Biomedical research often aims to establish cause-and-effect relationships between variables. One of the ways to achieve this is through the use of experimental designs, where variables are manipulated to observe their effect on an outcome of interest. But sometimes it’s not practical or ethical to conduct randomized controlled trials, especially when studying sensitive topics. For example, if we want to investigate whether the death of a parent increases suicide ideation in adolescents. That’s where quasi-experimental designs come in! In this article, we’ll take a closer look at how quasi-experimental designs are used in biomedical research, the different types, their advantages and disadvantages, and the best practices for reporting a quasi-experimental study.
Uses of Quasi-Experimental Designs
Quasi-experimental designs are particularly useful in situations where it is impossible or impractical to randomly assign participants to treatment groups. For example, it may not be ethical to randomly assign participants to a group that receives a potentially harmful intervention. In biomedical research, quasi-experimental designs are commonly used to evaluate the effectiveness of interventions or treatments in real-world settings, where randomization may not be feasible. For instance, you may want to examine whether postoperative nosocomial pneumonia affects the post-discharge quality of life, but you cannot randomly assign some postoperative patients to acquire pneumonia. Additionally, quasi-experimental designs can be used to evaluate the impact of policy changes or natural disasters, where the intervention or exposure is not under the control of the researcher.
Types of Quasi-Experimental Designs
There are several types of quasi-experimental designs, which are described below:
Pre-Post Design
Participants are measured before and after an intervention to determine if there was a change in the outcome of interest.
Non-Equivalent Control Group Design
Participants are assigned to a treatment group or a control group, but the groups are not equivalent at baseline.
Time-Series Design
Participants are measured multiple times before and after an intervention to determine if there was a change in the outcome of interest.
Interrupted Time-Series Design
Participants are measured multiple times before and after an intervention, but there is a discrete event that interrupts the time series, such as a policy change or natural disaster.
Advantages of Quasi-Experimental Designs
Quasi-experimental designs offer several advantages in biomedical research.
- For starters, they allow researchers to evaluate interventions or treatments in real-world settings. This can be beneficial because it may provide more accurate results than those obtained from a laboratory setting.
- Moreover, quasi-experimental designs can be a more practical and budget-friendly option than randomized controlled trials in some situations.
- On top of that, quasi-experimental designs can be used to assess the impact of policy changes or natural disasters, which wouldn’t be possible with randomized controlled trials.
Disadvantages of Quasi-Experimental Designs
Quasi-experimental designs also have some disadvantages.
- One of the major disadvantages is that they are more susceptible to bias than randomized controlled trials. Participants may self-select into treatment or control groups, or there may be systematic differences between the groups at baseline. For instance, in the study on postoperative nosocomial pneumonia mentioned earlier, the non-pneumonia group could have had younger or healthier patients compared to the pneumonia group, making age and preoperative health status factors that confound the findings.
- Moreover, quasi-experimental designs may not provide as strong evidence of causality as randomized controlled trials. Finally, quasi-experimental designs may require more complex statistical analyses than randomized controlled trials.
Best Practices in Reporting a Quasi-Experimental Study
When reporting a quasi-experimental study, it is important to clearly describe the study design and any limitations of the design. You will need to report any differences between the treatment and control groups at baseline and any efforts made to control/minimize these differences. Additionally, it is necessary to report any potential sources of bias and describe the statistical methods used to control for these sources of bias. Finally, make sure to include effect size and statistical significance when reporting the results of the study.
Do you want to derive maximum effectiveness from a quasi-experimental research design? Consult an expert biostatistician, through Editage’s Statistical Analysis & Review Services.
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