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1. Full Text Commentary: Interpretation and Sensitivity Analysis for the Localized Average Causal Effect Curve
by Small, Dylan S
Epidemiology, ISSN 1044-3983, 11/2014, Volume 25, Issue 6, pp. 886 - 888
Mendelian Randomization Analysis - methods | Epidemiologic Methods | Humans
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2. Full Text Correction
by Baiocchi, Michael and Cheng, Jing and Small, Dylan S
Statistics in Medicine, ISSN 0277-6715, 09/2019, Volume 38, Issue 20, p. 3960
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3. Full Text Correction
by Baiocchi, Michael and Cheng, Jing and Small, Dylan S
Statistics in Medicine, ISSN 0277-6715, 09/2019, Volume 38, Issue 20, pp. 3960 - 3960
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4. Full Text Correction
by Baiocchi, Michael and Cheng, Jing and Small, Dylan S
Statistics in Medicine, ISSN 0277-6715, 09/2019, Volume 38, Issue 20, pp. 3960 - 3960
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5. Full Text Instrumental variable methods for causal inference
by Baiocchi, Michael and Cheng, Jing and Small, Dylan S
Statistics in Medicine, ISSN 0277-6715, 06/2014, Volume 33, Issue 13, pp. 2297 - 2340
A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically... 
comparative effectiveness | confounding | instrumental variables | observational study | Confounding | Comparative effectiveness | Observational study | Instrumental variables | UNITED-STATES | MEDICINE, RESEARCH & EXPERIMENTAL | RESPONSE MODELS | PROPENSITY SCORE | MEDICAL INFORMATICS | PREMATURE-INFANTS | BIRTH-WEIGHT | STATISTICS & PROBABILITY | MENDELIAN RANDOMIZATION | RANDOMIZED CLINICAL-TRIALS | NONCOMPLIANCE | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | IMPERFECT COMPLIANCE | TO-TREAT | MATHEMATICAL & COMPUTATIONAL BIOLOGY | Data Interpretation, Statistical | Confounding Factors (Epidemiology) | Outcome Assessment (Health Care) - statistics & numerical data | Humans | Observational Studies as Topic - statistics & numerical data | Causality | Infant, Newborn
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6. Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies Through Quadratically Constrained Linear Programming
by Fogarty, Colin B and Small, Dylan S
Journal of the American Statistical Association, ISSN 0162-1459, 12/2016, Volume 111, Issue 516, pp. 1820 - 1830
A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in... 
Studies | Sensitivity analysis
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7. Full Text Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization
by Zhao, Qingyuan and Chen, Yang and Wang, Jingshu and Small, Dylan S
International journal of epidemiology, ISSN 0300-5771, 07/2019
Summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS... 
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8. Full Text An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies
: Adaptive Mantel-Haenszel Test
by Rosenbaum, Paul R and Small, Dylan S
Biometrics, ISSN 0006-341X, 06/2017, Volume 73, Issue 2, pp. 422 - 430
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9. Full Text Empirically testable sources and implications of clumpiness
by Zhang, Yao and Small, Dylan S
Marketing Science, ISSN 0732-2399, 2015, Volume 34, Issue 2, pp. 209 - 217
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10. Full Text Evaluating Missouri’s Handgun Purchaser Law: A Bracketing Method for Addressing Concerns About History Interacting with Group
by Hasegawa, Raiden B and Webster, Daniel W and Small, Dylan S
Epidemiology, ISSN 1044-3983, 05/2019, Volume 30, Issue 3, pp. 371 - 379
In the comparative interrupted time series design (also called the method of difference-in-differences), the change in outcome in a group exposed to treatment... 
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11. Full Text The use of bootstrapping when using propensity‐score matching without replacement: a simulation study
by Austin, Peter C and Small, Dylan S
Statistics in Medicine, ISSN 0277-6715, 10/2014, Volume 33, Issue 24, pp. 4306 - 4319
Propensity‐score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important... 
propensity‐score matching | Monte Carlo simulations | bootstrap | variance estimation | propensity score | matching | Matching | Propensity-score matching | Bootstrap | Propensity score | Variance estimation | MEDICINE, RESEARCH & EXPERIMENTAL | MEDICAL INFORMATICS | PERFORMANCE | STATISTICS & PROBABILITY | RISK DIFFERENCES | propensity-score matching | PROPORTIONS | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | MODELS | MATHEMATICAL & COMPUTATIONAL BIOLOGY | Propensity Score | Confidence Intervals | Data Interpretation, Statistical | Observational Studies as Topic - methods | Computer Simulation | Humans | Monte Carlo Method | Monte Carlo method
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12. Full Text Fund US veteran toxic exposure database
by Small, DS
SCIENCE, ISSN 0036-8075, 05/2019, Volume 364, Issue 6441, pp. 642 - 642
AGENT-ORANGE | MULTIDISCIPLINARY SCIENCES | Occupational health and safety | Agent Orange-related diseases | Laws, regulations and rules | Veterans | Health aspects
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13. Testing endogeneity with high dimensional covariates
by Zijian Guo and Hyunseung Kang and T Tony Cai and Dylan S Small
Journal of Econometrics, ISSN 0304-4076, 11/2018, Volume 207, Issue 1, p. 175
Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primarily focusing on estimation of effects of endogenous... 
Variables | Simulation | Data models | Regression analysis | Econometrics
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14. Full Text Multiple Testing When Many p-Values are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions
by Zhao, Qingyuan and Small, Dylan S and Su, Weijie
Journal of the American Statistical Association, ISSN 0162-1459, 07/2019, Volume 114, Issue 527, pp. 1291 - 1304
In the evaluation of treatment effects, it is of major policy interest to know if the treatment is beneficial for some and harmful for others, a phenomenon... 
Uniform conditional stochastic order | Global null | Multisite study | Treatment effect heterogeneity | Selective inference | Meta-analysis | Education | Materials information | Test procedures
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15. Full Text A review of instrumental variable estimators for Mendelian randomization
by Burgess, Stephen and Small, Dylan S and Thompson, Simon G
Statistical Methods in Medical Research, ISSN 0962-2802, 10/2017, Volume 26, Issue 5, pp. 2333 - 2355
Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data.... 
Instrumental variable | Mendelian randomization | weak instruments | comparison of methods | finite-sample bias | causal inference | MEDICAL INFORMATICS | LOGISTIC-REGRESSION | BINARY OUTCOMES | STATISTICS & PROBABILITY | OBSERVATIONAL EPIDEMIOLOGY | CAUSAL ODDS RATIOS | GENERALIZED-METHOD | SIMULTANEOUS-EQUATIONS | MULTIPLE GENETIC-VARIANTS | SAMPLE PROPERTIES | SENSITIVITY-ANALYSIS | HEALTH CARE SCIENCES & SERVICES | MATHEMATICAL & COMPUTATIONAL BIOLOGY | Genetic Variation | Mendelian Randomization Analysis - methods | Confidence Intervals | Likelihood Functions | Humans | Risk Factors | Bayes Theorem | Least-Squares Analysis | Models, Statistical | Causality | Case-Control Studies | Statistical analysis | Bias | Popularity | Exposure | Statistical methods | Coverage | Genetic diversity | Confidence intervals | Variants | Randomization | Estimating techniques | Instrumental variable estimation | Estimators | Regular
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16. Full Text Interpretation and sensitivity analysis for the localized average causal effect curve
by Small, Dylan S
Epidemiology, ISSN 1044-3983, 2014, Volume 25, Issue 6, pp. 886 - 888
INFERENCE | PRINCIPAL STRATIFICATION | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
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17. Full Text Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error
by Lee, Kwonsang and Small, Dylan S
Journal of the American Statistical Association, ISSN 0162-1459, 01/2019, Volume 114, Issue 525, pp. 79 - 92
Malaria is a major health problem in many tropical regions. Fever is a characteristic symptom of malaria. The fraction of fevers that are attributable to... 
Causal inference | Bayes deconvolution problem | Exponential family g-modeling | Sufficient cause model | Two-component mixture model | PLASMODIUM-FALCIPARUM | GROWTH | STATISTICS & PROBABILITY | MODEL | Maximum likelihood estimation | Economic models | Sensitivity analysis | Error analysis | Malaria | Computer simulation | Tropical environments | Estimating techniques | Parasites | Density | Public health | Fever
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18. Full Text Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions
by Small, Dylan S
Journal of the American Statistical Association, ISSN 0162-1459, 09/2007, Volume 102, Issue 479, pp. 1049 - 1058
Instrumental variables regression (IV regression) is a method for making causal inferences about the effect of a treatment based on an observational study in... 
Causal inference | Econometrics | Structural equations models | Sensitivity analysis | Theory and Methods | Confounding variables | Inference | Instrumental variables | Regression analysis | Food demand | Observational studies | Estimators | Consistent estimators | Income elasticity of demand | econometrics | structural equations models | MODELS | LEAST-SQUARES ESTIMATION | STATISTICS & PROBABILITY | causal inference | IDENTIFICATION | GENERALIZED-METHOD | MOMENTS | Usage | Econometric models | Instrumental variables (Statistics) | Analysis
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19. Constructed Second Control Groups and Attenuation of Unmeasured Biases
by Pimentel, Samuel D and Small, Dylan S and Rosenbaum, Paul R
Journal of the American Statistical Association, ISSN 0162-1459, 09/2016, Volume 111, Issue 515, pp. 1157 - 1157
The informal folklore of observational studies claims that if an irrelevant observed covariate is left uncontrolled, say unmatched, then it will influence... 
Estimation bias | Sensitivity analysis | Algebraic group theory | Risk factors
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20. Constructed Second Control Groups and Attenuation of Unmeasured Biases
by Samuel D Pimentel and Dylan S Small and Paul R Rosenbaum
Journal of the American Statistical Association, ISSN 0162-1459, 09/2016, Volume 111, Issue 515, p. 1157
  The informal folklore of observational studies claims that if an irrelevant observed covariate is left uncontrolled, say unmatched, then it will influence... 
Estimation bias | Sensitivity analysis | Algebraic group theory | Variance analysis | Risk factors
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