Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 295 for “"treatment effect"”.
-
Innovative Methods for Treatment Effect Heterogeneity & Calibration
Treatment effect heterogeneity, which pertains to the identification of subgroups that have varyingresponses to the same treatment, is an increasingly important topic for personalized medicine and for validating the robustness of a novel treatment. Virtual Twins is an interpretable and flexible …
-
Rethinking Machine Learning for Heterogeneous Treatment Effect Estimation
The need to estimate the causal effect that a treatment, policy or other intervention had on an outcome variable is a problem which ubiquitously appears in fields ranging from economics to marketing and medicine. Historically, population average effects have been the main estimand of interest, …
-
Statistical Inference for the Treatment Effect in Cancer Clinical Trials
… clinical trials provide the best evidence on the effect of treatment studied. There are different types of measures on the treatment effect, depending on the endpoints of the trials. For a given measure, based on the data from clinical trials, various statistical procedures are available for the …
-
Causal inference for complex data: randomization inference for treatment effect heterogeneity, network outcomes, and subgroup specific effects
… method extends inference for the ``attributable effect'', the total of the difference of outcomes if the treatment group had instead been assigned to the control condition, to count and continuous data using a fast approximation algorithm. Alternative approaches are limited to binary data, …
-
Sensitivity Analysis for Non-Ignorable Dropout of Marginal Treatment Effect in Longitudinal Trials for G-Computation Based Estimators
We specify identifying assumptions under which linear increments (LI) estimator can be used to estimate unconditional expectation for longitudinal data from a clinical trial in the presence of dropout. We show that these are analog conditions under which extended linear SWEEP estimator achieves …
-
Analysis of Treatment Effect of Lamivudine, Adefovir Add-on Therapy in Patients with Lamivudine Resistant Chronic B-viral Hepatitis
… to ongoing lamivudine (LAM) is an established treatment modality for treatment of LAM resistance with chronic hepatitis B. We assessed the long?term efficacy (median follow-up of 20.18 months) of ADV add-on LAM therapy in 89 LAM-resistant patients. Methods: 89 patients (29.2% cirrhotics, 76.4% …
-
Analysis of Treatment Effect of Lamivudine plus Adefovir Add-on Therapy in Patients with Lamivudine Resistant Chronic B-viral Hepatitis
목적: 라미부딘은 만성 B형 간염의 1차 치료제로 널리 사용되어 왔으나 내성 발현이 잦은 단점이 있다. 이러한 단점을 극복하기 위해 아데포비어 병합 치료가 사용되고 있으나 한국인을 대상으로 한 연구 결과는 적은 편이다. 저자들은 라미부딘에 내성인 B형 만성 간염 환자를 대상으로 라미부딘과 아데포비어 병합 치료 효과와 바이러스 돌파 현상을 확인하고자 하였다. 방법: 2005년 8월부터 2010년 10월까지 본원에서 라미부딘 투여 중 내성이 발생한 후 아데포비어를 추가？？여 치료한 환자 중 12개월 이상 치료받은 93명을 대상으로 …
-
Estimating provisional causal treatment effects on reoffending for binary and multiple treatments using quasi-experimental methods for people in prison with an alcohol use disorder in England
… is known as to the efficacy of criminal justice treatment interventions for people in prison with an alcohol use disorder (AUD) to reduce reoffending. Prison-based treatment aims to provide pharmacological and psychosocial interventions that address physical and mental health needs concurrently …
-
Constructing appropriate models for meta-analyses
… studies or trials to achieve one overall treatment effect estimate and has come a long way since first appearing within medical literature 30 years ago. Most articles examine how best to combine the individual trials and measure the combined estimate. A lot of articles also examine the …
-
Quantifying balance for casual inference: An information theoretic perspective
… concerned with generating evidence of a causal effect of a policy or a treatment. The central question in any impact evaluation is what would have happened to those receiving the intervention had they not received it. Randomized controlled Trials (RCT) are best at answering this question because …
-
The Causal Impact of Information Crowd-sourcing Platform on Agricultural Supply Chain
… significant heterogeneity in post grading price treatment effect and new buyer rate. The heterogeneity in the former is driven by seller’s learning, while the heterogeneity in the latter is driven by local information availability. Difference in means analysis revealed that the difference in post …
-
Essays on semi-/non-parametric methods in econometrics
… individual structural parameters and the average treatment effects. Numerical studies suggest the sensitivity of parametric specification and the robustness of semiparametric estimation. This paper also shows that the absence of excluded instruments may result in the failure of identification, …
-
Causal Inference Beyond Estimating Average Treatment Effects
… control groups that is often called the average treatment effect (ATE), and rely on identifying assumptions to allow causal interpretation. However, more specific treatment effects beyond the ATE can be estimated under the same assumptions. For example, instead of estimating the mean of potential …
-
Statistical methods to study heterogeneity of treatment effects
… studies are designed to estimate the average treatment effect (ATE) of an intervention. Individuals may derive quantitatively, or even qualitatively, different effects from the ATE, which is called the heterogeneity of treatment effect. It is important to detect the existence of heterogeneity …
-
Statistical Analysis of Treatment Compliance for Clinical Trials using Electronic Compliance Monitoring
… hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation.
-
Nudging, transparency and rationality: An experimental investigation
… Manipulation, MBoM, defined as the difference in treatment effect between an LP and AEP intervention. I find that the AEP intervention completely failed to alter behavior, but while the LP intervention fares better at first, it tapers off towards the end of the survey and the treatment effect …
-
Machine Learning for causal Inference on Observational Data
… scientific way to make claims about cause and effect is to perform a Randomized Controlled Trial (RCT). However, although RCTs are the best way to determine causal effects, the chances to perform such rigorous scientific experiments is, most often, either impossible or unethical. The Average …
-
THE IMPACTS OF CHINA’S WHITELIST POLICY ON THE ELECTRIC VEHICLE BATTERY
… study employs a DID methodology to estimate the effect of China’s whitelist policy for battery industry, both on firm stock prices and firm performance. The results imply that the policy has some significant positive effects on stock prices. According to the results of the Dynamic Average …
-
The Efficacy Of Select Nonparametric And Distribution-Free Research Methods: Examining The Case Of Concomitant Heteroscedasticity And Effect Of Treatment
… decidedly nonrobust under variance in absence of treatment. Additionally, the performance of these procedures deteriorates under nonnormal distributions. Compared to parametric procedures, nonparametric and distribution-free research methods make minimal assumptions about underlying population …
-
Complicated Grief Treatment: What Makes It Work?
… of the putative mediators of complicated grief treatment (CGT) in an effort to gain a better understanding of the mechanisms by which the treatment exerts its effects. This three-paper dissertation utilizes data from an NIMH-funded randomized controlled trial of CGT (Shear et al., 2005), which …
Page 1 of 15