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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.
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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.

Experimental Designs

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...

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Related Experiment Video

Updated: Sep 26, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Published on: May 13, 2022

Planning a method for covariate adjustment in individually randomised trials: a practical guide.

Tim P Morris^1,2, A Sarah Walker³, Elizabeth J Williamson⁴

¹MRC Clinical Trials Unit at UCL, London, UK. tim.morris@ucl.ac.uk.

|April 19, 2022

Summary

Choosing the best statistical method for randomized trials requires careful consideration. Direct adjustment, standardization, and inverse-probability-of-treatment weighting (IPTW) have different strengths and weaknesses depending on the trial context.

Keywords:

Clinical trials Covariate adjustment Estimands Inverse probability of treatment weighting Missing data Randomised controlled trials Standardisation

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Area of Science:

Biostatistics
Clinical Trials
Epidemiology

Background:

Accounting for baseline covariates in randomized trials is advised to increase statistical power and validate experimental error estimates.
Several methods exist to incorporate covariates, but guidance on selecting the optimal approach is lacking.

Purpose of the Study:

To compare three primary methods for covariate adjustment in randomized trials: direct adjustment, standardization, and inverse-probability-of-treatment weighting (IPTW).
To provide insights for statistical analysis plan development regarding covariate adjustment strategies.

Main Methods:

The study evaluates direct adjustment, standardization, and IPTW from the perspective of developing a statistical analysis plan.
Key considerations include asymptotic efficiency, sensitivity to model misspecification, handling of designed balance, and estimation of marginal and conditional treatment effects.

Main Results:

All three methods are asymptotically efficient but can lose efficiency if covariate functions are misspecified.
Direct adjustment is not recommended for marginal estimands due to potential convergence issues; IPTW offers better convergence.
IPTW standard errors may be anti-conservative in small samples, and all methods have strategies for missing covariate data.

Conclusions:

No single covariate adjustment method is universally superior; the optimal choice depends on the specific trial context.
Researchers are encouraged to routinely consider direct adjustment, standardization, and IPTW for covariate adjustment in randomized trials.