Publications: Methods

IP weighting

• Random measurement times: Hernán MA, McAdams M, McGrath N, Lanoy E, Costagliola D. Observation plans in longitudinal studies with time-varying treatments. Statistical Methods in Medical Research 2009; 18(1):27-52. PMCID: PMC3878210
• Dynamic strategies for treatment initiation with a grace period: Cain LE, Robins JM, Lanoy E, Logan R, Costagliola D, Hernán MA. When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. International Journal of Biostatistics 2010; 6(2) Article 18. PMCID: PMC3406513
• Dynamic strategies for treatment switching with a grace period: Cain LE, Saag MS, Petersen M, May MT, Ingle SM, Logan R, Robins JM, Abgrall S, Shepherd BE, Deeks SG, Gill MJ, Touloumi G, Vourli G, Dabis F, Vandenhende MA, Reiss P, van Sighem A, Samji H, Hogg RS, Rybniker J, Sabin CA, Jose S, Del Amo J, Moreno S, Rodríguez B, Cozzi-Lepri A, Boswell SL, Stephan C, Pérez-Hoyos S, Jarrin I, Guest JL, Monforte AD, Antinori A, Moore R, Campbell CN, Casabona J, Meyer L, Seng R, Phillips AN, Bucher HC, Egger M, Mugavero MJ, Haubrich R, Geng EH, Olson A, Eron JJ, Napravnik S, Kitahata MM, Van Rompaey SE, Teira R, Justice AC, Tate JP, Costagliola D, Sterne JA, Hernán MA; Antiretroviral Therapy Cohort Collaboration, Centers for AIDS Research Network of Integrated Clinical Systems, and the HIV-CAUSAL Collaboration. Using observational data to emulate a randomized trial of dynamic treatment-switching strategies: an application to antiretroviral therapy. International Journal of Epidemiology 2016; 45(6):2038-2049. PMCID: PMC5841611
• Machine learning for IP weighting: Gruber S, Logan RW, Jarrin I, Monge S, Hernán MA. Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets. Statistics in Medicine 2015; 34(1):106-117. PMCID: PMC4262745
• Bayesian inference and IP weighting: Robins JM, Hernán MA, Wasserman L. On Bayesian estimation of marginal structural models. Biometrics 2015; 71: 296-299. PMCID: PMC4667748
• Comparing joint dynamic strategies with and without a “no direct effect” assumption: HIV-CAUSAL Collaboration, CNICS. Emulating a trial of joint dynamic strategies: an application to monitoring and treatment of HIV-positive patients. Statistics in Medicine 2019; 38(13): 2428-2446. PMCID: PMC6499640
  • SAS code
• Causal inference under constraints: Caniglia EC, Murray EJ, Hernán MA, Shahn Z. Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models. Statistics in Medicine 2021 (in press).

Parametric g-formula

• Static treatment strategies: Westreich D, Cole SR, Young JG, Pallela F, Tien PC, Kingsley L, Gange SJ, Hernán MA. The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death. Statistics in Medicine 2012; 31(18): 2000-2009.  PMCID: PMC3641816
• Dynamic treatment strategies: Young JG, Cain LE, Robins JM, O’Reilly E, Hernán MA. Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula. Statistics in Biosciences 2011; 3:119-143. PMCID: PMC3769803
• Equivalence with agent-based models: Murray EJ, Robins JM, Seage GR III, Freedberg KA, Hernán MA. A comparison of agent-based models and the parametric g-formula for causal inference. American Journal of Epidemiology 2017; 186(2):131-142. PMCID: PMC5860229
• Calibration of agent-based models: Murray EJ, Robins JM, Seage GR, Lodi S, Hyle EP, Reddy KP, Freedberg KA, Hernán MA. Using observational data to calibrate simulation models. Medical Decision Making 2018; 38(2):212-224. PMCID: PMC5771959
• NICE and ICE estimators of the parametric g-formula: Wen L, Young JG, Robins JM, Hernán MA. Parametric g-formula implementations for causal survival analyses. Biometrics 2021 (in press).
• Misspecification of parametric models: McGrath S, Young JG, Hernán MA. Revisiting the g-null paradox. Epidemiology 2021 (in press).