Assumptions of IV Methods for Observational Epidemiology.

Cited by (213)

Items 101–200 of 213

1. Lack of Identification in Semiparametric Instrumental Variable Models With Binary Outcomes
   Stephen Burgess, Raquel Granell, Tom Palmer, Jonathan A C Sterne, Vanessa Didelez · 2014 · American Journal of Epidemiology
2. A Note on the Control Function Approach with an Instrumental Variable and a Binary Outcome
   Eric Tchetgen Tchetgen · 2014 · Epidemiologic Methods
3. Assessing causal treatment effect estimation when using large observational datasets
   Ellie John, Keith R. Abrams, Christopher Edward Brightling, Nuala A. Sheehan · 2019 · BMC Medical Research Methodology
4. Serum total bilirubin levels and coronary heart disease — Causal association or epiphenomenon?
   Setor Kwadzo Kunutsor · 2015 · Experimental Gerontology
5. Associations of mortality with own height using son's height as an instrumental variable
   David Carslake, Abigail Fraser, George Davey Smith, Margaret May, Tom Palmer, Jonathan A C Sterne, Karri Silventoinen, Per Tynelius, Debbie A. Lawlor, Finn E Rasmussen · 2012 · Economics & Human Biology
6. Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study
   Charlie N. Saunders, Alex J. Cornish, Ben Kinnersley, Philip Law, Richard S. Houlston, Collaborators, Elizabeth B. Claus, Dora Il’yasova, Joellen M. Schildkraut, Jill S. Barnholtz‐Sloan et al. · 2020 · British Journal of Cancer
7. Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids
   So–Youn Shin, Ann-Kristin Petersen, Simone Wahl, Guangju Zhai, Werner Römisch‐Margl, Kerrin S. Small, Angela Döring, Bernet Sekasanvu Kato, Annette Peters, Elin Grundberg et al. · 2014 · Genome Medicine
8. Iterative regularisation in nonparametric instrumental regression
   Jan Johannes, Sébastien Van Bellegem, Anne Vanhems · 2012 · Journal of Statistical Planning and Inference
9. Quantitative Falsification of Instrumental Variables Assumption Using Balance Measures
   Mohammed Sanni Ali, Md. Jamal Uddin, Rolf H. H. Groenwold, Wiebe R. Pestman, Svetlana V. Belitser, Arno Wilhelmus Hoes, Anthonius de Boer, Kit C. B. Roes, Olaf H. Klungel · 2014 · Epidemiology
10. Methods for meta-analysis of individual participant data from Mendelian randomisation studies with binary outcomes
    Stephen Burgess, Simon G. Thompson · 2012 · Statistical Methods in Medical Research
11. Commentary
    Sonja A. Swanson · 2016 · Epidemiology
12. Approaches to the Estimation of the Local Average Treatment Effect in a Regression Discontinuity Design
    Aidan G. O’Keeffe, Gianluca Baio · 2016 · Scandinavian Journal of Statistics
13. Predicting the Direction of Causal Effect Based on an Instrumental Variable Analysis: A Cautionary Tale
    Stephen Burgess, Dylan S. Small · 2016 · Journal of Causal Inference
14. On the choice of parameterisation and priors for the Bayesian analyses of Mendelian randomisation studies
    Elinor M Jones, J. R. Thompson, Vanessa Didelez, Nuala A. Sheehan · 2012 · Statistics in Medicine
15. Exploring causal correlations between inflammatory cytokines and varicose veins: A Mendelian randomization analysis
    Shen Min, Mengzhen Xing, Hehe Jiang, Linlin Zhang, Chen Chen, Yuning Ma, Yuxia Ma · 2024 · International Wound Journal
16. Causal Genetic Inference Using Haplotypes as Instrumental Variables
    Fan Wang, Nuala J. Meyer, Keith R. Walley, James A. Russell, Rui Feng · 2015 · Genetic Epidemiology
17. Being overweight in early adulthood is associated with increased mortality in middle age
    David Carslake, Mona Jeffreys, George Davey Smith · 2016 · Scientific Reports
18. Estimation of local treatment effects under the binary instrumental variable model
    Linbo Wang, Yuexia Zhang, Thomas S. Richardson, James M. Robins · 2021 · Biometrika
19. The causal roles of vitamin B(12) and transcobalamin in prostate cancer: can Mendelian randomization analysis provide definitive answers?
    Simon M. Collin, Chris D. Metcalfe, Tom Palmer, Helga Refsum, Sarah Jane Lewis, George Davey Smith, Angela Cox, Michael Davis, Gemma L. Marsden, Carole Johnston et al. · 2011 · PubMed Central
20. A Bayesian approach for instrumental variable analysis with censored time‐to‐event outcome
    Gang Li, Xuyang Lu · 2014 · Statistics in Medicine
21. Methods for Estimating Complier Average Causal Effects for Cost-Effectiveness Analysis
    Karla Diaz‐Ordaz, A Franchini, Richard Grieve · 2017 · Journal of the Royal Statistical Society Series A (Statistics in Society)
22. Causal graphs for the analysis of genetic cohort data
    Oliver Hines, Karla Diaz‐Ordaz, Stijn Vansteelandt, Yalda Jamshidi · 2020 · Physiological Genomics
23. The causal effect of air pollution on the risk of essential hypertension: a Mendelian randomization study
    Zhiwei Xia, Yinjiang Liu, Chao Liu, Ziyu Dai, Xisong Liang, Nan Zhang, Wantao Wu, Jie Wen, Hao Zhang · 2024 · Frontiers in Public Health
24. A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA)
    Kenneth A. Bollen, Kathleen M. Gates, Lan Luo · 2024 · Psychometrika
25. Lack of an association between gallstone disease and bilirubin levels with risk of colorectal cancer: a Mendelian randomisation analysis
    Richard Culliford, Alex J. Cornish, Philip Law, Susan Mary Farrington, Kimmo Palin, Mark A. Jenkins, Graham Casey, Michael Hoffmeister, Hermann Brenner, Jenny C. Chang-Claude et al. · 2021 · British Journal of Cancer
26. Mendelian Randomization Studies for a Continuous Exposure Under Case-Control Sampling
    James Y. Dai, Xuanqi Zhang · 2015 · American Journal of Epidemiology
27. Distributional robustness of K-class estimators and the PULSE
    Martin Emil Jakobsen, Jonas Peters · 2021 · Econometrics Journal
28. Mendelsk randomisering – genetisk tilnærming til epidemiologisk metode
    Mats Julius Stensrud · 2016 · Tidsskrift for Den norske legeforening
29. Estimating the influence of body mass index (BMI) on mortality using offspring BMI as an instrumental variable
    Elina Hyppönen, David Carslake, Diane J. Berry, Chris Power, George Davey Smith · 2021 · International Journal of Obesity
30. Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization
    James Y. Dai, Kwun Chuen Gary Chan, Li Hsu · 2014 · Statistics in Medicine
31. Commentary: Can 'many weak' instruments ever be 'strong'?
    Nuala A. Sheehan, Vanessa Didelez · 2011 · International Journal of Epidemiology
32. Unveiling challenges in Mendelian randomization for gene–environment interaction
    Malka Gorfine, Conghui Qu, Ulrike Peters, Li Hsu · 2024 · Genetic Epidemiology
33. Nonparametric instrumental regression with non-convex constraints
    Markus Grasmair, Otmar Scherzer, Anne Vanhems · 2013 · Inverse Problems
34. Regression Discontinuity Designs for Time-to-Event Outcomes: An Approach using Accelerated Failure Time Models
    Mariam Adeleke, Gianluca Baio, Aidan G. O’Keeffe · 2022 · Journal of the Royal Statistical Society Series A (Statistics in Society)
35. Bayesian Modelling for Binary Outcomes in the Regression Discontinuity Design
    Sara Geneletti, Federico Ricciardi, Aidan G. O’Keeffe, Gianluca Baio · 2019 · Journal of the Royal Statistical Society Series A (Statistics in Society)
36. Determinantal Generalizations of Instrumental Variables
    Luca Weihs, Bill Robinson, Emilie Dufresne, Jennifer Kenkel, Kaie Kubjas, McGee II Reginald, Nhan Nguyen, Elina Robeva, Mathias Drton · 2017 · Journal of Causal Inference
37. Understanding Causal Distributional and Subgroup Effects With the Instrumental Propensity Score
    Jing Cheng, Winston T. Lin · 2017 · American Journal of Epidemiology
38. The role of estimation in Mendelian randomization: should Mendelian randomization investigations provide estimates?
    Benjamin A. R. Woolf, Stephen Burgess · 2025 · AJE Advances Research in Epidemiology
39. Addressing Endogeneity in Meta-Analysis: Instrumental Variable Based Meta-Analytic Structural Equation Modeling
    Zijun Ke, Yucheng Eason Zhang, Zhongwei Hou, Michael James Zyphur · 2024 · Journal of Management
40. Dirichlet process mixture models for regression discontinuity designs
    Federico Ricciardi, Silvia Liverani, Gianluca Baio · 2022 · Statistical Methods in Medical Research
41. Mendelian randomization as an approach to assess causal effects of inflammatory bowel disease on atrial fibrillation
    Laite Chen, Guosheng Fu, Chenyang Jiang · 2021 · Aging
42. Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose
    Jinhao Zou, R. Talluri, Sanjay Shete · 2024 · PLoS ONE
43. Guidance for Researchers on Optimal Methods for Conducting Comparative Effectiveness Research With Observational Data
    Douglas P. Landsittel, Chung‐Chou H. Chang, Sally C. Morton · 2019
44. Investigating the causal relationship between immune cell and Alzheimer’s disease: a mendelian randomization analysis
    Min Shen, Linlin Zhang, Chen Chen, Xiaocen Wei, Yuning Ma, Yuxia Ma · 2024 · BMC Neurology
45. Unravelling the Metabolic Underpinnings of Gestational Diabetes Mellitus: A Comprehensive Mendelian Randomisation Analysis Identifying Causal Metabolites and Biological Pathways
    Min Shen, Lei Shi, Mengzhen Xing, Hehe Jiang, Yuning Ma, Yuxia Ma, Linlin Zhang · 2024 · Diabetes/Metabolism Research and Reviews
46. Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes
    Verena Zuber, Toinét Cronjé, Na Cai, Dipender Gill, Leonardo Bottolo · 2025 · The American Journal of Human Genetics
47. Genetic Instrumental Variable Studies of Effects of Prenatal Risk Factors
    George L. Wehby, Stephanie von Hinke · 2013 · Biodemography and Social Biology
48. Impact of socioeconomic and risk factors on cardiovascular disease and type II diabetes in Australia: comparison of results from longitudinal and cross-sectional designs
    Jinjing Li, Yohannes Kinfu · 2016 · BMJ Open
49. “Crossover” in Depressive Symptoms Among Older Couples: Are Previous Findings Artifactual?
    Aniruddha Das · 2018 · Journal of Aging and Health
50. Using instrumental variables to disentangle treatment and placebo effects in blinded and unblinded randomized clinical trials influenced by unmeasured confounders
    E. Chaibub · 2016 · Scientific Reports
51. Comparison of variance estimators for meta-analysis of instrumental variable estimates
    Amand Floriaan Schmidt, Aroon Dinesh Hingorani, Barbara J.M.H. Jefferis, Jon White, Rolf H. H. Groenwold, Frank Dudbridge · 2016 · International Journal of Epidemiology
52. Genetics of Acute Respiratory Distress Syndrome
    Heather M. Giannini, Nuala J. Meyer · 2021 · Critical Care Clinics
53. Likelihood Based Estimation of Logistic Structural Nested Mean Models with an Instrumental Variable
    Roland Matsouaka, Eric J. Tchetgen Tchetgen · 2014 · Collection of Biostatistics Research Archive
54. A Comparison of Three Methods of Mendelian Randomization when the Genetic Instrument, the Risk Factor and the Outcome Are All Binary
    Philippe Vuistiner, Murielle Bochud, Valentin Rousson · 2012 · PLoS ONE
55. Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
    Baoluo Sun, Yifan Cui, Eric Tchetgen Tchetgen · 2019 · arXiv (Cornell University)
56. Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes – a comparison
    Eleanor R John, Michael J. Crowther, Vanessa Didelez, Nuala A. Sheehan · 2024 · Statistical Methods in Medical Research
57. Does Chronic Intestinal Inflammation Promote Atrial Fibrillation: A Mendelian Randomization Study With Populations of European Ancestry
    Laite Chen, Chenyang Jiang · 2021 · Frontiers in Cardiovascular Medicine
58. Mediation analysis with case–control sampling: Identification and estimation in the presence of a binary mediator
    Marco Doretti, Minna Genbäck, Elena Stanghellini · 2024 · Biometrical Journal
59. Evaluating the effects of treatment switching with randomization as an instrumental variable in a randomized controlled trial
    Sara Jiménez, Dejian Lai, K. Lance Gould, Barry R. Davis · 2016 · Communications in Statistics - Simulation and Computation
60. Adjusting for bias in unblinded randomized controlled trials
    Amand Floriaan Schmidt, Rolf H. H. Groenwold · 2016 · Statistical Methods in Medical Research
61. Causal relationship between circulating inflammatory cytokines and the risk of hernia: a bidirectional Mendelian randomization study
    Yaqin Qi, Changjiu Li, Xingyue Gao, Fangjie Zhang · 2025 · Journal of International Medical Research
62. Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
    Nikolaj Thams, Søndergaard, Rikke, Sebastian Weichwald, Jonas Peters · 2022 · Research at the University of Copenhagen (University of Copenhagen)
63. Approaches to risk ratio estimation in a regression discontinuity design: Application to the prescription of statins for cholesterol reduction in UK primary care
    Mariam Adeleke, Aidan G. O’Keeffe, Gianluca Baio · 2023 · Statistical Methods in Medical Research
64. Using instrumental variables to estimate the attributable fraction
    Elisabeth Dahlqwist, Zoltán Kutalik, Arvid Sjölander · 2019 · Statistical Methods in Medical Research
65. The presence of an error term does not preclude causal inference in regression: a comment on Krause (2012)
    Cameron Norman McIntosh · 2012 · Quality & Quantity
66. Regularization statistical inferences for partially linear models with high dimensional endogenous covariates
    Changqing Liu, Peixin Zhao, Yiping Yang · 2020 · Journal of the Korean Statistical Society
67. Improving Propensity Score Methods in Pharmacoepidemiology
    Mohammed Sanni Ali · 2014 · Utrecht University Repository (Utrecht University)
68. A Mendelian randomization study of the entire phenome to explore the causal links between epilepsy
    Wei Zhang, Liming Zhang, Lin Zhi, Ji Qi, Jue He · 2024 · Brain and Behavior
69. Exploring the causal relationship between chronic pain and temporomandibular disorders: A two-sample Mendelian randomization study
    Luyao Song, Miaomiao Zhao, Yingnan Wang · 2025 · Archives of Oral Biology
70. Associations of mortality with own blood pressure using son’s blood pressure as an instrumental variable
    David Carslake, Abigail Fraser, Margaret May, Tom Palmer, Karri Silventoinen, Per Tynelius, Debbie A. Lawlor, George Davey Smith · 2019 · Scientific Reports
71. Causal inference with two-stage logistic regression -accuracy, precision, and application
    Bing Cai · 2010 · Scholarly Commons (University of Pennsylvania)
72. Local average treatment effects estimation via substantive model compatible multiple imputation
    Karla Diaz‐Ordaz, James Robert Carpenter · 2019 · Biometrical Journal
73. Sensitivity analysis of G‐estimators to invalid instrumental variables
    Valentin Vancak, Arvid Sjölander · 2023 · Statistics in Medicine
74. Commentary: To cause or not to cause confusion vs transparency with Mendelian Randomization
    Els J. T. Goetghebeur · 2010 · International Journal of Epidemiology
75. Regression Discontinuity Designs: A Decision Theoretic Approach
    Panayiota Constantinou, Aidan G. O’Keeffe · 2016 · arXiv (Cornell University)
76. Robustness and efficiency of covariate adjusted linear instrumental variable estimators
    Stijn Vansteelandt, Vanessa Didelez · 2015 · arXiv (Cornell University)
77. Towards personalized causal inference of medication response in mobile health: an instrumental variable approach for randomized trials with imperfect compliance
    Elias Chaibub Neto, Ross L. Prentice, Brian M. Bot, Mike Kellen, Stephen Henry Friend, Andrew Daniel Trister, Larsson Omberg, Lara M. Mangravite · 2016 · arXiv (Cornell University)
78. Causal Reasoning and Inference in Epidemiology
    Vanessa Didelez · 2024 · Springer eBooks
79. Circulating adiponectin levels in systemic sclerosis: A meta-analysis and bidirectional Mendelian randomization study
    Tahzeeb Fatima, Cecilia Överdahl, Cristina Maglio · 2025 · Journal of Scleroderma and Related Disorders
80. Double penalized regularization estimation for partially linear instrumental variable models with ultrahigh dimensional instrumental variables
    Peixin Zhao, Junqi Wang, Xinrong Tang, Weiming Yang · 2021 · Communications in Statistics - Simulation and Computation
81. Integrating Mendelian randomization with causal mediation analyses for characterizing direct and indirect exposure-to-outcome effects
    Fan Yang, Lin Chen, Shahram Oveisgharan, Dawood Darbar, David A. Bennett · 2024 · The Annals of Applied Statistics
82. Estimation of the number needed to treat, the number needed to be exposed, and the exposure impact number with instrumental variables
    Valentin Vancak, Arvid Sjölander · 2024 · Epidemiologic Methods
83. Role of immune cells in seborrheic dermatitis: a two-sample bidirectional Mendelian randomization study
    Qinan Xian, Juan Zhou, Xin Li, Yiwen Xu, Yan Sun · 2025 · Archives of Dermatological Research
84. Adding covariates to bounds: what is the question?
    Gustav Jonzon, Erin Gabriel, Arvid Sjölander, Michael Charles Sachs · 2025 · Journal of Causal Inference
85. On the physical interpretation of a meta-analysis in the presence of heterogeneity and bias: from clinical trials to Mendelian randomization
    Jack Bowden, Christopher H. Jackson · 2015 · arXiv (Cornell University)
86. Determinantal Generalizations of Instrumental Variables
    Luca Weihs, Bill Robinson, Emilie Dufresne, Jennifer Kenkel, Kaie Kubjas, Reginald L. McGee, Nhan Nguyen, Elina Robeva, Mathias Drton · 2017 · arXiv (Cornell University)
87. Model-assisted complier average treatment effect estimates in randomized experiments with non-compliance and a binary outcome
    Jiyang Ren · 2021 · arXiv (Cornell University)
88. Mendelian randomization for multiple exposures and outcomes with Bayesian Directed Acyclic Graphs exploration and causal effects estimation
    Verena Zuber, Héléne T. Cronjé, Na Cai, Dipender Gill, Leonardo Bottolo · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
89. Genetically predicted serum urate and the risk of all-cause and site-specific cancer: A Mendelian randomization study
    Tahzeeb Fatima, Mats Dehlin, Stephen Burgess, Amy M. Mason, Peter M. Nilsson, Olle Melander, L. Jacobsson, Meliha Crnkic Kapetanovic · 2024 · Research Square
90. Likelihood-based instrumental variable analysis in the presence of an unobserved latent confounder
    Anjun Cao · 2011 · Rutgers University Community Repository (Rutgers University)
91. Современный запрос в оценке медицинских технологий: исследования сравнительной эффективности
    А. В. Павлыш, Russian Federation · 2013 · CyberLeninK (CyberLeninka)
92. Statistical Methods for Marker Discovery and Disease Risk Prediction Amid Unobserved Genetic Interactions
    David M. Soave · 2016 · TSpace (University of Toronto)
93. Magnitude of selection bias in road safety epidemiology, a primer
    Marine Dufournet · 2018 · arXiv (Cornell University)
94. Multiply robust two-sample instrumental variable estimation
    Baoluo Sun · 2018 · arXiv (Cornell University)
95. Causal inference to detect selection bias in road safety epidemiology
    Marine Dufournet, Émilie Lanoy, Jean‐Louis Martin, Vivian Viallon · 2016 · arXiv (Cornell University)
96. Noncompliance and Missing Data in Health Economic Evaluation
    Karla Diaz‐Ordaz, Richard Grieve · 2019 · Oxford Research Encyclopedia of Economics and Finance
97. Evaluating Instrument Validity using the Principle of Independent Mechanisms
    Patrick F. Burauel · 2019 · SSRN Electronic Journal
98. Estimation of local treatment under the binary instrumental variable model
    Linbo Wang, Yuexia Zhang, Thomas S. Richardson, James M. Robins · 2020 · arXiv (Cornell University)
99. Causality and Generalizability: Identifiability and Learning Methods
    Martin Emil Jakobsen · 2021 · arXiv (Cornell University)
100. Modern needs in health technology assessment: a study of comparative effectiveness
     А. В. Павлыш, А. С. Колбин, Гапешин Роман Андреевич, Станислав Михайлович Малышев · 2013 · Педиатрическая фармакология