Statistics faculty run the Department of Health Care Policy's Methods Seminar, which covers methodological topics of interest to health policy and health services researchers. It draws a mixed audience of students, fellows, staff, and faculty. These happy hour seminars are Tuesday afternoons from 4 to 5 pm in the Department of Health Care Policy at Harvard Medical School, 180-A Longwood Ave.
February 12, 2019: Selection bias
Howe and Robinson. Survival-related selection bias in studies of racial health disparities: The importance of the target population and study design. Epidemiology. 29(4):521-4, 2018.
Hernán, Hernández-Díaz, Robins. A structural approach to selection bias. Epidemiology. 15(5): 615-25, 2004.
March 12, 2019
April 23, 2019
May 21, 2019
November 13, 2018: Novel and nontraditional data
Gebru et al. Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. PNAS. 114(50): 13108-13113, 2017.September 25, 2018: Matching and weighting for causal inference in observational studies
King & Nielsen. Why propensity scores should not be used for matching. Working paper. 2016.
Garrido et al. Methods for constructing and assessing propensity scores. Health Services Research. 49(5):1701-20, 2014.
October 23, 2018: Risk of bias tools to evaluate study quality
Losilla, Oliveras, Marin-Garcia, Vives. Three risk of bias tools lead to opposite conclusions in observational research synthesis. Journal of Clinical Epidemiology. 101:61-72, 2018.
April 17, 2018: Divide and recombine for distributed analysis
Lee, Brown, & Ryan. Sufficiency revisited: Rethinking statistical algorithms in the big data era. The American Statistician. 71(3): 202-8, 2017.
March 6, 2018: Machine learning and artificial intelligence for the analysis of medical images
Oakden-Rayner. Exploring the ChestXray14 Data Set: Problems
February 13, 2018: Nonlinear Models
Karaca-Mandic, Norton, Dowd. Interaction terms in nonlinear models. Health Services Research, 47(1):255-274, 2012.
December 5, 2017: Sensitivity analyses
VanderWeele and Ding. Sensitivity analysis in observational research: Introducing the e-value. Ann Intern Med, 167:268-74, 2017.
Rosenbaum. Design of Observational Studies. Ch 3 pp.65-94 New York: Springer, 2010.
November 7, 2017: Discontinuity designs
Moscoe, Bor, Barnighausen. Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. J Clin Epidemiol, 68:132-143, 2015.
Cattaneo, Idrobo, and Titiunik. A practical introduction to regression discontinuity designs. Cambridge Elements: Quantitative and Computational Methods for Social Science. [May 29, 2017 draft].
October 10, 2017: Instrumental variables
Baiocchi, Cheng, Small. Instrumental variable methods for causal inference. Statist Med, 33:2297-40, 2014.
Hernan and Robins. Instruments for Causal Inference: An Epidemiologist’s Dream? Epidemiology, 17(4): 360-72, 2006.
September 12, 2017: Matching and weighting methods
Garrido et al. Methods for constructing and assessing propensity scores. HSR,49(5):1701-20, 2014.
King and Nielsen. Why propensity scores should not be used for matching. Working paper. 16 Dec 2016.
May 16, 2017: The trouble with p-values
Hatch, Wise, Rothman. Inappropriate reliance on p-values in medical research. Twitter, May 9, 2017.
Lappe et al. Effect of Vitamin D and calcium supplementation on cancer incidence in older women: a randomized clinical trial. JAMA, 317(12):1234-43, 2017.
Wasserstein and Lazar. The ASA's statement on p-values: Context, process and purpose. The American Statistician, 70:2:129-133, 2016.
April 18, 2017: Algorithms for billing claims data
DuGoff, Walden, Ronk, Palta, Smith. Can claims data algorithms identify the physician of record? Medical Care, 2017.
March 21, 2017: Hospital safety evaluation
Hatfield, Baugh, Azzone, Normand. Regulator loss functions and hierarchical modeling for safety decision making. Medical Decision Making, 2017.
Spiegelhalter, et al. Statistical methods for healthcare regulation: rating, screening and surveillance. JRSSA, 175(1):1-47, 2012.
February 21, 2017: Ethics in machine learning
Corbett-Davies, Pierson, Feller, Goel. A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear. Washington Post, October 17, 2106.
Kleinberg, Luwig, Mullainathan. A guide to solving social problems with machine learning. Harvard Business Review, December 8, 2016.
Angwin and Lawson. Bias in criminal risk scores is mathematically inevitable, researchers say. ProPublica, December 30, 2106.
November 15, 2016: Family leave policies
Antecol et al. Equal but inequitable: Who benefits from gender neutral tenure clock stopping policies? (Preprint)
The Upshot. A family-friendly policy that's friendliest to male professors. June 24, 2016.
The Hardest Science Blog. Don't change your family-friendly tenure extension policy just yet. June 28, 2016
October 18, 2016: Correcting the scientific record, outcome switching
September 20, 2016: Medical errors
Makary and Daniel. Medical error-- the third leading cause of death in the US. BMJ, 353, 2016.
Shojania. Re: Medical error-- the third leading cause of death in the US. (Rapid Response)
May 17, 2016: Price Transparency
Desai, Hatfield, Hicks, Chernew, and Mehrotra. Association between availability of a price transparency tool and outpatient spending. JAMA, 315:1874-1881, 2016.
Whaley, et al. Association between availability of health service prices and payments for these services. JAMA, 312:1670-1676, 2014.
April 19, 2016: Quality Measurement
Porter, Larsson, and Lee. Standardizing patient outcome measurement. NEJM, 374:504-6, 2016.
Casalino et al. US physician practices spend more than $15.4 billion annually to report quality measures. Health Affairs, 35:401-6, 2016.
March 15, 2016: Mortality Trends
Case and Deaton. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. PNAS, 112:15078–83, 2015.
The Upshot. More details on rising mortality among middle-aged whites. Nov 6, 2015.
February 16, 2016: Reproducibility
Allison, Brown, George, and Kaiser. A tragedy of errors. Nature, 530:27-29, 2016.
Nuzzo. How scientists fool themselves -- and how they can stop. Nature, 526:182-5, 2015.
November 17, 2015: Model Robustness
Gelman. A Bayesian formulation of exploratory data analysis and goodness-of-fit testing. International Statistical Review, 71:369-382, 2003.
Refaeilzadeh, Tang, Liu. Cross-Validation. In Encyclopedia of Database Systems. Editors: Özsu and Liu. Springer, 2009.
October 27, 2015: Open Science
Download the slides
Open Science Collaboration. Estimating the reproducibility of psychological science. Science, 349:aac4716, 2015.
Stodden. Reproducing statistical results. Annual Review of Statistics and Its Applications, 2: 1-19, 2015.
September 22, 2015: Simulations
Rutter, Zaslavsky, and Feuer. Dynamic microsimulation models for health outcomes: A review. Medical Decision Making, 31:10-18, 2011.
Hallgren. Conducting simulation studies in the R programming environment. Tutorials in Quantitative Methods for Psychology, 9:43-60, 2013.
Lofland and Ottesen. Simulation in SAS with comparisons to R. Proceedings of the 2015 Western Users of SAS Software Conference.
Thomas Lumley's post on Herd Immunity Simulations (with movies).
August 25, 2015: Multiple Treatment Comparisons
Download the slides
Moore, Neugebauer, van der Laan, and Tager. Causal inference in epidemiological studies with strong confounding. Statistics in Medicine, 31:1380-1404, 2012.
Salanti, Ades, and Ioannidis. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. Journal of Clinical Epidemiology,64:163-171, 2011.
July 28, 2015: Health Disparities
Scanlan. Can we actually measure health disparities?” Chance, 19:47-51, 2006.
June 30, 2015: Finding and Accessing Data
Discussed multiple data sources and policies for obtaining access. Summary file will remain a living document.
April 14, 2015: Health Innovation
March 10, 2015: Difference-in-Differences
Ryan, Burgess, and Dimick. Why we should not be indifferent to specification choices for difference-in-differences. Health Services Research, 50:1211-1235, 2015.
February 17, 2015: Productivity Growth
Romley, Goldman, and Snood. US hospitals experienced substantial productivity growth during 2002-11. Health Affairs, 34:511-518, 2015.
January 20, 2015: Prediction
Zhao and Weng. Combining PubMed knowledge and EHR data to develop a weighted Bayesian network for pancreatic cancer prediction. Journal of Biomedical Informatics, 44:859-68, 2011.
Rose. Mortality risk score prediction in an elderly population using machine learning. American Journal of Epidemiology, 177:443-452, 2013.
November 13, 2014: Missing Data
Little and Rubin. Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Annual Review of Public Health, 21:121-45, 2000.
White and Carlin. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Statistics in Medicine, 29:2920-31, 2010.
October 9, 2014: Observational Studies
Madigan, et al. A systematic statistical approach to evaluating evidence from observational studies. Annual Review of Statistics and Its Application, 1:11-39, 2014.
Cook, Shadish, and Wong. Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within‐study comparisons. Journal of Policy Analysis and Management, 27.4:724-750, 2008.
September 11, 2014: Propensity Scores
Brooks and Ohsfeldt. Squeezing the balloon: propensity scores and unmeasured covariate balance. Health Services Research, 48(4): 1487-507, 2013.
Ali, Groenwold, and Klungel. Propensity score methods and unobserved covariate imbalance: comments on "squeezing the balloon". Health Services Research, 49(3): 1074-82, 2014.