Search results

1 – 10 of over 4000
Book part
Publication date: 7 October 2019

Xiqian Liu and Victor Borden

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect…

Abstract

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

Book part
Publication date: 21 February 2008

Michael Lechner

Lechner and Miquel (2001) approached the causal analysis of sequences of interventions from a potential outcome perspective based on selection-on-observables-type assumptions…

Abstract

Lechner and Miquel (2001) approached the causal analysis of sequences of interventions from a potential outcome perspective based on selection-on-observables-type assumptions (sequential conditional independence assumptions). Lechner (2004) proposed matching estimators for this framework. However, many practical issues that might have substantial consequences for the interpretation of the results have not been thoroughly investigated so far. This chapter discusses some of these practical issues. The discussion is related to estimates based on an artificial data set for which the true values of the parameters are known and that shares many features of data that could be used for an empirical dynamic matching analysis.

Details

Modelling and Evaluating Treatment Effects in Econometrics
Type: Book
ISBN: 978-0-7623-1380-8

Book part
Publication date: 21 May 2012

Sarah Brown

In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that…

Abstract

In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that the treated and comparison groups may have differences that lead to biased conclusions regarding treatment effectiveness. Propensity score analysis can be used to balance the differences in the groups, which can be used in a number of ways to reduce biased conclusions regarding effectiveness. A review of propensity scoring studies was conducted for this chapter, where the limited number of evaluations of criminal justice interventions using these methods was identified. Due to the small number of these studies, research was also reviewed if propensity scoring had been employed to evaluate interventions that are similar to those in criminal justice systems. These studies are used as examples to demonstrate how the methods can be used to evaluate criminal justice interventions, the different ways propensity scores can be used to analyse treatment and comparison group differences, and the strengths and limitations of this approach. It is concluded that, while not appropriate for all interventions/settings, propensity score analysis can be useful in criminal justice arenas, at least to investigate the comparability of treatment and comparison groups, with suspected non-comparability being a common weakness of traditional quasi-experimental studies and frequently cited limitation in terms of drawing efficacy conclusions from such evaluations.

Details

Perspectives on Evaluating Criminal Justice and Corrections
Type: Book
ISBN: 978-1-78052-645-4

Book part
Publication date: 30 May 2018

Paola Bertoli and Veronica Grembi

In healthcare, overuse and underuse of medical treatments represent equally dangerous deviations from an optimal use equilibrium and arouse concerns about possible implications…

Abstract

In healthcare, overuse and underuse of medical treatments represent equally dangerous deviations from an optimal use equilibrium and arouse concerns about possible implications for patients’ health, and for the healthcare system in terms of both costs and access to medical care. Medical liability plays a dominant role among the elements that can affect these deviations. Therefore, a remarkable economic literature studies how medical decisions are influenced by different levels of liability. In particular, identifying the relation between liability and treatments selection, as well as disentangling the effect of liability from other incentives that might be in place, is a task for sound empirical research. Several studies have already tried to tackle this issue, but much more needs to be done. In this chapter, we offer an overview of the state of the art in the study of the relation between liability and treatments selection. First, we reason on the theoretical mechanisms underpinning the relationship under investigation by presenting the main empirical predictions of the related literature. Second, we provide a comprehensive summary of the existing empirical evidence and its main weaknesses. Finally, we conclude by offering guidelines for further research.

Details

Health Econometrics
Type: Book
ISBN: 978-1-78714-541-2

Keywords

Book part
Publication date: 23 November 2011

Ian M. McCarthy and Rusty Tchernis

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data…

Abstract

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.

Details

Missing Data Methods: Cross-sectional Methods and Applications
Type: Book
ISBN: 978-1-78052-525-9

Keywords

Book part
Publication date: 15 June 2012

James R. Hollyer

Existing experimental and quasi-experimental results have demonstrated that both anticorruption initiatives that provide information and/or authority to the recipients of…

Abstract

Existing experimental and quasi-experimental results have demonstrated that both anticorruption initiatives that provide information and/or authority to the recipients of government programs – so-called “bottom-up” interventions – and initiatives that rely on government agencies for enforcement – “top-down” interventions – can be effective in some settings. Yet, in other instances, both forms of intervention have been found to be ineffective in combating corruption. These contrasting results strongly suggest that the effectiveness of both “top-down” and “bottom-up” anticorruption interventions is conditional on other factors. Unfortunately, the existing literature says little regarding the conditions conducive to the success of either forms of intervention. Assessing the conditional effects of anticorruption treatments poses substantial challenges for researchers – particularly for those employing experimental or quasi-experimental approaches. This chapter (1) discusses factors that may condition the effectiveness of both top-down and bottom-up interventions; (2) illustrates the difficulties in assessing these conditional relationships, with particular reference to experimental and quasi-experimental settings; and (3) suggests approaches that might mitigate these problems.

Details

New Advances in Experimental Research on Corruption
Type: Book
ISBN: 978-1-78052-785-7

Book part
Publication date: 20 August 2012

Rena M. Conti, Arielle Bernstein and David O. Meltzer

Purpose – Objective measures of a new treatment's expected ability to improve patients’ health are presumed to be significant factors influencing physicians’ treatment decisions…

Abstract

Purpose – Objective measures of a new treatment's expected ability to improve patients’ health are presumed to be significant factors influencing physicians’ treatment decisions. Physicians’ behavior may also be influenced by their patients’ disease severity and insurance reimbursement policies, firm promotional activities and public media reports. This chapter examines how objective evidence of the incremental effectiveness of novel drugs to treat cancer (“chemotherapies”) impacts the rate at which physicians’ adopt these treatments into practice, holding constant other factors.

Design/methodology – The novelty of the analysis resides in the dataset and estimation strategy employed. Data is derived from a United States population-based chemotherapy order entry system, IntrinsiQ Intellidose. Quality/price endogeneity is overcome by employing sample selection methods and an estimation strategy that exploits quality variation at the molecule-indication level. Pooled diffusion rates across molecule-indication pairs are estimated using nonparametric hazard models.

Findings – Results suggest incremental effectiveness is negatively and nonsignificantly associated with the diffusion of new chemotherapies; faster rates of diffusion are positively and significantly related to low five-year survival probabilities and measures of perceived clinical significance. Results are robust to numerous specification checks, including a measure of alternative therapeutic availability. We discuss the magnitude and potential direction of bias introduced by several threats to internal validity. Evidence of incremental effectiveness does not appear to motivate the rate of specialty physician diffusion of new medical treatment; in all models high risk of disease mortality and perceptions of therapeutic quality are significant drivers of physician use of novel chemotherapies.

Value/originality – Understanding the rate of technological advance across different clinical settings, as well as the product-, provider-, and patient-level determinants of this rate, is an important subject for future research.

Details

The Economics of Medical Technology
Type: Book
ISBN: 978-1-78190-129-8

Keywords

Book part
Publication date: 30 September 2020

Suryakanthi Tangirala

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive…

Abstract

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Book part
Publication date: 2 October 2001

Ray Brindle

Abstract

Details

Handbook of Transport Systems and Traffic Control
Type: Book
ISBN: 978-1-61-583246-0

Abstract

Details

Employee Inter- and Intra-Firm Mobility
Type: Book
ISBN: 978-1-78973-550-5

1 – 10 of over 4000