Biostatistical Considerations When Using RWD and RWE in Clinical Studies for Regulatory Purposes: A Landscape Assessment
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Yixin Fang | Richard C. Zink | Benjamin A. Goldstein | Mark Levenson | Frank Rockhold | Jie Chen | Douglas Faries | Weili He | Mark S Levenson | Martin Ho | Kwan Lee | Pallavi Mishra-Kalyani | Hongwei Wang | F. Rockhold | B. Goldstein | D. Faries | Weili He | P. Mishra-Kalyani | M. Ho | Yixin Fang | Jie Chen | Kwan Lee | Hongwei Wang
[1] Sean M. Randall,et al. Privacy-preserving record linkage on large real world datasets , 2014, J. Biomed. Informatics.
[2] D. Rubin. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials , 2007, Statistics in medicine.
[3] D B Rubin,et al. Multiple imputation in health-care databases: an overview and some applications. , 1991, Statistics in medicine.
[4] C Daniel Mullins,et al. A questionnaire to assess the relevance and credibility of observational studies to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. , 2014, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[5] Michael J Pencina,et al. Controlling for Informed Presence Bias Due to the Number of Health Encounters in an Electronic Health Record. , 2016, American journal of epidemiology.
[6] M. Nojima,et al. Misuse of Regression Adjustment for Additional Confounders Following Insufficient Propensity-Score Balancing. , 2019, Epidemiology.
[7] S. Pocock,et al. Guideline The Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) Statement : Guidelines for reporting observational studies * , 2014 .
[8] Til Stürmer,et al. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. , 2005, American journal of epidemiology.
[9] Richard C. Zink,et al. Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance , 2018, Therapeutic innovation & regulatory science.
[10] Richard F MacLehose,et al. Good practices for quantitative bias analysis. , 2014, International journal of epidemiology.
[11] Qi Jiang,et al. Sources of Safety Data and Statistical Strategies for Design and Analysis , 2018, Therapeutic innovation & regulatory science.
[12] Jeffrey R Curtis,et al. Linkage of a De‐Identified United States Rheumatoid Arthritis Registry With Administrative Data to Facilitate Comparative Effectiveness Research , 2014, Arthritis care & research.
[13] Dylan S. Small,et al. The Trend-in-trend Research Design for Causal Inference , 2017, Epidemiology.
[14] S. Greenland. Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias , 2003, Epidemiology.
[15] M. Baiocchi,et al. Instrumental variable methods for causal inference , 2014, Statistics in medicine.
[16] B. Wells,et al. Strategies for Handling Missing Data in Electronic Health Record Derived Data , 2013, EGEMS.
[17] Sebastian Schneeweiss,et al. When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials? , 2017, Clinical pharmacology and therapeutics.
[18] Heng Li,et al. A Note on Good Practice of Objective Propensity Score Design for Premarket Nonrandomized Medical Device Studies with an Example , 2016 .
[19] R. Brian Haynes,et al. Forming research questions. , 2006 .
[20] Qi Jiang,et al. Sources of Safety Data and Statistical Strategies for Design and Analysis: Real World Insights , 2018, Therapeutic innovation & regulatory science.
[21] N. Dreyer,et al. The GRACE Checklist: A Validated Assessment Tool for High Quality Observational Studies of Comparative Effectiveness , 2016, Journal of managed care & specialty pharmacy.
[22] J. Robins. Estimation of the time-dependent accelerated failure time model in the presence of confounding factors , 1992 .
[23] Benjamin A. Goldstein,et al. Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference , 2017, EGEMS.
[24] Richard E Gliklich,et al. GRACE principles: recognizing high-quality observational studies of comparative effectiveness. , 2010, The American journal of managed care.
[25] K. Roach,et al. Measurement of Health Outcomes: Reliability, Validity and Responsiveness , 2006 .
[26] J. Avorn,et al. A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.
[27] M. Abrahamowicz,et al. The missing cause approach to unmeasured confounding in pharmacoepidemiology , 2016, Statistics in medicine.
[28] T. VanderWeele. Principles of confounder selection , 2019, European Journal of Epidemiology.
[29] Clemens Scott Kruse,et al. The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature , 2018, Journal of Medical Systems.
[30] S Greenland,et al. Basic methods for sensitivity analysis of biases. , 1996, International journal of epidemiology.
[31] T. Lash,et al. A Guide to Implementing Quantitative Bias Analysis , 2009 .
[32] N. Dreyer,et al. The GRACE Checklist for Rating the Quality of Observational Studies of Comparative Effectiveness: A Tale of Hope and Caution , 2014, Journal of managed care & specialty pharmacy.
[33] David Atkins,et al. Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--Part I. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[34] Yunling Xu,et al. Designing Premarket Observational Comparative Studies Using Existing Data as Controls: Challenges and Opportunities , 2014, Journal of biopharmaceutical statistics.
[35] Yu-Chuan Li,et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers , 2015, MedInfo.
[36] Matthew Phelan,et al. How and when informative visit processes can bias inference when using electronic health records data for clinical research , 2019, J. Am. Medical Informatics Assoc..
[37] Uwe Siebert,et al. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retr , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[38] J. Robins,et al. Estimation of Regression Coefficients When Some Regressors are not Always Observed , 1994 .
[39] Margaret Gamalo-Siebers,et al. Propensity‐score‐based priors for Bayesian augmented control design , 2018, Pharmaceutical statistics.
[40] Shuang Wang,et al. Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research , 2014, BMC Medical Informatics and Decision Making.
[41] P. Austin. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies , 2011, Multivariate behavioral research.
[42] Marc Buyse,et al. Open science: The open clinical trials data journey , 2019, Clinical trials.
[43] D. Rubin. For objective causal inference, design trumps analysis , 2008, 0811.1640.
[44] Michael L. Johnson,et al. Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--Part III. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[45] Jon Nicholl,et al. Rethinking pragmatic randomised controlled trials: introducing the “cohort multiple randomised controlled trial” design , 2010, BMJ : British Medical Journal.
[46] S. Schneeweiss,et al. Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs , 2016, Clinical pharmacology and therapeutics.
[47] Ron D Hays,et al. What is sufficient evidence for the reliability and validity of patient-reported outcome measures? , 2007, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[48] David Erlinge,et al. Thrombus aspiration during ST-segment elevation myocardial infarction. , 2013, The New England journal of medicine.
[49] Nick Freemantle,et al. Composite outcomes in randomized trials: greater precision but with greater uncertainty? , 2003, JAMA.
[50] F. Bretz,et al. Estimands and Their Role in Clinical Trials , 2017 .
[51] P. Rosenbaum. Model-Based Direct Adjustment , 1987 .
[52] Hilary Townsend,et al. Natural language processing and clinical outcomes: the promise and progress of NLP for improved care. , 2013, Journal of AHIMA.
[53] S. Suissa,et al. Prevalent new‐user cohort designs for comparative drug effect studies by time‐conditional propensity scores , 2017, Pharmacoepidemiology and drug safety.
[54] David Madigan,et al. Good practices for real‐world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR‐ISPE Special Task Force on real‐world evidence in health care decision making , 2017, Pharmacoepidemiology and drug safety.
[55] J. Avorn,et al. Adjustments for Unmeasured Confounders in Pharmacoepidemiologic Database Studies Using External Information , 2007, Medical care.
[56] Don C Des Jarlais,et al. Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. , 2004, American journal of public health.
[57] J. Seaman,et al. “A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis” , 2016, Pharmacoepidemiology and drug safety.
[58] Wen-Chung Lee. Detecting and correcting the bias of unmeasured factors using perturbation analysis: a data-mining approach , 2014, BMC Medical Research Methodology.
[59] Qi Jiang,et al. Sources of Safety Data and Statistical Strategies for Design and Analysis: Clinical Trials , 2018, Therapeutic innovation & regulatory science.
[60] Olaf Klungel,et al. Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0. , 2017, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[61] D. Melzer,et al. Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review , 2017, Journal of clinical epidemiology.
[62] Yunling Xu,et al. A Study Design for Augmenting the Control Group in a Randomized Controlled Trial: A Quality Process for Interaction Among Stakeholders. , 2020, Therapeutic innovation & regulatory science.
[63] P Zweigenbaum,et al. Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare , 2015, Yearbook of Medical Informatics.
[64] James M Robins,et al. Per-Protocol Analyses of Pragmatic Trials. , 2017, The New England journal of medicine.
[65] Sebastian Schneeweiss,et al. Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. , 2004, American heart journal.
[66] Menggang Yu,et al. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies , 2012, Pharmacoepidemiology and drug safety.
[67] J. Robins,et al. A Structural Approach to Selection Bias , 2004, Epidemiology.
[68] M. J. van der Laan,et al. The International Journal of Biostatistics Targeted Maximum Likelihood Learning , 2011 .
[69] D. DeMets,et al. Considerations in the evaluation of surrogate endpoints in clinical trials. summary of a National Institutes of Health workshop. , 2001, Controlled clinical trials.
[70] Karen Price,et al. Evaluating the impact of unmeasured confounding with internal validation data: an example cost evaluation in type 2 diabetes. , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[71] J. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .
[72] S. Pocock,et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration , 2007, Epidemiology.
[73] Douglas Faries,et al. Bayesian modeling of cost‐effectiveness studies with unmeasured confounding: a simulation study , 2014, Pharmaceutical statistics.
[74] Noor Azina Ismail,et al. Statistical Methods Used to Test for Agreement of Medical Instruments Measuring Continuous Variables in Method Comparison Studies: A Systematic Review , 2012, PloS one.
[75] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[76] David B Richardson,et al. The Active Comparator, New User Study Design in Pharmacoepidemiology: Historical Foundations and Contemporary Application , 2015, Current Epidemiology Reports.
[77] P. Donnan,et al. The PRECIS-2 tool: designing trials that are fit for purpose , 2015, BMJ : British Medical Journal.
[78] Elizabeth A Stuart,et al. Matching methods for causal inference: A review and a look forward. , 2010, Statistical science : a review journal of the Institute of Mathematical Statistics.
[79] Jeffrey A Lieberman,et al. Time to All-Cause Treatment Discontinuation as the Primary Outcome in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Study , 2011, Statistics in biopharmaceutical research.
[80] Xiang Zhang,et al. Addressing unmeasured confounding in comparative observational research , 2018, Pharmacoepidemiology and drug safety.