Causal Inference with Secondary Outcomes
Statistics in Biosciences - Trang 1-14 - 2023
Tóm tắt
In this paper, we develop new methods for identifying causal effects in the presence of unmeasured confounding with continuous treatment and outcome. Under a set of linear structural equation models, we invent two identification strategies by introducing a secondary outcome. Specifically, we utilize the symmetry and asymmetry properties of distributions of random variables to achieve identification. We develop accompanying estimating procedures and evaluate their finite sample performance through simulations and a data application studying the causal effect of tau protein level on behavioral deficits in Alzheimer’s disease.
Tài liệu tham khảo
Ashford JW, Kolm P, Colliver JA, Bekian C, Hsu L-N (1989) Alzheimer patient evaluation and the mini-mental state: item characteristic curve analysis. J Gerontol 44(5):139–146
Blennow K (2004) Cerebrospinal fluid protein biomarkers for Alzheimer’s disease. NeuroRx 1(2):213–225
Burgess S (2014) Sample size and power calculations in mendelian randomization with a single instrumental variable and a binary outcome. Int J Epidemiol 43(3):922–929
Copas JB, Jackson D, White IR, Riley RD (2018) The role of secondary outcomes in multivariate meta-analysis. J R Stat Soc Ser C 67(5):1177–1205
Cosslett SR (1983) Distribution-free maximum likelihood estimator of the binary choice model. Econometrica 51(3):765–782
D’Amour A (2019) On multi-cause approaches to causal inference with unobserved counfounding: two cautionary failure cases and a promising alternative. In: The 22nd international conference on artificial intelligence and statistics, pp 3478–3486
Delaigle A, Hall P (2016) Methodology for non-parametric deconvolution when the error distribution is unknown. J R Stat Soc Ser B 78(1):231–252
DeSouza CM (1992) An approximate bivariate Bayesian method for analyzing small frequencies. Biometrics 48(4):1113–1130
Guerrero-Berroa E, Luo X, Schmeidler J, Rapp MA, Dahlman K, Grossman HT, Haroutunian V, Beeri MS (2009) The MMSE orientation for time domain is a strong predictor of subsequent cognitive decline in the elderly. Int J Geriatr Psychiatry 24(12):1429–1437
Guo Z, Kang H, Cai TT, Small DS (2018) Confidence intervals for causal effects with invalid instruments using two-stage hard thresholding with voting. J R Stat Soc Ser B 80(4):793–815
Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50(4):1029–1054
Harahsheh Y, Ho KM (2021) Optimizing the utility of secondary outcomes in randomized controlled trials. J Emerg Crit Care Med 5:8–8
Iqbal K, Alonso AC, Chen S, Chohan MO, El-Akkad E, Gong C-X, Khatoon S, Li B, Liu F, Rahman A et al (2005) Tau pathology in Alzheimer disease and other tauopathies. Biochim Biophys Acta 1739(2–3):198–210
Iqbal K, Liu F, Gong C-X, Grundke-Iqbal I (2010) Tau in Alzheimer disease and related tauopathies. Curr Alzheimer Res 7(8):656–664
Jo B, Muthen BO (2001) Modeling of intervention effects with noncompliance: a latent variable approach for randomized trials. In: Marcoulides GA, Schumacker RE (eds) New developments and techniques in structural equation modeling. Psychology Press, Hove, p 32
Kametani F, Hasegawa M (2018) Reconsideration of amyloid hypothesis and tau hypothesis in Alzheimer’s disease. Front Neurosci 12:25
Kang H, Zhang A, Cai TT, Small DS (2016) Instrumental variables estimation with some invalid instruments and its application to mendelian randomization. J Am Stat Assoc 111(513):132–144
Kelley BJ, Petersen RC (2007) Alzheimer’s disease and mild cognitive impairment. Neurol Clin 25(3):577–609
Kong D, Yang S, Wang L (2022) Identifiability of causal effects with multiple causes and a binary outcome. Biometrika 109(1):265–272
Mealli F, Pacini B (2013) Using secondary outcomes to sharpen inference in randomized experiments with noncompliance. J Am Stat Assoc 108(503):1120–1131
Miao W, Shi X, Tchetgen Tchetgen E (2018) A confounding bridge approach for double negative control inference on causal effects. arXiv preprint. arXiv:1808.04945
Ogburn EL, Shpitser I, Tchetgen EJT (2019) Comment on blessings of multiple causes. J Am Stat Assoc 114(528):1611–1615
Peters J, Buhlmann P (2014) Identifiability of Gaussian structural equation models with equal error variances. Biometrika 101(1):219–228
Shi X, Miao W, Tchetgen ET (2020) A selective review of negative control methods in epidemiology. Curr Epidemiol Rep 7(4):190–202
Tchetgen Tchetgen EJ (2014) A general regression framework for a secondary outcome in case-control studies. Biostatistics 15(1):117–128
van der Vaart A, Wellner JA (2011) A local maximal inequality under uniform entropy. Electron J Stat 5:192–203
Wang Y, Blei DM (2019) The blessings of multiple causes. J Am Stat Assoc 114(528):1574–1596
Wright PG, Wright S (1928) The tariff on animal and vegetable oils. Macmillan, New York
Yu D, Wang L, Kong D, Zhu H (2022) Mapping the genetic-imaging-clinical pathway with applications to Alzheimer’s disease. J Am Stat Assoc. https://doi.org/10.1080/01621459.2022.2087658
Zhang X, Wang L, Volgushev S, Kong D (2022) Fighting noise with noise: causal inference with many candidate instruments. arXiv preprint. arXiv:2203.09330
Zhou Y, Tang D, Kong D, Wang L (2020) The promises of parallel outcomes. arXiv preprint. arXiv:2012.05849