Jessica Franklin, PhD

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Brigham & Women’s Hospital
Department of Medicine
Division of Pharmacoepidemiology & Pharmacoeconomics
1620 Tremont Street, Suite 3030,
Boston, MA 02120
Phone: 617-278-0675 | Fax: 617-232-8602

Jessica Franklin, PhD, is an Assistant Professor of Medicine at Harvard Medical School and biostatistician in the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital. Her research focuses on developing and applying statistical methods for the study of medicines, including comparative effectiveness and adverse effects of drugs, the consequences of drug policy, and drug utilization. Her methodological interests are in causal inference and hierarchical modeling. Dr. Franklin received her Bachelor’s degree in mathematics at the University of Georgia and her doctorate in biostatistics at the Johns Hopkins Bloomberg School of Public Health.

Simulation document showing bias in the pooled ROR when selectively inverting clinical questions, as described in: Franklin JM, Dejene S, Huybrechts KF, Wang SV, Kulldorff M, Rothman KJ. A bias in the evaluation of bias comparing randomized trials with nonexperimental studies. Epidemiologic Methods 2017; in press.

Selected Publications | Current PubMed Search Results

Causal Inference for Medication Effects
  • Franklin JM, Dejene S, Huybrechts KF, Wang SV, Kulldorff M, Rothman KJ. A bias in the evaluation of bias comparing randomized trials with nonexperimental studies. Epidemiologic Methods 2017; in press.
  • Franklin JM, Eddings W, Austin PC, Stuart EA, Schneeweiss S. Comparing the performance of propensity score methods in healthcare database studies with rare outcomes. Statistics in Medicine 2017; in press.
  • Franklin JM, Schneeweiss S, Solomon DS. Assessment of confounders in comparative effectiveness studies from secondary databases. American Journal of Epidemiology 2017; 185(6):474-478
  • Franklin JM, Eddings W, Glynn RJ, Schneeweiss S. Regularized regression versus the high-dimensional propensity score for confounding adjustment in secondary database analyses. American Journal of Epidemiology. 2015; 182(7):651-659
  • Franklin JM, Eddings W, Schneeweiss S, Rassen JA. Incorporating linked healthcare claims to improve confounding control in a study of in-hospital medication use. Drug Safety. 2015; 38(6):589-600
  • Franklin JM, Schneeweiss S, Huybrechts KF, Glynn RJ. Evaluating possible confounding by prescriber in comparative effectiveness research. Epidemiology. 2015; 26(2):238-241.
  • Franklin JM, Rassen JA, Ackermann D, Bartels DB, Schneeweiss S. Metrics for covariate balance in cohort studies of causal effects. Statistics in Medicine. 2014;33(10):1685-99
  • Franklin JM, Schneeweiss S, Polinski JM, Rassen JA.  Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases. Computational Statistics and Data Analysis.  2014; 72:219-226
  • Franklin JM, Rassen JA, Bartels DB, Schneeweiss S. Prospective cohort studies of newly marketed medications: Using covariate data to inform the design of large-scale studies. Epidemiology. 2014; 25(1):126-133
  • Myers JA, Louis TA. Comparing treatments via the propensity score: stratification or modeling? Health Services Outcomes Research Methodology. 2012; 12(1):29-43 *HPSS Student Paper Award Winner
  • Myers, JA, Rassen, JA, Gagne, JG, Huybrechts, KF, Schneeweiss, S, Rothman, KJ, Joffe, MM, Glynn, RJ.  Effects of adjusting for instrumental variables on bias and precision of effect estimates. American Journal of Epidemiology. 2011; 174(11):1213-1222 *ASA Section on Statistics in Epidemiology Young Investigator Award winner
Applied Pharmacoepidemiology
  • Feldman C, Marty FM, Winkelmayer WC, Guan H, Franklin JM, Solomon DH, Costenbader KH, Kim SC. Comparative rates of serious infections among patients with systemic lupus erythematosus receiving immunosuppressive medications. Arthritis & Rheumatology 2017; 69(2):387-397
  • Huybrechts KF, Palmsten K, Avorn J, Cohen LS, Holmes LB, Franklin JM, Mogun H, Levin R, Kowal M, Setoguchi S, Hernandez-Diaz S. Antidepressant Use in Pregnancy and the Risk of Cardiac Defects. NEJM. 2014; 370(25):2397-2407
  • Kim SC, Solomon DH, Liu J, Franklin JM, Glynn RJ, Schneeweiss S. Risk of Venous Thromboembolism in Patients with Rheumatoid Arthritis Initiating Disease-Modifying Antirheumatic Drugs. American Journal of Medicine. 2014; 128(5)539.e7-17
  • Bateman BT, Bykov K, Choudhry NK, Schneeweiss S, Gagne JJ, Polinski JM, Franklin JM, Doherty M, Fischer MA, Rassen JA. Type of stress ulcer prophylaxis and the risk of nosocomial pneumonia in cardiac surgical patients: a cohort study. BMJ. 2013; 347:f5416
Medication Adherence
  • Choudhry NK, Krumme AA, Kahn N, Tong AY, Ercole P, Girdish C, Matlin O, Shrank WH, Brennan TA, Franklin JM. Reminder devices to increase evidence-based medication use: The Randomized Evaluation to Measure Improvements in Non-adherence from Low-Cost Devices (REMIND) trial. JAMA Internal Medicine 2017; 177(5):624-631
  • Franklin JM, Shrank WH, Lii J, Krumme AK, Matlin OS, Brennan TA, Choudhry NK. Observing versus predicting: Initial patterns of filling predict long-term adherence more accurately than high-dimensional modeling techniques. Health Services Research. 2016; 51(1):220-239
  • Franklin JM, Krumme AK, Tong AY, Shrank WH, Matlin OS, Brennan TA, Choudhry NK. Association between trajectories of statin adherence and subsequent cardiovascular events. Pharmacoepidemiology and Drug Safety. 2015; 24(10):1105-1113
  • Sanfelix-Gimeno G, Franklin JM, Shrank WH, Carlo M, Tong AY, Reisman L, Matlin OS, Brennan TA, Choudhry NK.  Did HEDIS get it right? Evaluating the quality of a quality measure: adherence to beta-blockers and cardiovascular outcomes after myocardial infarction. Medical Care. 2014; 52(7):669-76
  • Franklin JM, Shrank WH, Pakes J, Sanfélix-Gimeno G, Matlin OS, Brennan TA, Choudhry NK. Group-based trajectory models: A new approach to classifying and predicting long-term medication adherence. Medical Care. 2013; 51(9):789-796