Methods for Improving Confounding Control in Comparative Effectiveness Research Using Electronic Healthcare Databases

What was the research about? Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Electronic healthcare data are useful for this type of research. These data come from medical records and insurance claims. The data include information about how well patients respond to treatments. But many things—not just treatments— affect whether a patient’s health improves.

[1]  Sebastian Schneeweiss,et al.  Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases , 2017, Epidemiology.

[2]  Sherri Rose,et al.  Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies , 2017, American journal of epidemiology.

[3]  R. Lewis,et al.  Confounding by Indication in Clinical Research. , 2016, JAMA.

[4]  Jason R Guertin,et al.  Performance of the high-dimensional propensity score in adjusting for unmeasured confounders , 2016, European Journal of Clinical Pharmacology.

[5]  J. Gagne,et al.  Comparison of Calipers for Matching on the Disease Risk Score. , 2016, American journal of epidemiology.

[6]  Robert W. Platt,et al.  Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research , 2016, Epidemiology.

[7]  Yen S. Low,et al.  Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records , 2015, Journal of comparative effectiveness research.

[8]  Laura Balzer,et al.  Estimating Effects with Rare Outcomes and High Dimensional Covariates: Knowledge is Power , 2016, Epidemiologic methods.

[9]  Sebastian Schneeweiss,et al.  Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses. , 2015, American journal of epidemiology.

[10]  Mark J. van der Laan,et al.  Targeted Maximum Likelihood Estimation for Network Data , 2015 .

[11]  M. J. van der Laan,et al.  Practice of Epidemiology Improving Propensity Score Estimators ’ Robustness to Model Misspecification Using Super Learner , 2015 .

[12]  Alan R. Ellis,et al.  The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score. , 2014, American journal of epidemiology.

[13]  A. Hoes,et al.  Propensity score balance measures in pharmacoepidemiology: a simulation study , 2014, Pharmacoepidemiology and drug safety.

[14]  Jeremy A Rassen,et al.  Metrics for covariate balance in cohort studies of causal effects , 2014, Statistics in medicine.

[15]  Jennifer M. Polinski,et al.  Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases , 2014, Comput. Stat. Data Anal..

[16]  Til Stürmer,et al.  Balancing Automated Procedures for Confounding Control with Background Knowledge , 2014 .

[17]  Elizabeth A Stuart,et al.  Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. , 2013, Journal of clinical epidemiology.

[18]  S. Rose.  Mortality risk score prediction in an elderly population using machine learning. , 2013, American journal of epidemiology.

[19]  S. Schneeweiss,et al.  Practice of Epidemiology Implications of M Bias in Epidemiologic Studies: a Simulation Study , 2022 .

[20]  John P. A. Ioannidis,et al.  Methodological standards and patient-centeredness in comparative effectiveness research: the PCORI perspective. , 2012, JAMA.

[21]  Sebastian Schneeweiss,et al.  Using high‐dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system , 2012, Pharmacoepidemiology and drug safety.

[22]  J. Myers,et al.  Effects of adjusting for instrumental variables on bias and precision of effect estimates. , 2011, American journal of epidemiology.

[23]  M Alan Brookhart,et al.  Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. , 2011, American journal of epidemiology.

[24]  Til Stürmer,et al.  The role of the c‐statistic in variable selection for propensity score models , 2011, Pharmacoepidemiology and drug safety.

[25]  P. Austin,et al.  Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies , 2010, Pharmaceutical statistics.

[26]  Sebastian Schneeweiss,et al.  A basic study design for expedited safety signal evaluation based on electronic healthcare data , 2010, Pharmacoepidemiology and drug safety.

[27]  M. J. van der Laan,et al.  The International Journal of Biostatistics Collaborative Double Robust Targeted Maximum Likelihood Estimation , 2011 .

[28]  Mark J van der Laan,et al.  An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics , 2010, The international journal of biostatistics.

[29]  P. Austin.  Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples , 2009, Statistics in medicine.

[30]  J. Avorn,et al.  High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data , 2009, Epidemiology.

[31]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. , 2009, Circulation.

[32]  L. Tavazzi,et al.  Effect of rosuvastatin in patients with chronic heart failure (the GISSI-HF trial): a randomised, double-blind, placebo-controlled trial. , 2008, Lancet.

[33]  A. Jánosi,et al.  Rosuvastatin in older patients with systolic heart failure. , 2007, The New England journal of medicine.

[34]  S. Schneeweiss,et al.  Developments in Post‐marketing Comparative Effectiveness Research , 2007, Clinical pharmacology and therapeutics.

[35]  Sebastian Schneeweiss.  Understanding secondary databases: a commentary on "Sources of bias for health state characteristics in secondary databases". , 2007, Journal of clinical epidemiology.

[36]  W. Frishman.  Statin Therapy and Risks for Death and Hospitalization in Chronic Heart Failure , 2007 .

[37]  M. J. van der Laan,et al.  Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .

[38]  J. Avorn,et al.  Variable selection for propensity score models. , 2006, American journal of epidemiology.

[39]  Til Stürmer,et al.  A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. , 2006, Journal of clinical epidemiology.

[40]  J. Avorn,et al.  A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.

[41]  Vincent Mor,et al.  Weaknesses of goodness‐of‐fit tests for evaluating propensity score models: the case of the omitted confounder , 2005, Pharmacoepidemiology and drug safety.

[42]  C. Schweiger.  [Statins and the risk of dementia]. , 2001, Italian heart journal. Supplement : official journal of the Italian Federation of Cardiology.

[43]  H Jick,et al.  HMG-CoA reductase inhibitors and the risk of fractures. , 2000, JAMA.

[44]  J. Pearl,et al.  Confounding and Collapsibility in Causal Inference , 1999 .

[45]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[46]  I. Bross.  Spurious effects from an extraneous variable. , 1966, Journal of chronic diseases.