scholarly journals Sharp bounds on the relative treatment effect for ordinal outcomes

Biometrics ◽  
2019 ◽  
Vol 76 (2) ◽  
pp. 664-669
Author(s):  
Jiannan Lu ◽  
Yunshu Zhang ◽  
Peng Ding
Keyword(s):  
Treatment Effect ◽  
Sharp Bounds ◽  
Relative Treatment Effect

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

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e20523-e20523
Author(s):  
Eric Mackay ◽  
Justin Slater ◽  
Paul Arora ◽  
Kristian Thorlund ◽  
Audrey Beliveau ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Treatment Effect ◽  
Comparative Effectiveness ◽  
Error Propagation ◽  
Covariate Adjustment ◽  
Relative Treatment Effect ◽  
Kaplan Meier ◽  
Adjustment Term ◽  
Treatment Comparisons ◽  
True Treatment Effect

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.

Estimating a treatment effect: Choosing between relative and absolute measures

2016 ◽  
Vol 23 (2) ◽  
pp. 197-200 ◽  
Author(s):  
Maria Pia Sormani ◽  
Paolo Bruzzi
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Treatment Effect ◽  
Numerical Examples ◽  
Relative Risks ◽  
Relative Treatment Effect ◽  
Hazard Ratios ◽  
Numbers Needed To Treat ◽  
Indirect Comparisons ◽  
Independent Trials

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.

scholarly journals 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

2021 ◽  
Author(s):  
Tamar I. de Vries ◽  
Manon C. Stam‐Slob ◽  
Ron J. G. Peters ◽  
Yolanda van der Graaf ◽  
Jan Westerink ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Major Bleeding ◽  
Treatment Effect ◽  
Significant Interaction ◽  
Risk Model ◽  
Baseline Risk ◽  
Systemic Embolism ◽  
Allocation Model ◽  
Linear Predictor ◽  
Relative Treatment Effect

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.

SHARP BOUNDS ON THE DISTRIBUTION OF TREATMENT EFFECTS AND THEIR STATISTICAL INFERENCE

2009 ◽  
Vol 26 (3) ◽  
pp. 931-951 ◽  
Author(s):  
Yanqin Fan ◽  
Sang Soo Park
Keyword(s):  
Sample Size ◽  
Statistical Inference ◽  
Confidence Intervals ◽  
Simulation Study ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Asymptotic Distributions ◽  
Finite Sample ◽  
Sharp Bounds ◽  
Nonparametric Estimators

In this paper, we propose nonparametric estimators of sharp bounds on the distribution of treatment effects of a binary treatment and establish their asymptotic distributions. We note the possible failure of the standard bootstrap with the same sample size and apply the fewer-than-nbootstrap to making inferences on these bounds. The finite sample performances of the confidence intervals for the bounds based on normal critical values, the standard bootstrap, and the fewer-than-nbootstrap are investigated via a simulation study. Finally we establish sharp bounds on the treatment effect distribution when covariates are available.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Using Discount Rate to Predict Treatment Effect and Relapse

2007 ◽  
Author(s):  
Yun Zhang ◽  
Min Yu ◽  
Xinyue Zhou ◽  
Jingjing Lin ◽  
Yi Ni
Keyword(s):  
Discount Rate ◽  
Treatment Effect
Sign in / Sign up

Export Citation Format

Share Document