Subgroup analyses in randomized controlled trials: the need for risk stratification in kidney transplantation

Abstract

Although randomized controlled trials (RCT) are the gold standard for establishing causation in clinical research, their aggregated results can be misleading when applied to individual patients. A treatment may be beneficial in some patients, but its harms may outweigh benefits in others. While conventional one-variable-at-a-time subgroup analyses have well-known limitations, multivariable risk-based analyses can help uncover clinically significant heterogeneity in treatment effects that may be otherwise obscured. Trials in kidney transplantation have yielded the finding that a reduction in acute rejection does not translate into a similar benefit in prolonging graft survival and improving graft function. This paradox might be explained by the variation in risk for acute rejection among included kidney transplant recipients varying the likelihood of benefit or harm from intense immunosuppressive regimens. Analyses that stratify patients by their immunological risk may resolve these otherwise puzzling results. Reliable risk models should be developed to investigate benefits and harms in rationally designed risk-based subgroups of patients in existing RCT data sets. These risk strata would need to be validated in future prospective clinical trials examining long-term effects on patient and graft survival. This approach may allow better individualized treatment choices for kidney transplant recipients.

Figure 1. How risk stratification can explain the paradox in the hypothetical RCT

Illustration of the paradox discussed in the text about the hypothetical randomized trial described in the table. Treatment A in grey columns and treatment B in white columns, respectively. Panel A: Acute rejection rates associated with both treatments, with a constant relative risk reduction of 40% in the entire cohort as well as in subgroups stratified by baseline risk for acute rejection. Panel B: If acute rejection represents the only predictor of worse graft outcome, treatment B is associated with an improved glomerular filtration rate (GFR) due to the reduced rate of acute rejection. The actual effect size is dependent on the patient’s baseline risk for acute rejection. Panel C: We assume that only treatment B is associated with nephrotoxic effects that reduce GFR by 10 ml/min (striped boxes), and that this is constant across risk strata. Panel D: Overall treatment effect on GFR, considering treatment-related adverse events. (Detailed explanation in the text)