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Real-World Data and Real-World Evidence
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
The propensity score was proposed by Rosenbaum and Rubin in 198361. The propensity score is a function of the covariates, which is defined as the probability of receiving treatment (or exposure) conditional on observed covariates. In conventional analysis, the propensity score was typically estimated from regression models, such as a logistic regression or a probit regression of the treatment conditional on the covariates. In practice analysis, treatment variables are often multiple categorical or continuous, and hence generalized propensity scores should be used instead, the methods for which have been proposed by Imbens 62 and Hirano&Imbens 63, respectively. Generalized propensity scores can be estimated by regression model (such as logistic and probit regression model), machine learning (such as gradient boosting machine), the covariate balancing generalized propensity score based on generalized method of moments, and so forth.
Cancer Epidemiology
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
Propensity scoring is a method of balancing groups by their baseline variables. Each individual in the cohort is given a score, which is the probability the individual would be in the exposed group, given their baseline data. Let the baseline covariates be and let if an individual is in the exposed group and if in the non-exposed group. The propensity score, , is defined as
Preliminaries
Published in Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway, Eric B. Laber, Dynamic Treatment Regimes, 2019
Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway, Eric B. Laber
Thus, if the no unmeasured confounders assumption holds, then it also holds if we consider only the propensity score. This result is useful because the collection of covariates X that may be required to make the assumption of no unmeasured confounders tenable could be high dimensional, making it challenging to posit an outcome regression model Q(x, a; β) for E(Y|X = x, A = a). The propensity score π(X) is a one-dimensional function of X and can take on only values between 0 and 1. Thus, if π(X) were known, developing models for E{Y|π(X) = π(x), A = a} is likely to be considerably easier.
A 24-month updated analysis of the comparative effectiveness of ZUMA-5 (axi-cel) vs. SCHOLAR-5 external control in relapsed/refractory follicular lymphoma
Published in Expert Review of Anticancer Therapy, 2023
M Lia Palomba, Paola Ghione, Anik R Patel, Myrna Nahas, Sara Beygi, Anthony J Hatswell, Steve Kanters, Eve H. Limbrick-Oldfield, Sally W Wade, Markqayne D Ray, Jessica Owen, Sattva S Neelapu, John Gribben, John Radford, Sabela Bobillo
At 24 months, this study provides the longest available follow-up for a comparison between CAR-T and an external control cohort in r/r FL. The ZUMA-5 trial, with a minimum follow-up of 24-months, was compared to the SCHOLAR-5 external control cohort, which is comprised of patients from seven international cancer centers and post-trial patients from the DELTA trial. In order to minimize the issues of a non-randomized study design, propensity score methods were used to align the treatment groups with respect to effect-modifiers and prognostic factors. The efficacy benefit of axi-cel relative to the standard of care that was observed in the 18-month analysis was maintained at 24 months, suggesting that the treatment effect of axi-cel is durable. As the SCHOLAR-5 comparator remained the same, any changes in the comparison are solely due to events that occurred in the 6-month additional follow-up in ZUMA-5.
FOLFIRINOX versus gemcitabine plus nab-paclitaxel as the first-line chemotherapy in metastatic pancreatic cancer
Published in Journal of Chemotherapy, 2022
Seval Ay, Muhammed Mustafa Atcı, Rukiye Arıkan, Özgecan Dülgar, Deniz Tataroğlu Özyükseler, Nail Paksoy, İzzet Doğan, Buğra Öztosun, Didem Taştekin, Başak Bala Öven, Mahmut Gümüş
This study has some limitations. Firstly, it was designed as a retrospective study. Patients’ characteristics of two groups (Age, ECOG, and CRP) were unbalanced due to this retrospective design. Additionally, markers for targeted therapies and immunotherapies were not investigated in the study population. The immune-rich subtype has a significantly better prognosis and opportunity for targeted and immune therapies. The propensity score method is a statistical method that is used to remove confounding factors on treatment response outcomes and have been proposed to have a better balance of prognostic factors between groups. In the present study, this method was not feasible to use because of the number of patients. Further prospective clinical studies designed with a high number of patients are needed to compare FFX and GNP in all settings, adjuvant, locally advanced, and metastatic lines.
Effects of foam rolling on hip pain in patients with hip osteoarthritis: a retrospective propensity-matched cohort study
Published in Physiotherapy Theory and Practice, 2022
Hisashi Ikutomo, Koutatsu Nagai, Keiichi Tagomori, Namika Miura, Kenichi Okamura, Takato Okuno, Norikazu Nakagawa, Kensaku Masuhara
The 115 patients in the FR and non-FR groups were retrospectively matched in a 1:1 ratio using a propensity-score algorithm to adjust for baseline differences between the two groups. Propensity-score matching is a well-validated statistical technique that creates comparable groups and allows for the accurate assessment of treatment effect (Stukel et al., 2007). We estimated the scores using a multivariable logistic regression model. Patients were matched for age, sex, body mass index (BMI), Kellgren and Lawrence grade in the involved limb, and hip pain VAS at baseline. Baseline characteristics and outcome measurements between the FR and non-FR groups were compared using the Mann-Whitney U test for continuous variables and the chi-squared test for categorical variables. Effects of interventions on outcome measurements in the groups were compared using split-plot design variance analysis (ANOVA). If the ANOVA showed significant interactions, Bonferroni post-hoc test was used to identify the mean differences. Additionally, each matched group was classified as a high or low group according to the radiographic progression of hip osteoarthritis. We defined high groups as baseline Kellgren and Lawrence grade of 2–4 in the involved limb. The changes in hip pain VAS were compared after the intervention in the four groups using Kruskal-Wallis and Bonferroni post-hoc tests. Statistical significance was set at the level of p < .05. All analyzes were performed using IBM SPSS software (IBM Japan, Tokyo, Japan).