Investigating treatment-effect modification by a continuous covariate in IPD meta-analysis: an approach using fractional polynomials

Background In clinical trials, there is considerable interest in investigating whether a treatment effect is similar in all patients, or that one or more prognostic variables indicate a differential response to treatment. To examine this, a continuous predictor is usually categorised into groups according to one or more cutpoints. Several weaknesses of categorization are well known. To avoid the disadvantages of cutpoints and to retain full information, it is preferable to keep continuous variables continuous in the analysis. To handle this issue, the Subpopulation Treatment Effect Pattern Plot (STEPP) was proposed about two decades ago, followed by the multivariable fractional polynomial interaction (MFPI) approach. Provided individual patient data (IPD) from several studies are available, it is possible to investigate for treatment heterogeneity with meta-analysis techniques. Meta-STEPP was recently proposed and in patients with primary breast cancer an interaction of estrogen receptors with chemotherapy was investigated in eight randomized controlled trials (RCTs). Methods We use data from eight randomized controlled trials in breast cancer to illustrate issues from two main tasks. The first task is to derive a treatment effect function (TEF), that is, a measure of the treatment effect on the continuous scale of the covariate in the individual studies. The second is to conduct a meta-analysis of the continuous TEFs from the eight studies by applying pointwise averaging to obtain a mean function. We denote the method metaTEF. To improve reporting of available data and all steps of the analysis we introduce a three-part profile called MethProf-MA. Results Although there are considerable differences between the studies (populations with large differences in prognosis, sample size, effective sample size, length of follow up, proportion of patients with very low estrogen receptor values) our results provide clear evidence of an interaction, irrespective of the choice of the FP function and random or fixed effect models. Conclusions In contrast to cutpoint-based analyses, metaTEF retains the full information from continuous covariates and avoids several critical issues when performing IPD meta-analyses of continuous effect modifiers in randomised trials. Early experience suggests it is a promising approach.

Automated summary

In clinical trials, it is important to investigate whether a treatment effect is consistent in all patients or if certain variables indicate a different response to treatment. Categorizing continuous predictors into groups using cutpoints has several weaknesses. A better approach is to keep the variables continuous and analyze them using methods like Subpopulation Treatment Effect Pattern Plot (STEPP) and multivariable fractional polynomial interaction (MFPI). Meta-analysis techniques can be used to study treatment heterogeneity if individual patient data (IPD) from multiple studies are available. The recently proposed Meta-STEPP method was used to investigate the interaction of estrogen receptors with chemotherapy in eight randomized controlled trials (RCTs) of primary breast cancer patients.