Multigroup Propensity Score Approach to Evaluating an Effectiveness Trial of the New Beginnings Program

Tein, Jenn Yun; Mazza, Gina L.; Gunn, Heather; Kim, Hanjoe; Stuart, Elizabeth A.; Sandler, Irwin N.; Wolchik, Sharlene A.

doi:10.1177/0163278718763499

Cited by 21 publications

(16 citation statements)

References 66 publications

(83 reference statements)

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“…All the baseline measured factors including patient demographics, comorbidities, hospital characteristics, gynecologic disease type, and surgery type were entered in the model. Balance statistics were used in the post-weighted model, and a standardized difference of >0.2 indicated the presence of a size effect for clinical imbalance across the three group [35,36]. After weighting, a generalized estimating equation model was fitted to estimate the effect size for perioperative complications (upper-body complication and overall perioperative complication) across the three hysterectomy groups, expressed with odds ratios (OR) and corresponding 95% confidence intervals (CI).…”

Section: Discussionmentioning

confidence: 99%

Risk of Upper-body Adverse Events in Robot-assisted Total Laparoscopic Hysterectomy for Benign Gynecologic Disease

Matsuo

Mandelbaum

Nusbaum

et al. 2021

Journal of Minimally Invasive Gynecology

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Discussionmentioning

confidence: 99%

Risk of Upper-body Adverse Events in Robot-assisted Total Laparoscopic Hysterectomy for Benign Gynecologic Disease

Matsuo

Mandelbaum

Nusbaum

et al. 2021

Journal of Minimally Invasive Gynecology

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“…Post‐weighted models were assessed for the balance statistics; a standardized difference of >0.2 indicated imbalance across the three groups 22,23 . After the weight modeling, a generalized estimating equation model was fitted to estimate the effect size of perioperative complications for the class I‐II and class III obesity groups vs the non‐obesity group, 24 expressed with OR and 95% CI.…”

Section: Methodsmentioning

confidence: 99%

National trends and outcomes of morbidly obese women who underwent inpatient hysterectomy for benign gynecological disease in the USA

Matsuo

Mandelbaum

Nusbaum

et al. 2020

Acta Obstet Gynecol Scand

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Introduction The US population has witnessed an epidemic expansion of obesity in the past several decades; nearly 50% of the population is projected to be obese by 2030 and 25% morbidly obese. This study examined trends, characteristics and outcomes of morbidly obese women who underwent benign hysterectomy. Material and methods This is a population‐based retrospective observational study querying the National Inpatient Sample from January 2012 to September 2015. The study population included 509 395 women who underwent hysterectomy for benign gynecological disease: 430 865 (84.6%) non‐obese women, 50 435 (9.9%) women with class I‐II obesity and 28 095 (5.5%) women with class III obesity. Main outcome measures were (i) cohort‐level trends of obesity and perioperative complications assessed by piecewise linear regression with log transformation and (ii) patient‐level perioperative complication risk by body habitus assessed with a generalized estimating equation after using a multiple‐group generalized boosted model. Results The rate of class III obesity increased by 40.4%, higher than the rate of class I‐II obesity (22.2%) (both, P < .001). In parallel, cohort‐level rates of perioperative complication and prolonged hospitalization for ≥7 days increased by 19.4% and 54%, respectively (P < .001). In a weighted model, class I‐II obesity (16.4% vs 14.6%, odds ratio 1.15, 95% confidence interval 1.08‐1.21) and class III obesity (19.2% vs 14.6%, odds ratio 1.39, 95% confidence interval 1.28‐1.51) had a significantly increased risk of perioperative complications compared with non‐obesity. Larger body habitus was associated with higher total charge (median, $35 180, $36 094 and $39 382; all values cited in US dollars) and prolonged admission rate for ≥7 days (2.9%, 3.1% and 3.9%) (both, P < .001). Conclusions The rate of obesity, particularly morbid obesity, has significantly increased among women undergoing benign hysterectomy in the USA. Morbidly obese women had adverse perioperative outcomes, and the increasing number of morbidly obese women resulted in both an increased perioperative morbidity and total charges as a cohort. National and society‐based approaches are necessary to reduce the obesity rate and hysterectomy morbidity.

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“…Observational studies have gained traction in recent years, owing to large amounts of available historical data, in which objects can be been observed, recorded, and compared, albeit without random treatment allocation (2,4). However, a major disadvantage of this approach is the presence of systematic differences in baseline covariates related to outcomes between treatment and control groups, which biases the results (1,5). To accurately estimate treatment effects from observational data, analysts have proposed several methods for balancing data, such as matching, stratification, and regression adjustments; however, each of these methods has its respective deficiency: (I) matching and stratification approaches group together individuals with the same or similar covariates, but these methods fail when too many covariates require balancing; i.e., it is difficult to find individuals for whom all covariates are similar.…”

Section: Where Is It Used?mentioning

confidence: 99%

Propensity score matching with R: conventional methods and new features

Zhao¹,

Luo²,

Su³

et al. 2021

Ann Transl Med

148

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It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. However, observational studies also have their drawbacks, mainly including the systematic differences in baseline covariates, which relate to outcomes between treatment and control groups that can potentially bias results.Propensity score methods, which are a series of balancing methods in these studies, have become increasingly popular by virtue of the two major advantages of dimension reduction and design separation. Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets. While PSM tutorials are available in the literature, there is still room for improvement. Some PSM tutorials provide step-by-step guidance, but only one or two packages have been covered, thereby limiting their scope and practicality. Several articles and books have expounded upon propensity scores in detail, exploring statistical principles and theories; however, the lack of explanations on function usage in programming language has made it difficult for researchers to understand and follow these materials. To this end, this tutorial was developed with a six-step PSM framework, in which we summarize the recent updates and provide step-by-step guidance to the R programming language. This tutorial offers researchers with a broad survey of PSM, ranging from data preprocessing to estimations of propensity scores, and from matching to analyses. We also explain generalized propensity scoring for multiple or continuous treatments, as well as time-dependent PSM. Lastly, we discuss the advantages and disadvantages of propensity score methods.

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Multigroup Propensity Score Approach to Evaluating an Effectiveness Trial of the New Beginnings Program

Cited by 21 publications

References 66 publications

Risk of Upper-body Adverse Events in Robot-assisted Total Laparoscopic Hysterectomy for Benign Gynecologic Disease

Risk of Upper-body Adverse Events in Robot-assisted Total Laparoscopic Hysterectomy for Benign Gynecologic Disease

National trends and outcomes of morbidly obese women who underwent inpatient hysterectomy for benign gynecological disease in the USA

Propensity score matching with R: conventional methods and new features

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