ELIZA

return kwic search for effects out of >500 occurrences
566982 occurrences (No.17 in the rank) during 5 years in the PubMed. [cache]

414) We propose a two-stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta-analysis methods where individual patient data are available.

--- ABSTRACT ---

PMID:34048066 DOI:10.1002/sim.9034

2021 Statistics in medicine

* A two-stage prediction model for heterogeneous effects of treatments.

- Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision-making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta-analysis framework. We propose a two-stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta-analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta-regression model. We apply the approach to a network meta-analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing-remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high-risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low-risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.

--- ABSTRACT END ---

[
right
kwic]

[frequency of next (right) word to effects]

(1)230 of	(8)6 between	(15)3 may	(22)2 from
(2)71 on	(9)6 such	(16)3 model	(23)2 meta-analysis
(3)34 null	(10)5 is	(17)3 that	(24)2 or
(4)22 in	(11)3 at	(18)3 with	(25)2 reported
(5)12 were	(12)3 but	(19)2 against
(6)11 and	(13)3 by	(20)2 as
(7)10 are	(14)3 can	(21)2 for

add keyword

--- WordNet output for effects --- =>個人資産 Overview of noun effects The noun effects has 1 sense (no senses from tagged texts) 1. effects, personal effects -- (property of a personal character that is portable but not used in business; "she left some of her personal effects in the house"; "I watched over their effects until they returned") Overview of noun effect The noun effect has 6 senses (first 5 from tagged texts) 1. (101) consequence, effect, outcome, result, event, issue, upshot -- (a phenomenon that follows and is caused by some previous phenomenon; "the magnetic effect was greater when the rod was lengthwise"; "his decision had depressing consequences for business"; "he acted very wise after the event") 2. (11) impression, effect -- (an outward appearance; "he made a good impression"; "I wanted to create an impression of success"; "she retained that bold effect in her reproductions of the original painting") 3. (9) effect -- (an impression (especially one that is artificial or contrived); "he just did it for effect") 4. (2) effect, essence, burden, core, gist -- (the central meaning or theme of a speech or literary work) 5. (1) effect, force -- ((of a law) having legal validity; "the law is still in effect") 6. effect -- (a symptom caused by an illness or a drug; "the effects of sleep loss"; "the effect of the anesthetic") Overview of verb effect The verb effect has 2 senses (first 2 from tagged texts) 1. (17) effect, effectuate, set up -- (produce; "The scientists set up a shock wave") 2. (3) effect -- (act so as to bring into existence; "effect a change") --- WordNet end ---