials that are fit for purpose (BMJ 2015; 350: h2147). A checklist of eight items for the reporting of pragmatic trials was also developed as an extension of the CONSORT statement to facilitate the use of results from such trials in decisions about health-care (Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ 2008;337 (a2390):1-8).

 

The article Why we need observational studies to evaluate effectiveness of health care (BMJ 1996;312(7040):1215-18) documents situations in the field of health care intervention assessment where observational studies are needed because randomised trials are either unnecessary, inappropriate, impossible or inadequate. In a review of five interventions, Randomized, controlled trials, observational studies, and the hierarchy of research designs (N Engl J Med 2000;342(25):1887-92) found that the results of well-designed observational studies (with either a cohort or case-control design) did not systematically overestimate the magnitude of treatment effects. In defense of Pharmacoepidemiology-Embracing the Yin and Yang of Drug Research (N Engl J Med 2007;357(22):2219-21) shows that strengths and weaknesses of RCTs and observational studies make both designs necessary in the study of drug effects. However, When are observational studies as credible as randomised trials (Lancet 2004;363(9422):1728-31) explains that observational studies are suitable for the study of adverse (non-predictable) effects of drugs but should not be used for intended effects of drugs because of the potential for selection bias.

 

With regard to the selection and assessment of endpoints for CER, the COMET (Core Outcome Measures in Effectiveness Trials) Initiative aims at developing agreed minimum standardized sets of outcomes (‘core outcome sets’, COS) to be assessed and reported in effectiveness trials of a specific condition as discussed in Choosing Important Health Outcomes for Comparative Effectiveness Research: An Updated Review and User Survey (PLoS One. 2016 ;11(1):e0146444.).

 

10.1.3.2. Use of electronic healthcare databases

 

A review of uses of health care utilization databases for epidemiologic research on therapeutics (J Clin Epidemiol. 2005;58(4):323-37) considers the application of health care utilisation databases to epidemiology and health services research, with particular reference to the study of medications. Information on relevant covariates and in particular on confounding factors may not be available or adequately measured in electronic healthcare databases. To overcome this limit, CER studies have integrated information from health databases with information collected ad hoc from study subjects. Enhancing electronic health record measurement of depression severity and suicide ideation: a Distributed Ambulatory Research in Therapeutics Network (DARTNet) study (J Am Board Fam Med. 2012;25(5):582-93) shows the value of adding direct measurements and pharmacy claims data to data from electronic healthcare records participating in DARTNet. Assessing medication exposures and outcomes in the frail elderly: assessing research challenges in nursing home pharmacotherapy (Med Care 2010;48(6 Suppl):S23-31) describe how merging longitudinal electronic clinical and functional data from nursing home sources with Medicare and Medicaid claims data can support unique study designs in CER but pose many challenging design and analytic issues. Pragmatic randomized trials using routine electronic health records: putting them to the test (BMJ 2012;344:e55) discusses opportunities for using electronic healthcare records for conducting pragmatic trials.

 

A model based on counterfactual theory for CER using large administrative healthcare databases has been suggested, in which causal inference from observational studies based on large administrative health databases is viewed as an emulation of a randomized trial. This ‘target trial’ is made explicit and design and analytic approaches are reviewed in Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available (Am J Epidemiol (2016) 183 (8): 758-764).

 

10.1.3.3. Bias and confounding in observational CER

 

Methodological issues and principles of Section 5 of the ENCePP Guide are applicable to CER as well and the textbooks cited in that chapter are recommended for consultation.

 

Methods to assess intended effects of drug treatment in observational studies are reviewed (J Clin Epidemiol 2004;57(12):1223-31) provides an overview of methods that seek to adjust for confounding in observational studies when assessing intended drug effects. Developments in post-marketing comparative effectiveness research (Clin Pharmacol Ther 2007;82(2):143-56) also reviews the roles of propensity scores (PS), instrumental variables and sensitivity analyses to reduce measured and unmeasured confounding in CER. Use of propensity scores and disease risk scores in the context of observational health-care programme research is described in Summary Variables in Observational Research: Propensity Scores and Disease Risk Scores. More recently, high-dimensional propensity score has been suggested as a method to further improve control for confounding as these variables may collectively be proxies for unobserved factors.

 

Results presented in High-dimensional propensity score adjustment in studies of treatment effects using health care claims data (Epidemiology 2009;20(4):512-22) show that in a selected empirical evaluation, high-dimensional propensity score improved confounding control compared to conventional PS adjustment when benchmarked against results from randomized controlled trials. See section 5.3.4 of the Guide for an in-depth discussion of propensity scores. Several methods can be considered to handle cofounders in non-experimental CER (Confounding adjustment in comparative effectiveness research conducted within distributed research networks (Med Care 2013 ; 51 : S4-S10); Disease Risk Score (DRS) AS A Confounder Summary Method; Systematic Review and Recommendations (Pharmacoepidemiol Drug Saf 2013; 22: 122–129). Strategies for selecting variables for adjustment in non-experimental CER have also been proposed (Pharmacoepidemiol Drug Saf 2013; 22: 1139–1145).

A reason for discrepancies between results of randomised trials and observational studies may be the use of prevalent drug users in the latter. Evaluating medication effects outside of clinical trials: new-user designs (Am J Epidemiol. 2003;158(9):915-20) explains the biases introduced by use of prevalent drug users and how a new-user (or incident user) design eliminate these biases by restricting analyses to persons under observation at the start of the current course of treatment. The Incident User Design in Comparative Effectiveness Research reviews published CER case studies in which investigators had used the incident user design, discusses its strengths (reduced bias) and weakness (reduced precision of comparative effectiveness estimates) and provides recommendations to investigators considering to use this design. The value of incident user design and exceptions has been reviewed (Pharmacoepidemiol Drug Saf 2013; 22: 1–6).

 

Individual Chapters:

 

1. Introduction

2. Formulating the research question

3. Development of the study protocol

4. Approaches to data collection

4.1. Primary data collection

4.1.1. Surveys

4.1.2. Randomised clinical trials

4.2. Secondary data collection

4.3. Patient registries

4.3.1. Definition

4.3.2. Conceptual differences between a registry and a study

4.3.3. Methodological guidance

4.3.4. Registries which capture special populations

4.3.5. Disease registries in regulatory practice and health technology assessment

4.4. Spontaneous report database

4.5. Social media and electronic devices

4.6. Research networks

4.6.1. General considerations

4.6.2. Models of studies using multiple data sources

4.6.3. Challenges of different models

5. Study design and methods

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

5.1.1. Assessment of exposure

5.1.2. Assessment of outcomes

5.1.3. Assessment of covariates

5.1.4. Validation

5.2. Bias and confounding

5.2.1. Selection bias

5.2.2. Information bias

5.2.3. Confounding

5.3. Methods to handle bias and confounding

5.3.1. New-user designs

5.3.2. Case-only designs

5.3.3. Disease risk scores

5.3.4. Propensity scores

5.3.5. Instrumental variables

5.3.6. Prior event rate ratios

5.3.7. Handling time-dependent confounding in the analysis

5.4. Effect measure modification and interaction

5.5. Ecological analyses and case-population studies

5.6. Pragmatic trials and large simple trials

5.6.1. Pragmatic trials

5.6.2. Large simple trials

5.6.3. Randomised database studies

5.7. Systematic reviews and meta-analysis

5.8. Signal detection methodology and application

6. The statistical analysis plan

6.1. General considerations

6.2. Statistical analysis plan structure

6.3. Handling of missing data

7. Quality management

8. Dissemination and reporting

8.1. Principles of communication

8.2. Communication of study results

9. Data protection and ethical aspects

9.1. Patient and data protection

9.2. Scientific integrity and ethical conduct

10. Specific topics

10.1. Comparative effectiveness research

10.1.1. Introduction

10.1.2. General aspects

10.1.3. Prominent issues in CER

10.2. Vaccine safety and effectiveness

10.2.1. Vaccine safety

10.2.2. Vaccine effectiveness

10.3. Design and analysis of pharmacogenetic studies

10.3.1. Introduction

10.3.2. Identification of generic variants

10.3.3. Study designs

10.3.4. Data collection

10.3.5. Data analysis

10.3.6. Reporting

10.3.7. Clinical practice guidelines

10.3.8. Resources

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