Zobrazeno 1 - 10
of 912
pro vyhledávání: '"covariate adjustment"'
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-17 (2024)
Abstract Purpose We aim to thoroughly compare past and current methods that leverage baseline covariate information to estimate the average treatment effect (ATE) using data from of randomized clinical trials (RCTs). We especially focus on their perf
Externí odkaz:
https://doaj.org/article/30af2315d03e4941ba00138bc10126bd
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard rati
Externí odkaz:
https://doaj.org/article/7868ebbe3029476f927dabf1de4e6529
Autor:
Prasad, Smriti, Choubey, Manesh
Publikováno v:
International Journal of Social Economics, 2023, Vol. 51, Issue 6, pp. 741-756.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJSE-01-2023-0070
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model
Externí odkaz:
https://doaj.org/article/3e756037f1f949ecb6dafef617cd60f4
Autor:
Seungjun Ahn, Somnath Datta
Publikováno v:
BMC Genomics, Vol 24, Iss 1, Pp 1-7 (2023)
Abstract Background Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis
Externí odkaz:
https://doaj.org/article/c017579d5d8a424e8ca875c33050280f
Autor:
Jiabu Ye, Dejian Lai
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 10 (2024)
Propensity score is one of the most commonly used score functions in adjusting for covariates effect in statistical inference. It is important to understand the impact with propensity score in case some of the prespecified covariates are severely imb
Externí odkaz:
https://doaj.org/article/1053c249ae734601adfee81e6131c80d
Publikováno v:
Trials, Vol 24, Iss 1, Pp 1-10 (2023)
Abstract Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sam
Externí odkaz:
https://doaj.org/article/dfd586a1156848de8ee1e3b56ddceea8
Publikováno v:
Statistical Theory and Related Fields, Vol 7, Iss 2, Pp 159-163 (2023)
To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of
Externí odkaz:
https://doaj.org/article/fc63c682afa146028d53a521eacfc10a
Autor:
Hongjiao Liu, Wodan Ling, Xing Hua, Jee-Young Moon, Jessica S. Williams-Nguyen, Xiang Zhan, Anna M. Plantinga, Ni Zhao, Angela Zhang, Rob Knight, Qibin Qi, Robert D. Burk, Robert C. Kaplan, Michael C. Wu
Publikováno v:
Microbiome, Vol 11, Iss 1, Pp 1-19 (2023)
Abstract Background Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the associati
Externí odkaz:
https://doaj.org/article/198f7a84d6c048ee9cd0fcb3451401e4
Publikováno v:
Applied Sciences, Vol 14, Iss 9, p 3662 (2024)
Controlling for confounding bias is crucial in causal inference. Causal inference using data from observational studies (e.g., electronic health records) or imperfectly randomized trials (e.g., imperfect randomization or compliance) requires accounti
Externí odkaz:
https://doaj.org/article/e8ea7f3529d04bbaa31b2a53c2ce3937