The Use and Interpretation of Quasi-Experimental Studies in Medical Informatics PMC
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In contrast to quasi-experiments, randomized experiments are often thought to be the gold standard when estimating the effects of treatment interventions. However, circumstances frequently arise where quasi-experiments can usefully supplement randomized experiments or when quasi-experiments can fruitfully be used in place of randomized experiments. Researchers need to appreciate the relative strengths and weaknesses of the various quasi-experiments so they can choose among pre-specified designs or craft their own unique quasi-experiments. Several such innovations are covered in other articles within this special issue (e.g. Kim et al., this issue). These designs are becoming increasingly popular in clinical treatment (Bhatt and Mehta, 2016) but could also hold promise for implementation scientists, especially as interest grows in rapid-cycle testing of implementation strategies or efforts.
Quasi-experimental Designs That Use Control Groups and Pretests
This study didn't use specific aspects to determine which start-up companies should participate. Therefore, the results may seem straightforward, but several aspects may determine the growth of a specific company, apart from the variables used by the researchers. The directionality problem is avoided in quasi-experimental research since the regression analysis is altered before the multiple regression is assessed. However, because individuals are not randomized at random, there are likely to be additional disparities across conditions in quasi-experimental research.
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The bottom panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment did not work. It has been observed that it is more difficult to conduct a good quasi-experiment than to conduct a good randomized trial (43). Although QEDs are increasingly used, it is important to note that randomized designs are still preferred over quasi-experiments except where randomization is not possible. In this paper we present three important QEDs and variants nested within them that can increase internal validity while also improving external validity considerations, and present case studies employing these techniques. One of the strengths of QEDs is that they are often employed to examine intervention effects in real world settings and often, for more diverse populations and settings. Consequently, if there is adequate examination of characteristics of participants and setting-related factors it can be possible to interpret findings among critical groups for which there may be no existing evidence of an intervention effect for.
Interrupted Time Series
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Measurement of both treatment and control conditions classically occurs pre- and post-intervention, with differential improvement between the groups attributed to the intervention. This design is popular due to its practicality, especially if data collection points can be kept to a minimum. It may be especially useful for capitalizing on naturally occurring experiments such as may occur in the context of certain policy initiatives or rollouts—specifically, rollouts in which it is plausible that a control group can be identified.
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STRATEGIES TO STRENGTHEN EXTERNAL VALIDITY
The purpose of quasi-experimental design is to investigate the causal relationship between two or more variables when it is not feasible or ethical to conduct a randomized controlled trial (RCT). Quasi-experimental designs attempt to emulate the randomized control trial by mimicking the control group and the intervention group as much as possible. This method is used to reduce bias in quasi-experimental designs by matching participants in the intervention group with participants in the control group who have similar characteristics.
In this study design, an intervention (X) is implemented and a posttest observation (O1) is taken. For example, X could be the introduction of a pharmacy order-entry intervention and O1 could be the pharmacy costs following the intervention. This design is the weakest of the quasi-experimental designs that are discussed in this article. Without any pretest observations or a control group, there are multiple threats to internal validity. Unfortunately, this study design is often used in medical informatics when new software is introduced since it may be difficult to have pretest measurements due to time, technical, or cost constraints. Randomized controlled trials (RCTs) in which individuals are assigned to intervention or control (standard-of-care or placebo) arms are considered the gold standard for assessing causality and as such are a first choice for most intervention research.
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Qualitative data can enhance quasi-experimental research by revealing participants’ experiences and opinions, but quantitative data is the method’s foundation. If any substantial variations between them can be well explained, you may be very assured that any differences are attributable to the treatment but not to other extraneous variables. These pre-existing groups can be used to compare the symptom development of individuals who received the novel therapy with those who received the normal course of treatment, even though the groups weren’t chosen at random. In a genuine trial, you’d split half of the psych ward into treatment groups, With half getting the new psychotherapy therapy and the other half receiving standard depression treatment.
Matching can be based on demographic and other important factors (e.g. measures of health care access or time-period). The study used a nonexperimental design to assess the impact of a workplace financial education program. Two companies offered a financial education program to all employees for a one-time reimbursed fee of $150. The program group consisted of 46 employees, 10 from Company A and 36 from Company B. Program participants attended classes once a week over a 10-week period.
What Are the Threats to Establishing Causality When Using Quasi-experimental Designs in Medical Informatics?
Because the quasi-experimental study design has recognized limitations, we sought to determine whether authors acknowledged the potential limitations of this design. Examples of acknowledgment included mention of lack of randomization, the potential for regression to the mean, the presence of temporal confounders and the mention of another design that would have more internal validity. In a stepped wedge, all participants receive the intervention, but are assigned to the timing of the intervention in a staggered fashion (Betran et al., 2018; Brown and Lilford, 2006; Hussey and Hughes, 2007), typically at the site or cluster level. Stepped wedge designs have their analytic roots in balanced incomplete block designs, in which all pairs of treatments occur an equal number of times within each block (Hanani, 1961). Traditionally, all sites in stepped wedge trials have outcome measures assessed at all time points, thus allowing sites that receive the intervention later in the trial to essentially serve as controls for early intervention sites. A recent special issue of the journal Trials includes more detail on these designs (Davey et al., 2015), which may be ideal for situations in which it is important for all participating patients or sites to receive the intervention during the trial.
As for design A5, the assumption must be made that the effect of the intervention is transient, which is most often applicable to medical informatics interventions. Because in this design, subjects may serve as their own controls, this may yield greater statistical efficiency with fewer numbers of subjects. This design adds a third posttest measurement (O3) to the one-group pretest-posttest design and then removes the intervention before a final measure (O4) is made. The advantage of this design is that it allows one to test hypotheses about the outcome in the presence of the intervention and in the absence of the intervention.
One example of an implementation SMART is the Adaptive Implementation of Effective Program Trial (ADEPT; Kilbourne et al., 2014a). ADEPT was a clustered SMART (NeCamp et al., 2017) designed to inform an adaptive sequence of implementation strategies for implementing an evidence-based collaborative chronic care model, Life Goals (Kilbourne et al., 2014c; Kilbourne et al., 2012a), into community-based practices. In one classic example, the treatment was the reduction of the work shifts in a factory from 10 hours to 8 hours (Cook & Campbell, 1979). This would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them.
This method involves summarizing the data collected during a study using measures such as mean, median, mode, range, and standard deviation. Descriptive statistics can help researchers identify trends or patterns in the data, and can also be useful for identifying outliers or anomalies. This type of research is often performed in cases where a control group cannot be created or random selection cannot be performed. Its objective is to equalize the 2 groups, and therefore, any observed difference in the study outcome afterwards will only be attributed to the intervention – i.e. it removes confounding. As AI continues to grow in power, so too does the need for economic research to better understand how we can harness its benefits while mitigating its risks.
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