From Subgroups to Precision Medicine
Recent methodological developments have shifted the focus away from simple subgroup comparisons toward treatment-effect heterogeneity (HTE) modelling.
Subgroup analyses have been a standard component of clinical trial reporting for decades.
Their original objective was to determine whether treatment effects differed across clinically relevant patient populations.
However, traditional subgroup analyses suffer from several well-known limitations:
reduced statistical power,
arbitrary categorization of continuous variables,
multiplicity concerns,
unstable effect estimates.
Recent methodological developments have shifted the focus away from simple subgroup comparisons toward treatment-effect heterogeneity (HTE) modelling.
Rather than asking whether a treatment works differently in predefined subgroups, modern approaches attempt to estimate how treatment benefit varies continuously across patients.
This article illustrates the evolution from classical subgroup analyses toward patient-level treatment-effect modelling using data from a randomized clinical trial.
The Traditional Subgroup Paradigm
Most clinical study reports contain a forest plot similar to the one shown below.
Investigators evaluate treatment effects separately in predefined patient groups such as:
younger versus older patients,
low versus high biomarker values,
disease severity categories,
geographic regions.
The underlying assumption is that these categories may reveal clinically meaningful differences in treatment response.
The Problem with Arbitrary Cutoffs
One of the most common practices in subgroup analyses is dichotomization.
Continuous variables are converted into categories such as:
age < 65 years versus ≥ 65 years,
low versus high biomarker values,
below versus above median.
While this approach simplifies interpretation, it also discards information.
A patient with a bilirubin value of 1.9 and another with 2.1 may be assigned to different subgroups despite being clinically very similar.
At the same time, patients with values of 2 and 20 may be grouped together despite substantial biological differences.
Continuous Effect Modification
Modern statistical approaches increasingly model treatment-effect heterogeneity using continuous variables.
Instead of creating categories, interaction models estimate how treatment effect changes across the entire range of a biomarker or clinical characteristic.
This allows investigators to evaluate gradual changes in treatment benefit rather than abrupt changes imposed by arbitrary cutoffs.
In the current example, bilirubin was modelled as a continuous effect modifier.
Beyond Average Treatment Effects
Most clinical trial publications report a single treatment effect estimate.
Examples include:
hazard ratios,
risk ratios,
odds ratios.
These values represent average treatment effects.
However, average effects may hide substantial variation among individual patients.
Some patients may derive considerable benefit.
Others may experience little benefit.
Some may even experience net harm.
This observation has led to growing interest in heterogeneous treatment effect modelling.
Patient-Level Treatment Benefit
Modern treatment-effect models estimate potential benefit for individual patients based on their baseline characteristics.
The objective is no longer to identify a single “responsive subgroup.”
Instead, the objective is to characterize the distribution of treatment benefit across the entire population.
Heterogeneous Treatment Effect Modelling
Recent methodological developments have expanded the subgroup analysis toolbox considerably.
Examples include:
interaction models,
spline-based effect-modification models,
Bayesian treatment-effect models,
causal forests,
uplift models,
Bayesian Additive Regression Trees (BART).
These approaches aim to estimate individualized treatment effects rather than perform repeated subgroup significance testing.
From Subgroups to Precision Medicine
The evolution of subgroup methodology reflects a broader shift within clinical development.
Historically, treatment decisions were based on average effects observed in large populations.
Today, increasing attention is being paid to identifying patients most likely to benefit from a specific intervention.
This transition represents one of the foundations of precision medicine.
