Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control

2021 ◽  
Author(s):  
Sridharan Raghavan ◽  
Kevin Josey ◽  
Gideon Bahn ◽  
Domenic Reda ◽  
Sanjay Basu ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Glycemic Control ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Heterogeneous Treatment Effects ◽  
Intensive Glycemic Control

scholarly journals The Threshold of the Severity of Diabetic Retinopathy below Which Intensive Glycemic Control Is Beneficial in Diabetic Patients: Estimation Using Data from Large Randomized Clinical Trials

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Yuqi Liu ◽  
Juan Li ◽  
Jinfang Ma ◽  
Nanwei Tong
Keyword(s):  
Clinical Trials ◽  
Diabetic Retinopathy ◽  
Glycemic Control ◽  
Glucose Control ◽  
Randomized Clinical Trials ◽  
Control Group ◽  
Diabetic Patients ◽  
Large Sample Size ◽  
Intensive Glucose Control ◽  
Intensive Glycemic Control

Intensive glucose therapy can protect the retina of individuals with diabetes, but it is unknown if it provides the same protection to patients with different severity of diabetic retinopathy (DR). We finally included DR-related studies involving intensive glucose control with large sample size and long follow-up time, including five large and high-quality randomized clinical trials (RCTs): DCCT, UKPDS, ACCORD, AdRem, and VADT. With DCCT as a reference, we supposed a DR severity threshold that is verified by other RCTs then. We found that individuals who have DR lesions that are equivalent to or less severe than moderate NPDR achieve benefits for the retina by intensive glycemic control. However, these are realized only if the HbA1c in type 1 or type 2 diabetic patients is reduced at least by 0.8% versus the control group or it is reduced to <7% and >3 years of intensive glucose control is required. If the severity of DR lesions is worse than moderate NPDR, intensive glycemic control may not bring benefits.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

Progression-free survival (PFS) as surrogate endpoint for overall survival (OS) in clinical trials of HER2-targeted agents in HER2-positive metastatic breast cancer (MBC): An individual patient data (IPD) analysis.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 610-610 ◽  
Author(s):  
Stefan Michiels ◽  
Lina Pugliano ◽  
Delphine Grun ◽  
Jana Barinoff ◽  
David A. Cameron ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Metastatic Breast ◽  
Targeted Agents ◽  
First Line ◽  
Her2 Positive ◽  
Individual Level ◽  
The Individual

610 Background: The gold standard endpoint in randomized clinical trials (RCTs) in MBC is OS, which has the disadvantage of requiring extended follow-up and being confounded by subsequent anti-cancer therapies. Although therapeutics have been approved based on PFS, its use as a primary endpoint is controversial. This study, the first IPD meta-analysis of targeted agents in MBC, aimed to collect data from RCTs of HER2-targeted agents in HER2+ MBC, assessing to what extent PFS correlates with, and may be used as, a surrogate for OS. Methods: A search was conducted in April 2011. Eligible RCTs accrued HER2+ MBC patients (pts) in 1992-2008. Collaboration was obtained from industrial partners (Roche, GSK) for industry-led studies. Investigator-assessed PFS was defined as the time from randomization to clinical or radiological progression, or death. A correlation approach was used: at the individual level, to estimate the association between PFS and OS using a bivariate survival model and at the trial level, to estimate the association between treatment effects on PFS and OS. Squared correlation values close to 1.0 would indicate strong surrogacy. Results: The search strategy resulted in 2137 eligible pts in 13 RCTs testing trastuzumab or lapatinib. We collected IPD data from 1963 pts in 9 RCTs. One phase II RCT did not have sufficient follow-up data so that 1839 pts in 8 RCTs were retained (5 evaluating trastuzumab, 3 lapatinib); 6 out of 8 RCTs were first-line. At the individual level, the Spearman rank correlation using Hougaard copula was equal to r=0.66 (95% CI 0.65 to 0.66) corresponding to an r2 of 0.42. At the trial level, the squared correlation between treatment effects on PFS and OS was provided by R2=0.33 (95% CI -0.22 to 0.86) using Hougaard copula and R2=0.53 (95% CI 0.22 to 0.83) using log hazard ratios from Cox models. Conclusions: In RCTs of HER2-targeted agents in HER2+ MBC, PFS is moderately correlated with OS and treatment effects on PFS are modestly correlated with treatment effects on OS, similarly to first-line chemotherapy in MBC (Burzykowski et al JCO 2008). PFS does not completely substitute for OS.

Mediators and Moderators of Treatment Effects in Randomized Clinical Trials

2002 ◽  
Vol 59 (10) ◽  
pp. 877 ◽  
Author(s):  
Helena Chmura Kraemer ◽  
G. Terence Wilson ◽  
Christopher G. Fairburn ◽  
W. Stewart Agras
Keyword(s):  
Clinical Trials ◽  
Treatment Effects ◽  
Randomized Clinical Trials
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