Impact of a Patient’s Baseline Risk on the Relative Benefit and Harm of a Preventive Treatment Strategy: Applying Trial Results in Clinical Decision Making

Background For translating an overall trial result into an individual patient’s expected absolute treatment effect, differences in relative treatment effect between patients need to be taken into account. The aim of this study was to evaluate whether relative treatment effects of medication in 2 large contemporary trials are influenced by multivariable baseline risk of an individual patient. Methods and Results In 9361 patients from SPRINT (Systolic Blood Pressure Intervention Trial), risk of major adverse cardiovascular events was assessed using a newly derived risk model. In 18 133 patients from the RE‐LY (Randomized Evaluation of Long‐Term Anticoagulant Therapy) trial, risk of stroke or systemic embolism and major bleeding was assessed using the Global Anticoagulant Registry in the Field–Atrial Fibrillation risk model. Heterogeneity of trial treatment effect was assessed using Cox models of trial allocation, model linear predictor, and their interaction. There was no significant interaction between baseline risk and relative treatment effect from intensive blood pressure lowering in SPRINT ( P =0.92) or from dabigatran compared with warfarin for stroke or systemic embolism in the RE‐LY trial ( P =0.71). There was significant interaction between baseline risk and treatment effect from dabigatran versus warfarin in the RE‐LY trial ( P <0.001) for major bleeding. Quartile‐specific hazard ratios for bleeding ranged from 0.40 (95% CI, 0.26–0.61) to 1.04 (95% CI, 0.83–1.03) for dabigatran, 110 mg, and from 0.61 (95% CI, 0.42–0.88) to 1.20 (95% CI, 0.97–1.50) for dabigatran, 150 mg, compared with warfarin. Conclusions Effect modification of relative treatment effect by individual baseline event risk should be assessed systematically in randomized clinical trials using multivariate risk prediction, not only in terms of treatment efficacy but also for important treatment harms, as a prespecified analysis. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01206062.

Error propagation in simulated treatment comparisons for multiple myeloma: Results from a simulation.

e20523 Background: Comparing the effectiveness of multiple myeloma treatments presents a challenge due to the limited number of head-to-head trials with which to conduct indirect treatment comparisons. This is particularly true when subgroup analysis is of interest. In comparative effectiveness research Simulated Treatment Comparisons (STCs) are becoming increasingly common in the absence of head-to-head trials. STCs use estimates from limited IPD to adjust for covariate imbalance between trials, however the uncertainty from these estimates is generally ignored when estimating relative treatment effects. This study demonstrates the need to account for this uncertainty when conducting STCs for indications such as multiple myeloma. We introduce an STC method that accounts for the uncertainty due to covariate adjustment, and demonstrate its effectiveness via simulation. Methods: We simulated two single arm studies (N = 300 for both), each containing age and overall survival. We assume study 1 has individual patient data available, and study 2 only has aggregate age data and a digitized Kaplan-Meier curve. We compute a covariate adjustment term based on the mean age difference between the studies and the age coefficients from fitting a parametric survival model to the observed study 1 IPD. We then estimate the variance of this adjustment term via bootstrapping and incorporate this uncertainty into a Bayesian STC model which estimates the relative treatment effect for the two study datasets converted to a digitized Kaplan-Meier format. Results: The proportion of 95% credible intervals (CrI) that captured the true treatment effect was 86.8% without error propagation, whereas 92.0% of CrI’s captured the true treatment with error propagation. 94.9% of CrI’s contained the true treatment effect when using survival regression with the complete IPD. Conclusions: Failing to account for uncertainty from the covariate adjustment when conducting simulated treatment comparisons generally leads to underestimating the uncertainty of the relative treatment effect. This method better captures the uncertainty introduced when conducting an STC and has the potential to yield more reliable estimates of the comparative effectiveness of multiple myeloma treatments.

Testing Equality of Treatments under an Incomplete Block Crossover Design with Ordinal Responses

The generalized odds ratio (GOR) for paired sample is considered to measure the relative treatment effect on patient responses in ordinal data. Under a three-treatment two-period incomplete block crossover design, both asymptotic and exact procedures are developed for testing equality between treatments with ordinal responses. Monte Carlo simulation is employed to evaluate and compare the finite-sample performance of these test procedures. A discussion on advantages and disadvantages of the proposed test procedures based on the GOR versus those based on Wald’s tests under the normal random effects proportional odds model is provided. The data taken as a part of a crossover trial studying the effects of low and high doses of an analgesic versus a placebo for the relief of pain in primary dysmenorrhea over the first two periods are applied to illustrate the use of these test procedures.

Estimating a treatment effect: Choosing between relative and absolute measures

The size of a treatment effect in clinical trials can be expressed in relative or absolute terms. Commonly used relative treatment effect measures are relative risks, odds ratios, and hazard ratios, while absolute estimate of treatment effect are absolute differences and numbers needed to treat. When making indirect comparisons of treatment effects, which is common in multiple sclerosis (MS), having now many drugs tested in independent trials, we can have different figures if we use relative or absolute measures, and a frequently asked question by clinicians is which approach should be used. In this report, we will try to define these measures, to give numerical examples of their calculation and specify their meaning and their context of use.