Stratification refers to the division of study participants into subgroups (strata) according to predefined prognostic or predictive factors prior to randomization in clinical trials. The objective is to ensure that these factors are equally distributed across treatment groups and thus do not bias the comparison between groups. Stratification is one of the most important methods for controlling known confounders in randomized clinical trials. It is typically combined with block randomization, which ensures balanced allocation throughout the entire recruitment period within each stratum. Stratification fields and the assigned allocations are stored in the randomization list of the Interactive Response Technology system (IRT/IXRS) and automatically retrieved at each enrollment.
Purpose and Operating Principle of Stratification
Without stratification, despite randomization, an unequal distribution of important prognostic factors between treatment groups may occur by chance—particularly in small study populations. Through stratified randomization, random allocation is performed separately within each stratum, ensuring that each group has the same composition with respect to the stratification variables. This increases the internal validity of the study and improves the precision of effect estimation as well as the statistical power for demonstrating the treatment effect.
A typical example from oncology: If disease stage (early vs. advanced) and age (under/over 65 years) are selected as stratification factors, four strata are created. Within each stratum, patients are randomized separately, ensuring that the active treatment and placebo groups have the same number of patients with this characteristic profile in each stratum.
Selection of Stratification Factors
The selection of stratification factors is based on clinical and statistical considerations. Variables that are known to have a strong influence on the primary endpoint (prognostic factors) or that could differentially affect treatment efficacy in different subgroups (predictive factors) are suitable as stratification factors. Typical stratification factors include: disease severity, geographic region or study site, patient age, sex, prior treatments, and biomarker status.
The number of stratification factors should be limited to what is necessary. Too many factors result in numerous small strata in which only a few patients are randomized—this can complicate balancing and negate the benefits of stratification. As a rule of thumb: For fewer than 200 participants, no more than 2–3 stratification factors with a maximum of 2–3 levels each should be used. All stratification factors and their levels must be fully described in the study protocol. Ethics committees and regulatory authorities review the scientific justification for the selection of stratification variables as part of the CTA assessment.
Stratification and Statistical Analysis
When stratification factors have been used in randomization, they should also be accounted for in the primary statistical analysis. This can be accomplished by including the stratification variables as covariates in the regression model or through stratified analyses (e.g., stratified log-rank test, Mantel-Haenszel method). Ignoring stratification in the analysis typically leads to conservative estimates and a loss of statistical power.
The Statistical Analysis Plan (SAP) must clearly describe how stratification factors will be accounted for in the primary analysis. Discrepancies between the stratification variables used in randomization and those in the analysis are critically scrutinized by regulatory authorities such as the EMA and require justification in the Clinical Study Report (CSR).
Stratification in Multicenter and International Studies
In multicenter studies, the study site (or a group of sites) is frequently used as a stratification factor. This ensures that site-related differences in patient population or treatment quality do not affect group comparability. When there are very many sites, they are often grouped into site blocks to ensure sufficiently large strata.
In international multi-regional clinical trials (MRCTs) according to ICH E17, geographic region is a common stratification factor, as population differences between regions can influence efficacy and tolerability. Stratification by region also enables regional subgroup analyses that are of interest to local regulatory authorities. CROs such as mediconomics support sponsors in defining appropriate stratification factors based on available data from prior studies and in the technical implementation in the IRT system. Incorrect configuration of the stratification system in the IRT is considered a critical process error and can jeopardize randomization integrity.
Frequently Asked Questions (FAQ)
What is the difference between stratification and subgroup analysis?
Stratification is a design feature of randomization that is established before study initiation and ensures comparability of groups. A subgroup analysis is a statistical evaluation conducted after study completion that examines the treatment effect in defined subgroups. Stratification factors can serve as the basis for prespecified subgroup analyses, but not every subgroup analysis requires prior stratification.
Is stratification necessary in large studies?
In very large studies (several hundred or thousand participants), simple randomization typically provides sufficiently good balancing of prognostic factors. Stratification is particularly useful in small to medium-sized studies and for factors with a strong influence on the primary endpoint. Nevertheless, many study statisticians recommend stratification as standard practice, as it can improve the efficiency of the analysis even in large studies.
What is minimization as an alternative to stratification?
Minimization is an adaptive randomization method that dynamically selects treatment assignment to minimize imbalance across multiple factors. Unlike classical stratified randomization, it simultaneously accounts for many factors without creating an exponential number of strata. Minimization is a particularly suitable alternative for small studies with many stratification factors, but is evaluated differently by some regulatory authorities and must be clearly described in the SAP.