scholarly journals Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization

2020 ◽  
Vol 11 (3) ◽  
pp. 957-982
Author(s):  
Yichong Zhang ◽  
Xin Zheng
Keyword(s):  
Propensity Score ◽  
Quantile Regression ◽  
Standard Error ◽  
Wald Test ◽  
Estimation Methods ◽  
Compact Set ◽  
Nominal Level ◽  
Adaptive Randomization ◽  
Asymptotic Size ◽  
Quantile Treatment Effect

In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate‐adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets.

Improved method for estimating Ca uptake in vascular smooth muscle using compartmental analysis

1990 ◽  
Vol 259 (1) ◽  
pp. R172-R183 ◽  
Author(s):  
H. A. Massaldi ◽  
J. Copello ◽  
A. Muller ◽  
M. F. Villamil
Keyword(s):  
Smooth Muscle ◽  
Vascular Smooth Muscle ◽  
Standard Error ◽  
Compartmental Analysis ◽  
Compartment Model ◽  
Parameter Estimate ◽  
Estimation Methods ◽  
Standard Errors ◽  
Influx Rate ◽  
Ca Uptake

A comparative study on the modeling aspects of Ca uptake in vascular smooth muscle is presented with particular emphasis on determination of the influx rate and its standard error for one- and two-compartment models. Experimental data from our laboratory of 45Ca uptake by dog carotid arteries were optimally fitted to a one-compartment model and were used to compare different estimation methods and experiment designs. Reparameterization of the model equation yielded an expression that allows direct estimation of the influx rate and its standard error. Experiment design with replicated sampling at three to four times were found to provide the highest estimation precision and successful comparisons of influx rates under treatment and control conditions. Two-compartment model data reported in the literature for Ca uptake by cells were reprocessed, yielding standard errors for the rate constant of the fast component an order of magnitude larger than the mean estimate. For this case, a three-parameter variant of the one-compartment model was developed that described the data with acceptable standard errors. Overall we found that the choice of the model that fitted Ca uptake data best required consideration of parameter estimate precision comparisons in addition to F tests of significance between alternate models.

scholarly journals INSTRUMENTAL VARIABLE QUANTILE REGRESSION WITH MISCLASSIFICATION

2020 ◽  
pp. 1-36
Author(s):  
Takuya Ura
Keyword(s):  
Quantile Regression ◽  
Instrumental Variable ◽  
Reduced Form ◽  
Outcome Variable ◽  
Annual Review ◽  
Monotonicity Condition ◽  
Treatment Variable ◽  
Treatment Status ◽  
Instrumental Variable Quantile Regression ◽  
Quantile Treatment Effect

This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics, 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the structural quantile treatment effect under the stochastic monotonicity condition. This result is relevant, not only when the treatment variable is subject to misclassification, but also when any measurement of the treatment variable is not available. The second result is for the structural quantile function when the treatment status is measured with error; the sharp identified set is characterized by a set of moment conditions under widely used assumptions on the measurement error. Furthermore, an inference method is provided in the presence of other covariates.

ON COMPARISON OF ESTIMATION METHODS IN QUANTILE REGRESSION

2018 ◽  
Vol 52 (3) ◽  
pp. 203-213
Author(s):  
Song Jea Woo ◽  
Kee-Hoon Kang
Keyword(s):  
Quantile Regression ◽  
Estimation Methods

Is the classical Wald test always suitable under response-adaptive randomization?

2016 ◽  
Vol 27 (8) ◽  
pp. 2294-2311 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Alessandro Vagheggini ◽  
Maroussa Zagoraiou
Keyword(s):  
Sample Size ◽  
Power Function ◽  
Wald Test ◽  
Correct Choice ◽  
Consistent Estimator ◽  
Initial Sample ◽  
Normal Response ◽  
Adaptive Randomization ◽  
Classical Test ◽  
The Impact

The aim of this paper is to analyze the impact of response-adaptive randomization rules for normal response trials intended to test the superiority of one of two available treatments. Taking into account the classical Wald test, we show how response-adaptive methodology could induce a consistent loss of inferential precision. Then, we suggest a modified version of the Wald test which, by using the current allocation proportion to the treatments as a consistent estimator of the target, avoids some degenerate scenarios and so it should be preferable to the classical test. Furthermore, we show both analytically and via simulations how some target allocations may induce a locally decreasing power function. Thus, we derive the conditions on the target guaranteeing its monotonicity and we show how a correct choice of the initial sample size allows one to overcome this drawback regardless of the adopted target.

ASYMPTOTIC SIZE AND A PROBLEM WITH SUBSAMPLING AND WITH THE m OUT OF n BOOTSTRAP

2009 ◽  
Vol 26 (2) ◽  
pp. 426-468 ◽  
Author(s):  
Donald W.K. Andrews ◽  
Patrik Guggenberger
Keyword(s):  
Limit Distribution ◽  
Test Statistic ◽  
Nominal Level ◽  
Asymptotic Size ◽  
Bootstrap Tests ◽  
Exact Size ◽  
High Level

This paper considers inference based on a test statistic that has a limit distribution that is discontinuous in a parameter. The paper shows that subsampling and m out of n bootstrap tests based on such a test statistic often have asymptotic size—defined as the limit of exact size—that is greater than the nominal level of the tests. This is due to a lack of uniformity in the pointwise asymptotics. We determine precisely the asymptotic size of such tests under a general set of high-level conditions that are relatively easy to verify. The results show that the asymptotic size of subsampling and m out of n bootstrap tests is distorted in some examples but not in others.

Propensity Score Adjusted Comparison of MIDCAB versus Full Sternotomy Left Anterior Descending Artery Revascularization

2015 ◽  
Vol 10 (3) ◽  
pp. 174-178 ◽  
Author(s):  
Shahzad G. Raja ◽  
Umberto Benedetto ◽  
Eman Alkizwini ◽  
Sapna Gupta ◽  
Mohamed Amrani
Keyword(s):  
Propensity Score ◽  
Standard Error ◽  
Artery Bypass ◽  
Length Of Hospital Stay ◽  
Coronary Intervention ◽  
Long Term Results ◽  
Attractive Alternative ◽  
Long Term Outcomes ◽  
Short And Long Term

Objective Minimally invasive direct coronary artery bypass (MIDCAB) has been proposed as an attractive alternative to full sternotomy (FS) revascularization in isolated left anterior descending (LAD) artery disease not suitable for percutaneous coronary intervention. However, surgeons are still reluctant to perform MIDCAB owing to concerns about early and late outcomes. We aimed to compare short- and long-term outcomes after MIDCAB versus FS revascularization. Methods Prospectively collected data from institutional database were reviewed. Data for late mortality were obtained from the General Register Office. MIDCAB was performed in 318 patients, whereas 159 had FS, according to the surgeon's preference, among 477 patients with isolated LAD disease. Inverse propensity score weighting was used to estimate treatment effects on short- and long-term outcomes. Results In the propensity score-adjusted analysis, FS revascularization versus MIDCAB was associated increased rate of surgical site infection [4 (2.8%) versus 1 (0.7%); P = 0.04]. The 2 groups did not significantly differ with regard to other complications including operative mortality. Mean length of hospital stay was similar for the 2 groups. After a mean follow-up time of 6.2 years (interquartile range, 3.5–9.7 years), compared to MIDCAB, FS was not associated with an improved late survival (β coef, −1.42; standard error, 1.65; P = 0.39) or risk reduction for repeat revascularization (β coef, 1.22; standard error, 1.41; P = 0.15). Conclusions MIDCAB was associated with a trend toward better short-term outcomes and excellent long-term results comparable to FS revascularization. According to these findings, surgeons should not be reluctant to perform MIDCAB in isolated LAD disease.

scholarly journals Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

2020 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Michelle Lee Maroto
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Regression Models ◽  
Wage Distribution ◽  
Motherhood Penalty ◽  
Unconditional Quantile Regression ◽  
Quantile Treatment Effect

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

scholarly journals Estimating the Effectiveness of Health-Risk Communications with Propensity-Score Matching: Application to Arsenic Groundwater Contamination in Four US Locations

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Andrew J. Leidner
Keyword(s):  
Propensity Score ◽  
Health Risk ◽  
Risk Communication ◽  
Propensity Score Matching ◽  
Arsenic Contamination ◽  
Estimation Methods ◽  
Future Research ◽  
Two Stage ◽  
Level Indicator ◽  
Propensity Score Model

This paper provides a demonstration of propensity-score matching estimation methods to evaluate the effectiveness of health-risk communication efforts. This study develops a two-stage regression model to investigate household and respondent characteristics as they contribute to aversion behavior to reduce exposure to arsenic-contaminated groundwater. The aversion activity under study is a household-level point-of-use filtration device. Since the acquisition of arsenic contamination information and the engagement in an aversion activity may be codetermined, a two-stage propensity-score model is developed. In the first stage, the propensity for households to acquire arsenic contamination information is estimated. Then, the propensity scores are used to weight observations in a probit regression on the decision to avert the arsenic-related health risk. Of four potential sources of information, utility, media, friend, or others, information received from a friend appears to be the source of information most associated with aversion behavior. Other statistically significant covariates in the household’s decision to avert contamination include reported household income, the presence of children in household, and region-level indicator variables. These findings are primarily illustrative and demonstrate the usefulness of propensity-score methods to estimate health-risk communication effectiveness. They may also be suggestive of areas for future research.

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