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Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Group Design

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

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Related Experiment Video

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Selection bias and subject refusal in a cluster-randomized controlled trial.

Rochelle Yang¹, Barry L Carter^2,3, Tyler H Gums⁴

¹Department of Pharmacy Practice & Science, College of Pharmacy, University of Iowa, Iowa City, IA, 52242, USA.

BMC Medical Research Methodology

|July 12, 2017

Summary

Selection bias in cluster-randomized trials was assessed. While some initial differences were noted, the overall impact of patient selection and refusal to participate was minimal, ensuring external validity.

Keywords:

Cluster randomized trial Non participation bias Selection bias

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Area of Science:

Clinical Trials Methodology
Health Services Research
Biostatistics

Background:

Selection and non-participation bias can compromise the external validity of studies.
Cluster-randomized controlled trials (CRCTs) are particularly susceptible to selection bias due to the impracticality of blinding clusters to treatment allocation.
This study specifically evaluated the influence of selection bias within a large-scale CRCT.

Purpose of the Study:

To assess the impact of selection bias in a large cluster-randomized controlled trial.
To determine if patient characteristics differed between those who consented and declined participation across intervention and control groups.

Main Methods:

The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE) study utilized a remote pharmacist-led intervention across twelve primary care offices.
Patient eligibility was assessed using standardized forms, and eligible patients were approached for recruitment.
Comparisons of consenting versus declining patients were made between intervention and control arms, with statistical significance determined by t-tests and chi-square tests, adjusted for cluster variation.

Main Results:

Initial analysis indicated that patients with poorly controlled diabetes were more likely to decline in intervention sites.
Patients with uncontrolled hypertension who declined in control sites had higher diastolic blood pressure than those declining in intervention sites.
After adjusting for cluster variation, females showed a significantly higher consent rate (OR=1.41; 95% CI=1.03, 1.92).

Conclusions:

Despite initial observations, the overall impact of selection bias and patient refusal on the study's external validity was minimal.
Rigorous methodological approaches are crucial to mitigate the threat of selection bias in cluster-randomized trials.
The study highlights the importance of accounting for cluster variation in bias assessments.