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ENCePP Guide on Methodological Standards in Pharmacoepidemiology

4.4.2. Large simple trials

Large simple trials (LSTs) are pragmatic, randomized clinical trials with minimal data collection protocols that are narrowly focused on clearly defined outcomes important to patients as well as clinicians. Their large sample size provides the adequate statistical power to detect small differences in effects between treatments in a situation where a moderate difference in an important outcome may be important. Additionally, LST's include follow-up that mimics normal clinical practice.

LSTs are particularly suited when an adverse event is very small or delayed (with a large expected attrition rate), when the population exposed to the risk is heterogeneous (e.g. different indications and age groups), when several risks need to be assessed in the same trial (e.g. risks of stroke and of myocardial infarction) or when many confounding factors need to be balanced between treatment groups. In these circumstances, the cost and complexity of a traditional RCT may outweigh its advantages and LSTs can help keep the volume and complexity of data collection to a minimum.

Outcomes that are simple and objective can also be measured from the routine process of care using epidemiological follow-up methods, for example by using questionnaires or hospital discharge records. LST methodology is discussed in Chapters 36 and 37 of the book Pharmacoepidemiology (Strom BL, Kimmel SE, Hennessy S. 5^th Edition, Wiley, 2012), which includes a list of conditions appropriate for the conduct of a LST and a list of conditions which make a LST feasible. Examples of published LSTs are Assessment of the safety of paediatric ibuprofen: a practitioner based randomised clinical trial (JAMA 1995;279:929-33) and Comparative mortality associated with ziprasidone and olanzapine in real-world use among 18,154 patients with schizophrenia: The Zodiac Observational Study of Cardiac Outcomes (ZODIAC) (Am J Psychiatry 2011;168(2):193-201).

Note that the use of the term ‘simple’ in the expression ‘LST’ refers to data structure and not data collection. It is used in relation to situations in which a small number of outcomes are measured. The term may not adequately reflect the complexity of the studies undertaken.

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

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