Targeted Maximum Likelihood Estimation of Causal Effects with Interference: A Simulation Study

Overview

Journal Stat Med

Publisher Wiley

Specialty Public Health

Date 2022 Jul 19

PMID 35852017

Authors

Paul N Zivich

Michael G Hudgens

Maurice A Brookhart

James Moody

David J Weber

Allison E Aiello

Affiliations

Soon will be listed here.

Abstract

Interference, the dependency of an individual's potential outcome on the exposure of other individuals, is a common occurrence in medicine and public health. Recently, targeted maximum likelihood estimation (TMLE) has been extended to settings of interference, including in the context of estimation of the mean of an outcome under a specified distribution of exposure, referred to as a policy. This paper summarizes how TMLE for independent data is extended to general interference (network-TMLE). An extensive simulation study is presented of network-TMLE, consisting of four data generating mechanisms (unit-treatment effect only, spillover effects only, unit-treatment and spillover effects, infection transmission) in networks of varying structures. Simulations show that network-TMLE performs well across scenarios with interference, but issues manifest when policies are not well-supported by the observed data, potentially leading to poor confidence interval coverage. Guidance for practical application, freely available software, and areas of future work are provided.

References

Edwards J, Cole S, Lesko C, Mathews W, Moore R, Mugavero M . An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects. Am J Epidemiol. 2016; 184(4):336-44. PMC: 4983650. DOI: 10.1093/aje/kwv339. View

Clark A, Wilder C, Winstanley E . A systematic review of community opioid overdose prevention and naloxone distribution programs. J Addict Med. 2014; 8(3):153-63. DOI: 10.1097/ADM.0000000000000034. View

Christakis N, Fowler J . The spread of obesity in a large social network over 32 years. N Engl J Med. 2007; 357(4):370-9. DOI: 10.1056/NEJMsa066082. View

Balzer L, Petersen M, van der Laan M . Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching. Stat Med. 2016; 35(21):3717-32. PMC: 4965321. DOI: 10.1002/sim.6965. View

Jarvis M, Feyerabend C, Bryant A, Hedges B, Primatesta P . Passive smoking in the home: plasma cotinine concentrations in non-smokers with smoking partners. Tob Control. 2001; 10(4):368-74. PMC: 1747624. DOI: 10.1136/tc.10.4.368. View

Virtanen P, Gommers R, Oliphant T, Haberland M, Reddy T, Cournapeau D . SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020; 17(3):261-272. PMC: 7056644. DOI: 10.1038/s41592-019-0686-2. View

Grundy S, Stone N, Bailey A, Beam C, Birtcher K, Blumenthal R . 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018; 73(24):e285-e350. DOI: 10.1016/j.jacc.2018.11.003. View

Sofrygin O, van der Laan M . Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population. J Causal Inference. 2017; 5(1). PMC: 5650205. DOI: 10.1515/jci-2016-0003. View

Du Y, Cui X, Sidorenkov G, Groen H, Vliegenthart R, Heuvelmans M . Lung cancer occurrence attributable to passive smoking among never smokers in China: a systematic review and meta-analysis. Transl Lung Cancer Res. 2020; 9(2):204-217. PMC: 7225146. DOI: 10.21037/tlcr.2020.02.11. View

10.

Zedler B, Xie L, Wang L, Joyce A, Vick C, Kariburyo F . Risk factors for serious prescription opioid-related toxicity or overdose among Veterans Health Administration patients. Pain Med. 2014; 15(11):1911-29. DOI: 10.1111/pme.12480. View

11.

Li Y, Undurraga E, Zubizarreta J . Effectiveness of Localized Lockdowns in the COVID-19 Pandemic. Am J Epidemiol. 2022; 191(5):812-824. PMC: 8807239. DOI: 10.1093/aje/kwac008. View

12.

Seaman S, Pavlou M, Copas A . Review of methods for handling confounding by cluster and informative cluster size in clustered data. Stat Med. 2014; 33(30):5371-87. PMC: 4320764. DOI: 10.1002/sim.6277. View

13.

Seamans M, Carey T, Westreich D, Cole S, Wheeler S, Alexander G . Association of Household Opioid Availability and Prescription Opioid Initiation Among Household Members. JAMA Intern Med. 2017; 178(1):102-109. PMC: 5833498. DOI: 10.1001/jamainternmed.2017.7280. View

14.

Halloran M, Haber M, Longini Jr I . Interpretation and estimation of vaccine efficacy under heterogeneity. Am J Epidemiol. 1992; 136(3):328-43. DOI: 10.1093/oxfordjournals.aje.a116498. View

15.

Zimmerman R, Nowalk M, Raymund M, Tabbarah M, Hall D, Wahrenberger J . Tailored interventions to increase influenza vaccination in neighborhood health centers serving the disadvantaged. Am J Public Health. 2003; 93(10):1699-705. PMC: 1448037. DOI: 10.2105/ajph.93.10.1699. View

16.

Zhang S, de la Haye K, Ji M, An R . Applications of social network analysis to obesity: a systematic review. Obes Rev. 2018; 19(7):976-988. DOI: 10.1111/obr.12684. View

17.

Green T, Ray M, Bowman S, McKenzie M, Rich J . Two cases of intranasal naloxone self-administration in opioid overdose. Subst Abus. 2014; 35(2):129-32. PMC: 4019939. DOI: 10.1080/08897077.2013.825691. View

18.

Schuler M, Rose S . Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies. Am J Epidemiol. 2016; 185(1):65-73. DOI: 10.1093/aje/kww165. View

19.

Hernan M, Robins J . Estimating causal effects from epidemiological data. J Epidemiol Community Health. 2006; 60(7):578-86. PMC: 2652882. DOI: 10.1136/jech.2004.029496. View

20.

Munoz I, van der Laan M . Population intervention causal effects based on stochastic interventions. Biometrics. 2011; 68(2):541-9. PMC: 4117410. DOI: 10.1111/j.1541-0420.2011.01685.x. View