An Extension of the Propensity Score Adjustment Method for the Analysis of Group Differences in MIMIC Models

1999 ◽  
Vol 34 (4) ◽  
pp. 467-492 ◽  
Author(s):  
David Kaplan
Keyword(s):  
Propensity Score ◽  
Group Differences ◽  
Adjustment Method ◽  
Mimic Models ◽  
Propensity Score Adjustment

Using propensity score adjustment method in genetic association studies

2016 ◽  
Vol 62 ◽  
pp. 1-11 ◽  
Author(s):  
Amrita Sengupta Chattopadhyay ◽  
Ying-Chao Lin ◽  
Ai-Ru Hsieh ◽  
Chien-Ching Chang ◽  
Ie-Bin Lian ◽  
...  
Keyword(s):  
Propensity Score ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Adjustment Method ◽  
Propensity Score Adjustment

A Propensity-score-adjustment Method for Nonignorable Nonresponse

2016 ◽  
Vol 4 (2) ◽  
pp. 215-245 ◽  
Author(s):  
Minsun Kim Riddles ◽  
Jae Kwang Kim ◽  
Jongho Im
Keyword(s):  
Propensity Score ◽  
Adjustment Method ◽  
Nonignorable Nonresponse ◽  
Propensity Score Adjustment

scholarly journals Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis

2018 ◽  
Vol 28 (12) ◽  
pp. 3534-3549 ◽  
Author(s):  
Arman Alam Siddique ◽  
Mireille E Schnitzer ◽  
Asma Bahamyirou ◽  
Guanbo Wang ◽  
Timothy H Holtz ◽  
...  
Keyword(s):  
Propensity Score ◽  
Simulation Study ◽  
Antimicrobial Agents ◽  
Treatment Success ◽  
Likelihood Estimation ◽  
Multidrug Resistant ◽  
Patient Treatment ◽  
Resistant Tuberculosis ◽  
Multidrug Resistant Tuberculosis ◽  
Propensity Score Adjustment

This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis.

scholarly journals Propensity Scores: Method for Matching on Multiple Variables in Down Syndrome Research

2009 ◽  
Vol 47 (5) ◽  
pp. 348-357 ◽  
Author(s):  
Jennifer Urbano Blackford
Keyword(s):  
Down Syndrome ◽  
Propensity Score ◽  
Effect Size ◽  
Propensity Scores ◽  
Group Differences ◽  
Typically Developing ◽  
Children With Down Syndrome ◽  
Confounding Variables ◽  
Multiple Variables ◽  
Birth Data

Abstract Confounding variables can affect the results from studies of children with Down syndrome and their families. Traditional methods for addressing confounders are often limited, providing control for only a few confounding variables. This study introduces propensity score matching to control for multiple confounding variables. Using Tennessee birth data as an example, newborns with Down syndrome were compared with a group of typically developing infants on birthweight. Three approaches to matching on confounders—nonmatched, covariate matched, and propensity matched—were compared using 8 potential confounders. Fewer than half of the newborns with Down syndrome were matched using covariate matching, and the matched group was differed from the unmatched newborns. Using propensity scores, 100% of newborns with Down syndrome could be matched to a group of comparison newborns, a decreased effect size was found on newborn birthweight, and group differences were not statistically significant.

scholarly journals Impact of previous abdominal surgery on robotic-assisted rectal surgery in patients with locally advanced rectal adenocarcinoma: a propensity score matching study

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ching-Wen Huang ◽  
Wei-Chih Su ◽  
Tsung-Kun Chang ◽  
Cheng-Jen Ma ◽  
Tzu-Chieh Yin ◽  
...  
Keyword(s):  
Propensity Score ◽  
Abdominal Surgery ◽  
Propensity Score Matching ◽  
Locally Advanced ◽  
Rectal Adenocarcinoma ◽  
Rectal Surgery ◽  
Perioperative Outcomes ◽  
Group Differences ◽  
Robotic Assisted ◽  
Previous Abdominal Surgery

Abstract Background The application of minimally invasive surgery in patients with colorectal cancer (CRC) and a history of previous abdominal surgery (PAS) remains controversial. This retrospective study with propensity score matching (PSM) investigated the impact of PAS on robotic-assisted rectal surgery outcomes in patients with locally advanced rectal adenocarcinoma undergoing preoperative concurrent chemoradiotherapy (CCRT). Methods In total, 203 patients with locally advanced rectal adenocarcinoma who underwent preoperative CCRT and robotic-assisted rectal surgery between May 2013 and December 2019 were enrolled. Patients were categorized into PAS and non-PAS groups based on the PAS history. The PSM caliper matching method with 1-to-3 matches was used to match PAS patients with non-PAS. Results Of the 203 enrolled patients, 35 were PAS patients and 168 were non-PAS patients. After PSM, 32 PAS patients and 96 non-PAS patients were included for analysis. No significant between-group differences were noted in the perioperative outcomes, including median console time (165 min (PAS) vs. 175 min (non-PAS), P = 0.4542) and median operation time (275 min (PAS) vs. 290 min (non-PAS), P = 0.5943) after PSM. Postoperative recovery and overall complication rates were also similar (all P > 0.05). Moreover, the between-group differences in pathological or short-term oncological outcomes were also nonsignificant (all P > 0.05). No 30-day postoperative deaths were observed in either group. Conclusion The current results indicate that robotic-assisted surgery is safe and feasible for PAS patients with locally advanced rectal adenocarcinoma undergoing preoperative CCRT. However, future prospective randomized clinical trials are required to verify these findings.

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