scholarly journals Insights on Variance Estimation for Blocked and Matched Pairs Designs

2020 ◽  
pp. 107699862094627
Author(s):  
Nicole E. Pashley ◽  
Luke W. Miratrix
Keyword(s):  
Variance Estimation ◽  
Control Unit ◽  
Potential Outcomes ◽  
Matched Pairs ◽  
Randomized Experiments ◽  
Multiple Treatment ◽  
Variance Estimators ◽  
Control Units ◽  
Average Impact ◽  
And Control

Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different estimators for the standard errors of the estimated average impact, but they are also built on different sets of assumptions. Neither literature handles cases with blocks of varying size that contain singleton treatment or control units, a case which can occur in a variety of contexts, such as with different forms of matching or poststratification. In this article, we reconcile the literatures by carefully examining the performance of variance estimators under several different frameworks. We then use these insights to derive novel variance estimators for experiments containing blocks of different sizes.

A Stimulation-Retreat Special Care Unit for Elders With Dementing Illness

1998 ◽  
Vol 10 (4) ◽  
pp. 379-395 ◽  
Author(s):  
M. Powell Lawton ◽  
Kimberly Van Haitsma ◽  
Jennifer Klapper ◽  
Morton H. Kleban ◽  
Ira R. Katz ◽  
...  
Keyword(s):  
Control Unit ◽  
Special Care ◽  
Special Care Unit ◽  
Negative Behaviors ◽  
Positive Behaviors ◽  
Control Units ◽  
Dementing Illness ◽  
And Control ◽  
Negative Attributes ◽  
Additional Stimulation

Two equivalent special care nursing home units for elders with dementing illness were randomly designated as experimental and control units for an intervention called the “stimulation-retreat” model. This model introduced a set of staffing and program changes whose purpose was to diagnose, prescribe, and apply a package of care according to individual needs for additional stimulation or relief from stimulation (“retreat”). A total of 49 experimental and 48 control unit residents completed 12 months of care and were evaluated at baseline, 6 months, and 12 months. It was hypothesized that the intervention would not affect the basic disability (cognitive and activities of daily living functions), would improve negative behaviors and observed affects, and would have maximum impact in increasing positive behaviors and affects. Over time, most functions worsened, including negative attributes and affects. Lesser decline in positive affect and increases in external engagement, however, led to the conclusion that the intervention showed a marginally significant and selective effect on positive behaviors and affect.

scholarly journals Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds

2018 ◽  
Vol 43 (5) ◽  
pp. 540-567 ◽  
Author(s):  
Jiannan Lu ◽  
Peng Ding ◽  
Tirthankar Dasgupta
Keyword(s):  
Causal Effect ◽  
Real Life ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Randomized Experiments ◽  
Sharp Bounds ◽  
Average Causal Effect ◽  
Behavioral Studies ◽  
And Control ◽  
Potential Outcomes Framework

Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the parameter of interest, is difficult to interpret for ordinal outcomes. To address this challenge, we propose to use two causal parameters, which are defined as the probabilities that the treatment is beneficial and strictly beneficial for the experimental units. However, although well-defined for any outcomes and of particular interest for ordinal outcomes, the two aforementioned parameters depend on the association between the potential outcomes and are therefore not identifiable from the observed data without additional assumptions. Echoing recent advances in the econometrics and biostatistics literature, we present the sharp bounds of the aforementioned causal parameters for ordinal outcomes, under fixed marginal distributions of the potential outcomes. Because the causal estimands and their corresponding sharp bounds are based on the potential outcomes themselves, the proposed framework can be flexibly incorporated into any chosen models of the potential outcomes and is directly applicable to randomized experiments, unconfounded observational studies, and randomized experiments with noncompliance. We illustrate our methodology via numerical examples and three real-life applications related to educational and behavioral research.

scholarly journals Do early trends in oak barrens fire treatment predict later outcomes? Insights from three decades of vegetation monitoring

Fire Ecology ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
John B. Taft
Keyword(s):  
Species Diversity ◽  
Fire Management ◽  
Basal Area ◽  
Control Unit ◽  
Species Density ◽  
Ground Layer ◽  
Treatment Unit ◽  
Reliable Predictor ◽  
Control Units ◽  
And Control

Abstract Background Vegetation structure, species diversity, and composition have been monitored at a fire-treatment and a fire-free control unit of a dry oak barrens and woodland complex in southern Illinois, USA, over a 29-year period and five burns. The restoration hypothesis is that fire management would result in different trends for vegetation parameters of conservation interest between fire treatment and control units, that before–after differences would be greater with fire management, and that early trends provide a reliable predictor for later outcomes. This study examines effectiveness of management in achieving restoration goals and uses response to the first two burns and monitoring results over the first seven years as an estimation period to test whether early trends with fire treatment are a reliable predictor for outcomes following three additional burns over the following 22 years. Results Trends differed between fire-treatment and control units for all parameters measured, and before–after differences were greater at the fire treatment unit. However, trends at the fire-treatment unit during the estimation period were a poor predictor of later outcomes. Tree density and basal area declined more than expected while ground-layer species density, richness, diversity, and percent cover did not keep pace with expectations of increase. Trends at the control unit were more predictable; however, tree basal area declined more than expected, possibly due to an outbreak of rapid white oak mortality disease, and decline of ground-layer species density was less than predicted from the early estimation period. Conclusions Results suggested that fire alone can be effective at restoring woodland and barrens natural areas and that a fire return interval of about every four years would be optimal for maintaining composition and diversity in this specific oak barrens habitat. However, burns followed immediately by severe drought possibly can have negative interactions, resulting in declines of ground-layer species diversity.

scholarly journals Removal of Pathogens from Domestic Wastewater Using Small-Scale Gradual Hydroponics Planted with Duranta erecta, Addis Ababa, Ethiopia

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Solomon Tibebu ◽  
Abebe Worku ◽  
Kenatu Angassa
Keyword(s):  
Heterotrophic Bacteria ◽  
Addis Ababa ◽  
Control Unit ◽  
Domestic Wastewater ◽  
Experimental Unit ◽  
Small Scale ◽  
Hydroponic System ◽  
Control Units ◽  
Significant Difference ◽  
And Control

This study aimed to evaluate the treatment potential of gradual hydroponics planted with Duranta erecta in the removal of pathogens from domestic wastewater. Two experimental and control units were configured in series. Each unit contains three bioreactors and was arranged in a cascaded configuration. The two experimental units used both plant and media, but the two control units used only media to treat the wastewater. Gravel and polyester sponge were used as media. Experimental unit 1 and control unit 1 used gravel as media; however, experimental unit 2 and control unit 2 used polyester sponges as media. The experiment was operated at hydraulic retention times of 1, 3, 5, and 7 days in a continuous mode. The performance of the hydroponic system was evaluated by characterizing the influent and effluent quality using standard methods. At optimum hydraulic retention time (7 days), the average removal of experimental units 1 and 2 was 98.7% and 89.8% for heterotrophic bacteria, 96.2% and 86.8% for total coliform, and 92.9% and 84.0% for fecal coliform, respectively. Analysis of variance showed that there was a significant difference P < 0.05 between the two experimental and control units in removing pathogens, but no significant difference P > 0.05 was observed between the two experimental units and between the two control units. Heterotrophic bacteria and coliforms were satisfactorily removed from domestic wastewater via a gradual hydroponic system. Hence, the hydroponic treatment system planted with Duranta erecta has a promising potential in the removal of pathogens from domestic wastewater in developing countries including Ethiopia.

A Study on Fault Detection Algorithm for Motor Control Unit of Electric Vehicle

2013 ◽  
Vol 431 ◽  
pp. 226-230
Author(s):  
Dong Hyun Seo ◽  
Wae Gyeong Shin ◽  
Jong Sang No
Keyword(s):  
Motor Control ◽  
Fault Detection ◽  
Real Time ◽  
Electric Motor ◽  
Control Unit ◽  
Detection Algorithm ◽  
Control Units ◽  
Safety And Reliability ◽  
Different Characteristics ◽  
And Control

Algorithms for motor control unit in electric vehicles are being actively developed with consideration given to safety and reliability these days. Faults during driving are a critical problem that is directly linked to the safety of drivers, and studies on fault detection of control units in various situations are needed. This study investigated the faults of control units in a signal level interface with a dynamic model of drive motor and the real-time interconnection of motor control unit and HILS (hardware-in-the-loop simulation). It was found through real-time simulation that simulating the fault conditions with the sensors of motor control unit could reveal different characteristics of motor control unit. Furthermore, vehicle driving simulations with electric motor control were performed. The results of this study are expected to help the development of electric motor simulations and the evaluation of MCU and control algorithms.

Design-Based Covariate Adjustments in Paired Experiments

2020 ◽  
pp. 107699862094146
Author(s):  
Edward Wu ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Random Forests ◽  
Potential Outcomes ◽  
Prediction Algorithm ◽  
Randomized Experiments ◽  
Control Groups ◽  
Trade Off ◽  
And Control

In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. The resulting treatment and control groups should be well-balanced; however, there may still be small chance imbalances. Building on work for completely randomized experiments, we propose a design-based method to adjust for covariate imbalances in paired experiments. We leave out each pair and impute its potential outcomes using any prediction algorithm such as lasso or random forests. This method addresses a unique trade-off that exists for paired experiments. By addressing this trade-off, the method has the potential to improve precision over existing methods.

scholarly journals Regression-adjusted average treatment effect estimates in stratified randomized experiments

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 935-948
Author(s):  
Hanzhong Liu ◽  
Yuehan Yang
Keyword(s):  
Treatment Effect ◽  
Asymptotic Variance ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Asymptotically Normal ◽  
Variance Estimators ◽  
Average Treatment ◽  
Effect Estimation ◽  
Regression Adjustment ◽  
And Control

Summary Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem for a stratified experiment, and we prove that both the stratified difference-in-means estimator and the regression-adjusted average treatment effect estimator are consistent and asymptotically normal; the asymptotic variance of the latter is no greater and typically less than that of the former. We also provide conservative variance estimators that can be used to construct large-sample confidence intervals for the average treatment effect.

scholarly journals The LOOP Estimator: Adjusting for Covariates in Randomized Experiments

2018 ◽  
Vol 42 (4) ◽  
pp. 458-488 ◽  
Author(s):  
Edward Wu ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Variable Selection ◽  
Controlled Trial ◽  
Potential Outcomes ◽  
Prediction Algorithm ◽  
Randomized Experiments ◽  
Randomized Controlled ◽  
Statistical Assumptions ◽  
Leave One Out ◽  
And Control ◽  
Baseline Covariates

Background: When conducting a randomized controlled trial, it is common to specify in advance the statistical analyses that will be used to analyze the data. Typically, these analyses will involve adjusting for small imbalances in baseline covariates. However, this poses a dilemma, as adjusting for too many covariates can hurt precision more than it helps, and it is often unclear which covariates are predictive of outcome prior to conducting the experiment. Objectives: This article aims to produce a covariate adjustment method that allows for automatic variable selection, so that practitioners need not commit to any specific set of covariates prior to seeing the data. Results: In this article, we propose the “leave-one-out potential outcomes” estimator. We leave out each observation and then impute that observation’s treatment and control potential outcomes using a prediction algorithm such as a random forest. In addition to allowing for automatic variable selection, this estimator is unbiased under the Neyman–Rubin model, generally performs at least as well as the unadjusted estimator, and the experimental randomization largely justifies the statistical assumptions made.

scholarly journals Bridging Finite and Super Population Causal Inference

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Peng Ding ◽  
Xinran Li ◽  
Luke W. Miratrix
Keyword(s):  
Causal Inference ◽  
Finite Population ◽  
Causal Effect ◽  
Causal Analysis ◽  
Potential Outcomes ◽  
Alternative Formulation ◽  
Randomized Experiments ◽  
Infinite Population ◽  
Variance Estimators ◽  
Average Causal Effect

AbstractThere are two general views in causal analysis of experimental data: the super population view that the units are an independent sample from some hypothetical infinite population, and the finite population view that the potential outcomes of the experimental units are fixed and the randomness comes solely from the treatment assignment. These two views differs conceptually and mathematically, resulting in different sampling variances of the usual difference-in-means estimator of the average causal effect. Practically, however, these two views result in identical variance estimators. By recalling a variance decomposition and exploiting a completeness-type argument, we establish a connection between these two views in completely randomized experiments. This alternative formulation could serve as a template for bridging finite and super population causal inference in other scenarios.

Controlled Trial Measuring the Effect of a Feedback Intervention on Hand Hygiene Compliance in a Step-Down Unit

2008 ◽  
Vol 29 (8) ◽  
pp. 730-735 ◽  
Author(s):  
Alexandre R. Marra ◽  
Cláudia D'Arco ◽  
Bruno de Arruda Bravim ◽  
Marinês Dalla Valle Martino ◽  
Luci Correa ◽  
...  
Keyword(s):  
Hand Hygiene ◽  
Healthcare Workers ◽  
Hand Hygiene Compliance ◽  
Controlled Trial ◽  
Control Unit ◽  
Feedback Intervention ◽  
Control Units ◽  
Hygiene Compliance ◽  
And Control ◽  
Intervention Unit

Objective.To evaluate hand hygiene compliance in 2 adult step-down units (SDUs).Design.A 6-month (from March to September 2007), controlled trial comparing 2 SDUs, one with a feedback intervention program (ie, the intervention unit) and one without (ie, the control unit).Setting.Two 20-bed SDUs at a tertiary care private hospital.Methods.Hand hygiene episodes were measured by electronic recording devices and periodic observational surveys. In the intervention unit, feedback was provided by the SDU nurse manager, who explained twice a week to the healthcare workers the goals and targets for the process measures.Results.A total of 117,579 hand hygiene episodes were recorded in the intervention unit, and a total of 110,718 were recorded in the control unit (P= .63). There was no significant difference in the amount of chlorhexidine used in the intervention and control units (34.0 vs 26.7 L per 1,000 patient-days;P= .36) or the amount of alcohol gel used (72.5 vs 70.7 L per 1,000 patient-days;P= .93). However, in both units, healthcare workers used alcohol gel more frequently than chlorhexidine (143.2 vs 60.7 L per 1,000 patient-days;P< .001). Nosocomial infection rates in the intervention and control units, respectively, were as follows: for bloodstream infection, 3.5 and 0.79 infections per 1,000 catheter-days (P= .18); for urinary tract infection, 15.8 and 15.7 infections per 1,000 catheter-days (P= .99); and for tracheostomy-associated pneumonia, 10.7 and 5.1 infections per 1,000 device-days (P= . 13). There were no cases of infection with vancomycin-resistant enterococci and only a single case of infection with methicillin-resistantStaphylococcus aureus(in the control unit).Conclusions.The feedback intervention regarding hand hygiene had no significant effect on the rate of compliance. Other measures must be used to increase and sustain the rate of hand hygiene compliance.

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