Random Measurement Error Does Not Bias the Treatment Effect Estimate in the Regression-Discontinuity Design

1991 ◽  
Vol 15 (4) ◽  
pp. 395-419 ◽  
Author(s):  
Joseph C. Cappelleri ◽  
William M.K. Trochim ◽  
T.D. Stanley ◽  
Charles S. Reichardt
Keyword(s):  
Measurement Error ◽  
Treatment Effect ◽  
Regression Discontinuity ◽  
Effect Estimate ◽  
Regression Discontinuity Design ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Treatment Effect Estimate

scholarly journals Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Nicole Bohme Carnegie ◽  
Rui Wang ◽  
Victor De Gruttola
Keyword(s):  
Infectious Disease ◽  
Disease Prevention ◽  
Social Interactions ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Effect Estimate ◽  
Treatment Effect Estimate ◽  
Partial Interference ◽  
The Difference ◽  
Cluster Randomized

AbstractAn issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of “partial interference” – interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the no-interference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none – referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference – contacts sufficient for transmission that are with treated clusters – is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

scholarly journals Validation of a new signal processing scheme for the MST radar at Aberystwyth

2008 ◽  
Vol 26 (11) ◽  
pp. 3253-3268 ◽  
Author(s):  
D. A. Hooper ◽  
J. Nash ◽  
T. Oakley ◽  
M. Turp
Keyword(s):  
Signal Processing ◽  
Measurement Error ◽  
Gravity Wave ◽  
Wave Activity ◽  
Horizontal Wind ◽  
Time Averaging ◽  
Random Measurement Error ◽  
Processing Scheme ◽  
Random Measurement ◽  
Short Period

Abstract. This paper describes a new signal processing scheme for the 46.5 MHz Doppler Beam Swinging wind-profiling radar at Aberystwyth, in the UK. Although the techniques used are similar to those already described in literature – i.e. the identification of multiple signal components within each spectrum and the use of radial- and time-continuity algorithms for quality-control purposes – it is shown that they must be adapted for the specific meteorological environment above Aberystwyth. In particular they need to take into account the three primary causes of unwanted signals: ground clutter, interference, and Rayleigh scatter from hydrometeors under stratiform precipitation conditions. Attention is also paid to the fact that short-period gravity-wave activity can lead to an invalidation of the fundamental assumption of the wind field remaining stationary over the temporal and spatial scales encompassed by a cycle of observation. Methods of identifying and accounting for such conditions are described. The random measurement error associated with horizontal wind components is estimated to be 3.0–4.0 m s−1 for single cycle data. This reduces to 2.0–3.0 m s−1 for data averaged over 30 min. The random measurement error associated with vertical wind components is estimated to be 0.2–0.3 m s−1. This cannot be reduced by time-averaging as significant natural variability is expected over intervals of just a few minutes under conditions of short-period gravity-wave activity.

Effects of Random Measurement Error on Lung Cancer Screening Decisions

CHEST Journal ◽  
2020 ◽  
Author(s):  
Tanner J. Caverly ◽  
Xuefei Zhang ◽  
Rodney A. Hayward ◽  
Ji Zhu ◽  
Akbar K. Waljee
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Measurement Error ◽  
Lung Cancer Screening ◽  
Random Measurement Error ◽  
Random Measurement

scholarly journals Impact of informative censoring on the treatment effect estimate of disability worsening in multiple sclerosis clinical trials

2020 ◽  
Vol 39 ◽  
pp. 101865
Author(s):  
Katherine Riester ◽  
Ludwig Kappos ◽  
Krzysztof Selmaj ◽  
Stacy Lindborg ◽  
Ilya Lipkovich ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Treatment Effect ◽  
Informative Censoring ◽  
Effect Estimate ◽  
Treatment Effect Estimate

The Mechanics of Treatment-effect Estimate Bias for Nonexperimental Data

2019 ◽  
pp. 004912411985237
Author(s):  
Roberto V. Penaloza ◽  
Mark Berends
Keyword(s):  
Treatment Effect ◽  
Latent Variable ◽  
Value Added ◽  
Effect Estimate ◽  
Education Production Function ◽  
Education Production ◽  
Group Assignment ◽  
Treatment Effect Estimate ◽  
Flexible Form ◽  
Variable Errors

To measure “treatment” effects, social science researchers typically rely on nonexperimental data. In education, school and teacher effects on students are often measured through value-added models (VAMs) that are not fully understood. We propose a framework that relates to the education production function in its most flexible form and connects with the basic VAMs without using untenable assumptions. We illustrate how, due to measurement error (ME), cross-group imbalances created by nonrandom group assignment cause correlations that drive the models’ treatment-effect estimate bias. We derive formulas to calculate bias and rank the models and show that no model is better in all situations. The framework and formulas’ workings are verified and illustrated via simulation. We also evaluate the performance of latent variable/errors-in-variables models that handle ME and study the role of extra covariates including lags of the outcome.

scholarly journals ON NONPARAMETRIC INFERENCE IN THE REGRESSION DISCONTINUITY DESIGN

2017 ◽  
Vol 34 (3) ◽  
pp. 694-703 ◽  
Author(s):  
Vishal Kamat
Keyword(s):  
Treatment Effect ◽  
Null Hypothesis ◽  
Regression Discontinuity ◽  
Nonparametric Tests ◽  
Average Treatment Effect ◽  
Nonparametric Inference ◽  
Regression Discontinuity Design ◽  
Average Treatment ◽  
Size Distortions

This paper studies the validity of nonparametric tests used in the regression discontinuity design. The null hypothesis of interest is that the average treatment effect at the threshold in the so-called sharp design equals a pre-specified value. We first show that, under assumptions used in the majority of the literature, for any test the power against any alternative is bounded above by its size. This result implies that, under these assumptions, any test with nontrivial power will exhibit size distortions. We next provide a sufficient strengthening of the standard assumptions under which we show that a version of a test suggested in Calonico, Cattaneo, and Titiunik (2014) can control limiting size.

Influence of Random Measurement Error on Estimated Rates of Headache Chronification and Remission

2013 ◽  
Vol 53 (6) ◽  
pp. 920-929 ◽  
Author(s):  
Timothy T. Houle ◽  
Dana P. Turner ◽  
Todd A. Smitherman ◽  
Donald B. Penzien ◽  
Richard B. Lipton
Keyword(s):  
Measurement Error ◽  
Random Measurement Error ◽  
Random Measurement
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