Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data

Author(s):  
Yanghui Liu ◽  
Riquan Zhang ◽  
Shujie Ma ◽  
Xiuzhen Zhang
Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.


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.


2019 ◽  
Vol 38 (28) ◽  
pp. 5391-5412
Author(s):  
Chathura Siriwardhana ◽  
K. B. Kulasekera ◽  
Somnath Datta

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1017-1025 ◽  
Author(s):  
Yuanyuan Shen ◽  
Tianxi Cai

2014 ◽  
Vol 16 (4) ◽  
pp. 479-490 ◽  

The use of neuroimaging approaches to identify likely treatment outcomes in patients with major depressive disorder is developing rapidly. Emerging work suggests that resting state pretreatment metabolic activity in the fronto-insular cortex may distinguish between patients likely to respond to psychotherapy or medication and may function as a treatment-selection biomarker. In contrast, high metabolic activity in the subgenual anterior cingulate cortex may be predictive of poor outcomes to both medication and psychotherapy, suggesting that nonstandard treatments may be pursued earlier in the treatment course. Although these findings will require replication before clinical adoption, they provide preliminary support for the concept that brain states can be measured and applied to the selection of a specific treatment most likely to be beneficial for an individual patient.


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