Consider the The Randomized Control Trial like an atom in physics, it is a stand alone piece of research, a true experimental design that aims for the highest quality. Individuals are randomly assigned into groups. One group, the intervention and the other a control (no treatment, a placebo, or a standard intervention). There are however different types
Randomised parallel-group trials - Normal RCTs and most common (IR) 10/10
Cluster-randomised parallel-group trials
Randomised crossover trials and other matched designs
Before reading this page look at this article which explains RCTs in a simplified way:https://obgyn.onlinelibrary.wiley.com/doi/10.1111/aogs.13309
Quasi-experimental studies DO NOT include Randomisation. They sit between observational studies and true experimental designs.
No Randomization: Participants are not randomly assigned to intervention or control groups.
Comparison Groups: They often use a comparison group that did not receive the intervention, but the groups may differ in baseline characteristics.
Intervention is Manipulated: Like in experimental studies, the researcher still introduces an intervention or treatment.
Why Use Quasi-Experimental Designs?
Ethical or Practical Constraints
Randomization isn't always feasible. For example, you can’t randomly assign people to smoke vs. not smoke or assign schools to underfunded vs. well-funded.
Real-World Relevance
Quasi-experiments often happen in natural settings (e.g. schools, hospitals), increasing external validity—how well findings generalize.
Policy and Program Evaluation
Often used in evaluating public health interventions, education reforms, or service changes where randomization would disrupt real-world systems.
Stages to conducting a Randomised Controlled Trial
Study Design: Determine the research question and design the study protocol, including the intervention or treatment being tested, the control group, and the primary and secondary outcome measures.
Ethical Approval: Obtain ethical approval from the relevant research ethics committee or institutional review board to ensure participant safety and ethical considerations are addressed.
Participant Recruitment: Recruit and enroll participants who meet the predetermined eligibility criteria for the study. Obtain informed consent from the participants.
Randomisation: Randomly assign participants to either the intervention group or the control group using appropriate randomisation techniques to minimize selection bias.
Blinding: Implement blinding or masking procedures to reduce bias.
Intervention and Control: Administer the intervention to the intervention group according to the study protocol,
Data Collection: Collect data on the predetermined outcome measures, including baseline characteristics, treatment response, adverse events, and other relevant data. Use standardized data collection tools and techniques.
Data Analysis: Analyze the collected data using appropriate statistical methods to compare the outcomes between the intervention and control groups. This may include descriptive statistics, inferential tests, and subgroup analyses. Use this link to find out which tool to use.
Results Interpretation: Interpret the findings of the data analysis, considering the statistical significance, effect sizes, and clinical relevance of the results.
Discussion and Conclusion: Discuss the implications of the results in the context of existing literature, strengths, and limitations of the study, and potential areas for further research. Draw conclusions based on the findings.
Reporting: Prepare a comprehensive report or manuscript adhering to the reporting guidelines, such as CONSORT (Consolidated Standards of Reporting Trials), to ensure transparent and accurate reporting of the study methodology, results, and conclusions.
Before reading this page look at this article which explains RCTs in a simplified way: https://obgyn.onlinelibrary.wiley.com/doi/10.1111/aogs.13309
Descriptive studies paint the picture; analytical studies interpret it.
Aim: To describe characteristics, frequencies, trends, or distributions within a population.
Examples:
How many people in Lincolnshire have diabetes?
What is the age distribution of patients attending A&E?
Common types:
Case reports
Case series
Cross-sectional studies (can be both descriptive and analytical)
Strengths:
Useful for generating hypotheses and understanding scope or burden.
Limitations:
They do not test hypotheses or identify causal relationships.
Aim: To test hypotheses and examine relationships or associations between variables (often exposure and outcome).
What they do: Answer the “why” and “how” questions.
Examples:
Does smoking increase the risk of COPD?
Is there an association between shift work and burnout in nurses?
Common types:
Cohort studies
Case-control studies
Cross-sectional studies (again, can be analytical)
Randomized controlled trials (though these are experimental, not observational)
Strengths:
Allow for examination of potential causes, risk factors, or predictors.
Limitations:
More complex design; can still be limited by confounding and bias, especially if not randomized.
It is important for RCTs to begin with a clear understanding of what intervention is being measured and how, and for these to be as clear and repeatable throughout the reasearch. Without them there is the risk of researchers introducing bias by looking for answers that the study was not set up to provide. It is also important for the eligibility criteria of the population of interest to be clear.
In a RCT trial participants are randomly allocated to control or experimental groups. Well-designed trials feature randomisation that is as free from bias as possible. A strong report of a RCT should also include details of the randomisation method in order to be as transparent as possible.
Blinding is when the trial participants and/or the people administering the intervention do not know whether a participant has been allocated to the experimental or the control group. This is done to avoid the influence of bias, following the principle that if an individual knows which group they are in they will act differently as a result. “Single-blind” trials have only the participants unaware of group allocation while “double-blind” trials have both the participants and administrators unaware of group allocation. Blinding of administrators is important in order to prevent them from acting differently in response to awareness of whether they are administering the exposure of interest or not. This difference in action may influence the participant’s behaviour, or measurement of the impact of the exposure, or other factors. This could obscure the true effect of the intervention.
The trial participants have been investigated thoroughly and found to be adequately representative of the broader population they represent while still being sufficiently similar so as to produce research data that is free from confounding factors. The sample cohort also needs to be large enough so that statistical analysis allows for results to be applicable to the eligible population with a reasonable degree of confidence.
It is difficult for a study to be entirely free of bias. However, researchers can design their study in such a manner to control for potential sources of bias as much as possible. This includes appropriately managing any involvement by commercial partners in the trial in order to prevent their participation having any influence on its execution and conclusions.
In order for the trial participants to be a fair representation of the population of interest (i.e. the larger group that the sample is taken from), recruitment of this sample cohort needs to be robust. This means that recruitment should be conducted with the purpose of obtaining a representative cross-section of the population of interest, and that segments of that population should not be overrepresented if that could cause bias in the results. For example, if the proportion of males in the sample cohort is much higher than in the population of interest the sample is representing, this may have implications for the application of the study’s conclusion to the broader population which (proportionally) has far fewer males than the sample that were actually studied.
While randomisation is an important strength of randomised controlled trials, some forms of randomisation are susceptible to bias. For this reason, randomisation needs to be effectively concealed from both the party administering the intervention and the participant receiving it. Effective concealment means that the administrator of the intervention is unable to learn beforehand or otherwise effect which participants receive which treatments.
A study has greater validity if all participants are accounted for throughout the life of the trial, rather than being ‘lost to follow up’ after the intervention and possibly skewing the data as a result. Reporting of trials should account for all participants initially enrolled in the study.
In solid trials, all participants are analysed in the group that corresponds to how they were intended to be treated, and not necessarily how they were treated. In other words, if a participant in the intervention group ended up not actually receiving the intervention, their data should still be included in the intervention groups’ data and not in the control group. This is known as an intention-to-treat analysis and preserves the important randomisation feature of the trial.
Many trials are conducted with the sponsorship of companies that sell the intervention being studied in the trial. Common examples include pharmaceuticals or medical devices. Research published in 2018 found that studies which had industry sponsorship were more likely to report results favourable to the intervention of interest than studies not sponsored by industry. This suggests the existence of a bias for the studies which received industry sponsorship.
Despite this, it is important to be aware that many trials would not happen at all without industry sponsorship. Furthermore, the existence of industry sponsorship bias does not mean that all commercially-sponsored trials are inherently biased.
Randomisation and blinding reduce bias and impact of confounders. Confounders are variables which impact the outcome of a study and are outside the control of the researchers.
Researchers have greater control over the study’s circumstances.
Can lead to strong conclusions about a causal relationship between exposure and feature(s) of interest.
Design, execution and evaluation can be complex, costly and lengthy.
Recruitment of participants can be difficult for some research.
May not be appropriate for some research due to ethical concerns.
May not be appropriate for some research due to difficulty of blinding participants for some interventions.