Polymatching algorithm in observational studies with multiple treatment groups

2022 ◽  
Vol 167 ◽  
pp. 107364
Author(s):  
Giovanni Nattino ◽  
Chi Song ◽  
Bo Lu
Keyword(s):  
Observational Studies ◽  
Treatment Groups ◽  
Multiple Treatment

scholarly journals Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Keyi Mou ◽  
Zhiming Li
Keyword(s):  
Binary Data ◽  
Estimation Error ◽  
Control Group ◽  
Homogeneity Test ◽  
Optimal Algorithms ◽  
Score Statistic ◽  
Correlated Binary Data ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Rate Score

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.

Abstract 154: Estimating Treatment Effects in More Than Two Treatment Groups via Propensity Score Weighting: Practical Guidance and Application from Anticoagulant Therapy in Atrial Fibrillation Study

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Gboyega Adeboyeje ◽  
Gosia Sylwestrzak ◽  
John Barron
Keyword(s):  
Atrial Fibrillation ◽  
Propensity Score ◽  
Anticoagulant Therapy ◽  
Observational Studies ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Multinomial Logistic Regression ◽  
Baseline Risk ◽  
Risk Scores ◽  
Treatment Groups

Background: The methods for estimating and assessing propensity scores in the analysis of treatment effects between two treatment arms in observational studies have been well described in the outcomes research methodology literature. However, in practice, the decision makers may need information on the comparative effectiveness of more than two treatment strategies. There’s little guidance on the estimation of treatment effects using inverse probability of treatment weights (IPTW) in studies where more than two treatment arms are to be compared. Methods: Data from an observational cohort study on anticoagulant therapy in atrial fibrillation is used to illustrate the practical steps involved in estimating the IPTW from multiple propensity scores and assessing the balance achieved under certain assumptions. For all patients in the study, we estimated the propensity score for the treatment each patient received using a multinomial logistic regression. We used the inverse of the propensity scores as weights in Cox proportional hazards to compare study outcomes for each treatment group Results: Before IPTW adjustment, there were large and statistically significant baseline differences between treatment groups in terms of demographic, plan type, and clinical characteristics including validated stroke and bleeding risk scores. After IPTW, there were no significant differences in all measured baseline risk factors. In unadjusted estimates of stroke outcome, there were large differences between dabigatran [Hazard ratio, HR, 0.59 (95% CI: 0.53 - 0.66)], apixaban [HR, 0.69 (CI: 0.57, 0.83)], rivaroxaban [HR, 0.60 (CI: 0.53 0.68)] and warfarin users. After IPTW, estimated stroke risk differences were significantly reduced or eliminated between dabigatran [HR, 0.89 (CI: 0.80, 0.98)], apixaban [HR, 0.92 (0.76, 1.10)], rivaroxaban [HR, 0.84 (CI: 0.75, 0.95)] and warfarin users. Conclusions: Our results showed IPTW methods, correctly employed under certain assumptions, are practical and relatively simple tools to control for selection bias and other baseline differences in observational studies evaluating the comparative treatment effects of more than two treatment arms. When preserving sample size is important and in the presence of time-varying confounders, IPTW methods have distinct advantages over propensity matching or adjustment.

scholarly journals Unmeasured Confounding in Observational Studies with Multiple Treatment Arms

Epidemiology ◽  
2016 ◽  
Vol 27 (5) ◽  
pp. 624-632 ◽  
Author(s):  
Junxin Shi ◽  
Bo Lu ◽  
Krista K. Wheeler ◽  
Huiyun Xiang
Keyword(s):  
Observational Studies ◽  
Unmeasured Confounding ◽  
Multiple Treatment

scholarly journals Heat map visualization of high-density clinical chemistry data

2007 ◽  
Vol 31 (2) ◽  
pp. 352-356 ◽  
Author(s):  
J. Todd Auman ◽  
Gary A. Boorman ◽  
Ralph E. Wilson ◽  
Gregory S. Travlos ◽  
Richard S. Paules
Keyword(s):  
Large Scale ◽  
Clinical Chemistry ◽  
High Density ◽  
Male Rats ◽  
Z Score ◽  
Heat Map ◽  
Density Data ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Chemistry Data

Clinical chemistry data are routinely generated as part of preclinical animal toxicity studies and human clinical studies. With large-scale studies involving hundreds or even thousands of samples in multiple treatment groups, it is currently difficult to interpret the resulting complex, high-density clinical chemistry data. Accordingly, we conducted this study to investigate methods for easy visualization of complex, high-density data. Clinical chemistry data were obtained from male rats each treated with one of eight different acute hepatotoxicants from a large-scale toxicogenomics study. The raw data underwent a Z-score transformation comparing each individual animal's clinical chemistry values to that of reference controls from all eight studies and then were visualized in a single graphic using a heat map. The utility of using a heat map to visualize high-density clinical chemistry data was explored by clustering changes in clinical chemistry values for >400 animals. A clear distinction was observed in animals displaying hepatotoxicity from those that did not. Additionally, while animals experiencing hepatotoxicity showed many similarities in the observed clinical chemistry alterations, distinct differences were noted in the heat map profile for the different compounds. Using a heat map to visualize complex, high-density clinical chemistry data in a single graphic facilitates the identification of previously unrecognized trends. This method is simple to implement and maintains the biological integrity of the data. The value of this clinical chemistry data transformation and visualization will manifest itself through integration with other high-density data, such as genomics data, to study physiology at the systems level.

On estimating average effects for multiple treatment groups

2012 ◽  
Vol 32 (11) ◽  
pp. 1829-1841 ◽  
Author(s):  
V. Landsman ◽  
R.M. Pfeiffer
Keyword(s):  
Treatment Groups ◽  
Multiple Treatment

scholarly journals Evaluating Glaucoma Treatment Effect on Intraocular Pressure Reduction Using Propensity Score Weighted Regression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mengfei Wu ◽  
Mengling Liu ◽  
Joel S. Schuman ◽  
Yuyan Wang ◽  
Katie A. Lucy ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
Propensity Score ◽  
Selection Bias ◽  
Treatment Effect ◽  
Laser Surgery ◽  
Observational Studies ◽  
Combination Therapies ◽  
Treatment Groups ◽  
Valid Assessment ◽  
Important Evidence

Abstract Observational studies in glaucoma patients can provide important evidence on treatment effects, especially for combination therapies which are often used in reality. But the success relies on the reduction of selection bias through methods such as propensity score (PS) weighting. The objective of this study was to assess the effects of five glaucoma treatments (medication, laser, non-laser surgery (NLS), laser + medication, and NLS + medication) on 1-year intraocular pressure (IOP) change. Data were collected from 90 glaucoma subjects who underwent a single laser, or NLS intervention, and/or took the same medication for at least 6 months, and had IOP measures before the treatment and 12-months after. Baseline IOP was significantly different across groups (p = 0.007) and this unbalance was successfully corrected by the PS weighting (p = 0.81). All groups showed statistically significant PS-weighted IOP reductions, with the largest reduction in NLS group (−6.78 mmHg). Baseline IOP significantly interacted with treatments (p = 0.03), and at high baseline IOP medication was less effective than other treatments. Our findings showed that the 1-year IOP reduction differed across treatment groups and was dependent on baseline IOP. The use of PS-weighted methods reduced treatment selection bias at baseline and allowed valid assessment of the treatment effect in an observational study.

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