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Estimating causal effects from randomized experiments is central to clinical research.
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
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A New Rating Scale for Alzheimer’s Disease
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Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
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An inventory to assess activities of daily living for clinical trials in Alzheimer’s disease. The Alzheimer’s Disease Cooperative Study
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Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model
J. Scott Long and Laurie H. Ervin · 2000
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Asymptotic Statistics
A W van der Vaart · 2000
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Rethinking historical controls
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Efficiency Study of Estimators for a Treatment Effect in a Pretest–Posttest Trial
Li Yang and Anastasios A Tsiatis · 2001
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Estimating causal effects, 2002
George Maldonado and Sander Greenland · 2002
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Semiparametric Estimation of Treatment Effect in a Pretest‐Posttest Study
Selene Leon, Anastasios A Tsiatis, and Marie Davidian · 2003
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Causal Inference Using Potential Outcomes
Donald B Rubin · 2005
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David A. Freedman · 2006
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A simple sample size formula for analysis of covariance in randomized clinical trials
George F Borm, Jaap Fransen, and Wim A J G Lemmens · 2007
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A Tsiatis · 2007
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A High-Density Whole-Genome Association Study Reveals That APOE Is the Major Susceptibility Gene for Sporadic Late-Onset Alzheimer’s Disease
Keith D Coon, Amanda J Myers, David W Craig, Jennifer A Webster, John V Pearson, Diane Hu Lince, Victoria L Zismann, Thomas G Beach, Doris Leung, Leslie Bryden, Rebecca F Halperin, Lauren Marlowe, Mona Kaleem, Douglas G Walker, Rivka Ravid, Christopher B Heward, Joseph Rogers, Andreas Papassotiropoulos, Eric M Reiman, John Hardy, and Dietrich A Stephan · 2007
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Randomized Clinical Trials and Observational Studies Guidelines for Assessing Respective Strengths and Limitations
Edward L. Hannan · 2008
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The prognostic analogue of the propensity score
B B Hansen · 2008
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Value and Limitations of Existing Scores for the Assessment of Cardiovascular Risk A Review for Clinicians
Marie Therese Cooney, Alexandra L. Dudina, and Ian M. Graham · 2009
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The Coalition Against Major Diseases: Developing Tools for an Integrated Drug Development Process for Alzheimer’s and Parkinson’s Diseases
K Romero, M Mars, D Frank, M Anthony, J Neville, L Kirby, K Smith, and R L Woosley · 2009
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Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables
Michael Rosenblum and Mark J van der Laan · 2010
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Docosahexaenoic Acid Supplementation and Cognitive Decline in Alzheimer Disease: A Randomized Trial
Joseph F. Quinn, Rema Raman, Ronald G. Thomas, Karin Yurko-Mauro, Edward B. Nelson, Christopher Van Dyck, James E. Galvin, Jennifer Emond, Clifford R. Jack, Michael Weiner, Lynne Shinto, and Paul S. Aisen · 2010
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Ye Luo and Martin Spindler · 2016
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High-dimensional regression adjustments in randomized experiments
Stefan Wager, Wenfei Du, Jonathan Taylor, and Robert J Tibshirani · 2016
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The Effectiveness of Transfer Learning in Electronic Health Records Data
Sebastien Dubois, Nathanael Romano, Kenneth Jung, Nigam Shah, and David Kale · 2017
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Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials
Jessica Lim, Rosalind Walley, Jiacheng Yuan, Jeen Liu, Abhishek Dabral, Nicky Best, Andrew Grieve, Lisa Hampson, Josephine Wolfram, Phil Woodward, Florence Yong, Xiang Zhang, and Ed Bowen · 2018
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Deep Neural Networks for Estimation and Inference
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The Methods of Comparative Effectiveness Research
Harold C. Sox and Steven N. Goodman · 2012
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Scikit-learn: Machine Learning in Python
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The application of differential privacy to health data
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Desideratum for Evidence Based Epidemiology
J. Marc Overhage, Patrick B. Ryan, Martijn J. Schuemie, and Paul E. Stang · 2013
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Least squares after model selection in high-dimensional sparse models
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Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score
Richard Wyss, Mark Lunt, M. Alan Brookhart, Robert J. Glynn, and Til Stürmer · 2014
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The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies
Brennan C Kahan, Vipul Jairath, Caroline J Doré, and Tim P Morris · 2014
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Max H Farrell, Tengyuan Liang, and Sanjog Misra · 2018
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Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins · 2018
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Federated learning of predictive models from federated Electronic Health Records
Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch. Paschalidis, and Wei Shi · 2018
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg S. Corrado, and Jeffrey Dean · 2018
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Deep learning for healthcare: review, opportunities and challenges
Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley · 2018
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Using the Prognostic Score to Reduce Heterogeneity in Observational Studies
Rachael C Aikens, Dylan Greaves, and Michael Baiocchi · 2019
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Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions
Bingkai Wang, Elizabeth L. Ogburn, and Michael Rosenblum · 2019
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Foundations of Agnostic Statistics
Peter M Aronow and Benjamin T Miller · 2019
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Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
Coalition Against Major Diseases, Organiza, Abbott, Alliance for Aging Research, Alzheimer’s Association, Alzheimer’s Foundation of America, AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Critical Path Institute, CHDI Foundation, Inc , Eli Lilly and Company, F Hoffmann-La Roche Ltd, Forest Research Institute, Genentech, Inc , GlaxoSmithKline, Johnson & Johnson, National Health Council, Novartis Pharmaceuticals Corporation, Parkinson’s Action Network, Parkinson’s Disease Foundation, Pfizer, Inc , sanofi-aventis Collaborating, Charles K Fisher, Aaron M Smith, and Jonathan R Walsh · 2019
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Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control
Annette Kopp‐Schneider, Silvia Calderazzo, and Manuel Wiesenfarth · 2020
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A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)
Mercedeh Ghadessi, Rui Tang, Joey Zhou, Rong Liu, Chenkun Wang, Kiichiro Toyoizumi, Chaoqun Mei, Lixia Zhang, C. Q. Deng, and Robert A. Beckman · 2020
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