Rejoinder to Letter to the Editor on “A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment”

2021 ◽  
Vol 13 (4) ◽  
pp. 511-512
Author(s):  
Klaus Groes Larsen ◽  
Mette Krog Josiassen
2021 ◽  
pp. 00348-2021
Author(s):  
Ragdah Arif ◽  
Arjun Pandey ◽  
Ying Zhao ◽  
Kyle Arsenault-Mehta ◽  
Danya Khoujah ◽  
...  

Chronic obstructive pulmonary disease-associated pulmonary hypertension (COPD-PH) is an increasingly recognised condition which contributes to worsening dyspnea and poor survival in COPD. It is uncertain whether specific treatment of COPD-PH, including use of medications approved for pulmonary arterial hypertension (PAH), improves clinical outcomes. This systematic review and meta-analysis assesses potential benefits and risks of therapeutic options COPD-PH.We searched Medline and Embase for relevant publications until Sep 2020. Articles were screened for studies on treatment of COPD-PH for at least 4 weeks in 10 or more patients. Screening, data extraction, and risk of bias assessment were performed independently in duplicate. When possible, relevant results were pooled using the random effects model.Supplemental long-term O2 therapy (LTOT) mildly reduced mean pulmonary artery pressure (PAP), slowed progression of PH, and reduced mortality, but other clinical or functional benefits were not assessed. Phosphodiesterase type-5 inhibitors significantly improved systolic PAP (pooled treatment effect −5.9 mmHg; 95%CI −10.3, −1.6), but had inconsistent clinical benefits. Calcium-channel blockers and endothelin receptor antagonists had limited hemodynamic, clinical, or survival benefits. Statins had limited clinical benefits despite significantly lowering systolic PAP (pooled treatment effect −4.6 mmHg; 95% CI: −6.3, −2.9).This review supports guideline recommendations for LTOT in hypoxemic COPD-PH patients as well as recommendations against treatment with PAH-targeted medications, Effective treatment of COPD-PH depends upon research into the pathobiology, and future high-quality studies comprehensively assessing clinically relevant outcomes are needed.


Author(s):  
Richard D Riley ◽  
Aroon Hingorani ◽  
Karel GM Moons

A predictor of treatment effect is any factor or combination of factors (such as a patient characteristic, symptom, sign, test, or biomarker result) associated with the effect (benefit or harm) of a specific treatment in persons with a particular disease or health condition. Various terms are used across disciplines to refer to prediction of treatment effect, including treatment-predictor (treatment-covariate) interaction, effect modification, predictive (as opposed to prognostic) factors (in oncology), or moderation analysis. This chapter reviews principles of the design of studies of treatment effect predictors, such as exploration of treatment-predictor interactions in randomized trials and the importance of replication of such estimates using data from multiple trials. The application of predictors of treatment effect in practice for matching individuals or subgroups to specific treatments is introduced as one type of stratified care, and the need for impact studies to investigate whether stratified care leads to better outcomes and improved efficiency of healthcare is highlighted.


Author(s):  
P.S. Aisen ◽  
R. Raman

Dr. Umbricht suggests that the two examples we cite in our viewpoint (1) support rather than call into question the value of interim futility analyses in Alzheimer’s disease (AD) trials. He points out that the first example, the Phase 3 trials of aducanumab, the futility analyses did indeed indicate a trend toward a beneficial treatment effect in one of the two trials though the planned pooled futility decision led to stopping the trials. In the second case, in which a futility analysis led to a halt, full analysis of available data suggested efficacy; a subsequent study was negative.


2021 ◽  
Vol 9 (1) ◽  
pp. 83-108
Author(s):  
Jonathan Levy ◽  
Mark van der Laan ◽  
Alan Hubbard ◽  
Romain Pirracchio

Abstract The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific treatment effect function. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy conditions on the initial fits of the outcome model and treatment mechanism, as required by TMLE. Particularly, in circumstances where data adaptive fitting methods are very important to eliminate bias but hold no guarantee of satisfying the entropy condition, we show that the CV-TMLE sampling distributions maintain normality with a lower mean squared error than TMLE. In addition to verifying the theoretical properties of TMLE and CV-TMLE through simulations, we highlight some of the challenges in estimating the variance of the treatment effect, which lack double robustness and might be biased if the true variance is small and sample size insufficient.


2013 ◽  
Vol 22 (11) ◽  
pp. 1178-1188 ◽  
Author(s):  
Michal Abrahamowicz ◽  
Marie-Eve Beauchamp ◽  
Pierre Fournier ◽  
Alexandre Dumont

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