scholarly journals Propensity score weighting for covariate adjustment in randomized clinical trials

2020 ◽  
Author(s):  
Shuxi Zeng ◽  
Fan Li ◽  
Rui Wang ◽  
Fan Li
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Randomized Clinical Trials ◽  
Covariate Adjustment ◽  
Propensity Score Weighting

scholarly journals Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach

2021 ◽  
pp. 174077452110285
Author(s):  
Siyun Yang ◽  
Fan Li ◽  
Laine E Thomas ◽  
Fan Li
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Exercise Training ◽  
Randomized Clinical Trials ◽  
Analysis Of Covariance ◽  
Covariate Adjustment ◽  
Operating Characteristics ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Subgroup Analyses

Background: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. Methods: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate–subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. Results: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. Conclusion: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate–subgroup interactions in the propensity score model.

Comparative effectiveness of treatment options in elderly patients with colon cancer receiving adjuvant chemotherapy.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 470-470
Author(s):  
Sacha Satram-Hoang ◽  
Devi Ramanan ◽  
Luen F. Lee ◽  
Shui Yu ◽  
Carolina M. Reyes ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Clinical Trials ◽  
Propensity Score ◽  
Elderly Patients ◽  
Older Patients ◽  
Randomized Clinical Trials ◽  
Cohort Analysis ◽  
Risk Of Death ◽  
Morbidity Score ◽  
Co Morbidity

470 Background: While colon cancer (CC) is predominantly a disease of the elderly, older patients are underrepresented in clinical trials. We sought to evaluate whether the treatment patterns and benefits realized by trial participants pertain to older patients in the real-world setting. Methods: Using the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database, we conducted a retrospective cohort analysis of 3390 stage II and III CC patients diagnosed between 1/1/ 2004 to 12/31/2007, who were >66 years, enrolled in Medicare Parts A and B, and received adjuvant treatment with 5FU/LV (n=1368), FOLFOX (n=1398), CAP (capecitabine; n=507), and CAPOX (CAP + oxaliplatin; n=117) within 3 months after surgery. Date of last follow-up was 12/31/2007. Chi-square test and ANOVA or t-test assessed differences in patient and disease characteristics by treatment. Propensity score weighted Cox regression assessed the relative risk of death by treatment. Results: Patients treated with CAP were older (mean age 77 years; p<.0001), more likely female (61%; p<.05), more likely non-white (19%; p<.05) and had higher co-morbidity score (p<.0001) compared to the other treatment groups. The mean time to chemo initiation after surgery were similar between the groups (mean 46-49 days) while mean duration of treatment were longer for 5FU/LV (149 days) and FOLFOX (144 days), compared to CAP (121 days) and CAPOX (111 days); p<.0001. The incidence of adverse events (AEs) within 180 days after initiation of treatment were higher in patients treated with FOLFOX (82%) and 5FU/LV (78%) compared to CAP (74%) and CAPOX (71%); p=0.0002. Propensity score adjusted multivariate analysis demonstrated comparable survival for CAP-based regimens vs. 5-FU/LV- based regimens ( table ). Conclusions: Treatment outcomes for elderly patients observed in routine clinical practice were comparable between CAP-based and 5FU/LV-based regimens and consistent with results reported in randomized clinical trials. AEs associated with medical resource utilization were less frequent with CAP-based regimens. [Table: see text]

scholarly journals Evidence-based approaches to comparing the effectiveness of modern cardiology interventions: trends, bias and prospects

2020 ◽  
Vol 25 (8) ◽  
pp. 4037
Author(s):  
S. R. Gilyarevsky ◽  
Yu. N. Belenkov
Keyword(s):  
Clinical Trials ◽  
Clinical Practice ◽  
Drug Use ◽  
Propensity Score ◽  
Observational Studies ◽  
Large Scale ◽  
Randomized Clinical Trials ◽  
Evidence Based ◽  
Improved Methods

The article discusses the limitations of the evidence from observational studies. Modern approaches to reducing bias in observational studies are discussed in detail, in particular, propensity score matching, which has become popular in recent years. The main differences between randomized and observational studies are discussed. Arguments against the observational studies and improved methods of analysis to compare the treatments’ effectiveness in clinical practice are presented. The role of observational studies as a source of evidence is discussed. The article points out the validity of performing large-scale prospective observational studies to assess the effects of postmarketing drug use in clinical practice, as well as to obtain data on drug use in patients that differ from those in randomized clinical trials.

Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

2021 ◽  
Author(s):  
David Guy ◽  
Igor Karp ◽  
Piotr Wilk ◽  
Joseph Chin ◽  
George Rodrigues
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Data ◽  
Randomized Clinical Trials ◽  
Original Data ◽  
Considerable Proportion ◽  
Exact Matching ◽  
Coarsened Exact Matching ◽  
Comparison Groups

Aim & methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from randomized clinical trials to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in randomized clinical trials. Conclusion: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.

Subgroup Analysis and Covariate Adjustment in Randomized Clinical Trials of Traumatic Brain Injury: A Systematic Review

Neurosurgery ◽  
2005 ◽  
Vol 57 (6) ◽  
pp. 1244-1253 ◽  
Author(s):  
Adrían V. Hernández ◽  
Ewout W. Steyerberg ◽  
Gillian S. Taylor ◽  
Anthony Marmarou ◽  
J Dik F. Habbema ◽  
...  
Keyword(s):  
Systematic Review ◽  
Traumatic Brain Injury ◽  
Clinical Trials ◽  
Brain Injury ◽  
Subgroup Analysis ◽  
Randomized Clinical Trials ◽  
Covariate Adjustment

scholarly journals Restricted mean survival time: Does covariate adjustment improve precision in randomized clinical trials?

2018 ◽  
Vol 15 (2) ◽  
pp. 178-188 ◽  
Author(s):  
Theodore Karrison ◽  
Masha Kocherginsky
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Survival Time ◽  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Linear Models ◽  
Covariate Adjustment ◽  
Survival Times ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Background: Restricted mean survival time is a measure of average survival time up to a specified time point. There has been an increased interest in using restricted mean survival time to compare treatment arms in randomized clinical trials because such comparisons do not rely on proportional hazards or other assumptions about the nature of the relationship between survival curves. Methods: This article addresses the question of whether covariate adjustment in randomized clinical trials that compare restricted mean survival times improves precision of the estimated treatment effect (difference in restricted mean survival times between treatment arms). Although precision generally increases in linear models when prognostic covariates are added, this is not necessarily the case in non-linear models. For example, in logistic and Cox regression, the standard error of the estimated treatment effect does not decrease when prognostic covariates are added, although the situation is complicated in those settings because the estimand changes as well. Because estimation of restricted mean survival time in the manner described in this article is also based on a model that is non-linear in the covariates, we investigate whether the comparison of restricted mean survival times with adjustment for covariates leads to a reduction in the standard error of the estimated treatment effect relative to the unadjusted estimator or whether covariate adjustment provides no improvement in precision. Chen and Tsiatis suggest that precision will increase if covariates are chosen judiciously. We present results of simulation studies that compare unadjusted versus adjusted comparisons of restricted mean survival time between treatment arms in randomized clinical trials. Results: We find that for comparison of restricted means in a randomized clinical trial, adjusting for covariates that are associated with survival increases precision and therefore statistical power, relative to the unadjusted estimator. Omitting important covariates results in less precision but estimates remain unbiased. Conclusion: When comparing restricted means in a randomized clinical trial, adjusting for prognostic covariates can improve precision and increase power.

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