Minimisation

Minimisation

Minimisation is a statistical technique used in the design of clinical trials to ensure that treatment groups are balanced with respect to several prognostic factors. Unlike simple randomization, which can lead to imbalances in small sample sizes, minimisation aims to achieve balance across groups by considering the characteristics of participants already assigned to each group.

Overview

Minimisation is particularly useful in clinical trials where the sample size is small, and the risk of imbalance in prognostic factors could lead to biased results. The method involves assigning each new participant to a treatment group in a way that minimizes the imbalance across several predefined factors.

Methodology

The process of minimisation involves the following steps:

1. Selection of Prognostic Factors: Before the trial begins, researchers identify key prognostic factors that could influence the outcome of the study. These factors might include age, gender, disease severity, and other relevant characteristics.

2. Assignment of Participants: As each new participant is enrolled in the trial, the minimisation algorithm calculates the imbalance that would result from assigning the participant to each possible treatment group.

3. Minimisation Algorithm: The algorithm assigns the participant to the group that would result in the least imbalance. This is typically done using a weighted sum of the imbalances across all prognostic factors.

4. Random Component: To maintain the unpredictability of assignments, a random component is often added. This means that even if one group is slightly more balanced, the participant might still be assigned to another group with a small probability.

Advantages

- Balance Across Groups: Minimisation ensures that treatment groups are balanced with respect to important prognostic factors, reducing the risk of confounding. - Flexibility: The method can accommodate multiple prognostic factors and can be adjusted as the trial progresses.

Disadvantages

- Complexity: The method is more complex than simple randomization and requires careful planning and execution. - Potential for Bias: If the minimisation algorithm is not properly implemented, it could introduce bias into the trial.

Applications

Minimisation is widely used in clinical trials, particularly in oncology, cardiology, and other fields where patient characteristics can significantly influence outcomes. It is also used in other types of research where balance across groups is critical.

Also see

- Randomization - Clinical trial - Prognostic factor - Bias in research