Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided

Adjusting for bias introduced by instrumental variable estimation in the Cox Proportional Hazards Model

Instrumental variable (IV) methods are widely used for estimating average treatment effects in the presence of unmeasured confounders. However, the capability of existing IV procedures, and most notably the two-stage residual inclusion (2SRI) procedure recommended for use in nonlinear contexts, to account for unmeasured confounders in the Cox proportional hazard model is unclear. We show that instrumenting an endogenous treatment induces an unmeasured covariate, referred to as an individual frailty in survival analysis parlance, which if not accounted for leads to bias. We propose a new procedure that augments 2SRI with an individual frailty and prove that it is consistent under certain conditions. The finite sample-size behavior is studied across a broad set of conditions via Monte Carlo simulations. Finally, the proposed methodology is used to estimate the average effect of carotid endarterectomy versus carotid artery stenting on the mortality of patients suffering from carotid artery disease. Results suggest that the 2SRI-frailty estimator generally reduces the bias of both point and interval estimators compared to traditional 2SRI.Comment: 27 pages, 8 figures, 4 table

Estimating Peer Effects in Longitudinal Dyadic Data Using Instrumental Variables

The identification of causal peer effects (also known as social contagion or induction) from observational data in social networks is challenged by two distinct sources of bias: latent homophily and unobserved confounding. In this paper, we investigate how causal peer effects of traits and behaviors can be identified using genes (or other structurally isomorphic variables) as instrumental variables (IV) in a large set of data generating models with homophily and confounding. We use directed acyclic graphs to represent these models and employ multiple IV strategies and report three main identification results. First, using a single fixed gene (or allele) as an IV will generally fail to identify peer effects if the gene affects past values of the treatment. Second, multiple fixed genes/alleles, or, more promisingly, time-varying gene expression, can identify peer effects if we instrument exclusion violations as well as the focal treatment. Third, we show that IV identification of peer effects remains possible even under multiple complications often regarded as lethal for IV identification of intra-individual effects, such as pleiotropy on observables and unobservables, homophily on past phenotype, past and ongoing homophily on genotype, inter-phenotype peer effects, population stratification, gene expression that is endogenous to past phenotype and past gene expression, and others. We apply our identification results to estimating peer effects of body mass index (BMI) among friends and spouses in the Framingham Heart Study. Results suggest a positive causal peer effect of BMI between friends

Changes in Physician Antipsychotic Prescribing Preferences, 2002–2007

Objective Physician antipsychotic prescribing behavior may be influenced by comparative effectiveness evidence, regulatory warnings, and formulary and other restrictions on these drugs. This study measured changes in the degree to which physicians are able to customize treatment choices and changes in physician preferences for specific agents after these events. Methods The study used 2002–2007 prescribing data from the IMS Health Xponent database and data on physician characteristics from the American Medical Association for a longitudinal cohort of 7,399 physicians. Descriptive and multivariable regression analyses were conducted of the concentration of prescribing (physician-level Herfindahl index) and preferences for and likelihood of prescribing two first-generation antipsychotics and six second-generation antipsychotics. Analyses adjusted for prescribing volume, specialty, demographic characteristics, practice setting, and education. Results Antipsychotic prescribing was highly concentrated at the physician level, with a mean unadjusted Herfindahl index of .33 in 2002 and .29 in 2007. Psychiatrists reduced the concentration of their prescribing more over time than did other physicians. High-volume psychiatrists had a Herfindahl index that was half that of low-volume physicians in other specialties (.18 versus .36), a difference that remained significant (p<.001) after adjustment for physician characteristics. The share of physicians preferring olanzapine dropped from 29.9% in 2002 to 10.3% in 2007 (p<.001) while the share favoring quetiapine increased from 9.4% to 44.5% (p<.001). Few physicians (<5%) preferred a first-generation antipsychotic in 2002 or 2007. Conclusions Preferences for specific antipsychotics changed dramatically during this period. Although physician prescribing remained heavily concentrated, the concentration decreased over time, particularly among psychiatrists.National Institute of Mental Health (U.S.) (Grant R01MH093359)National Institute of Mental Health (U.S.) (Grant P30 MH090333)National Institute of Mental Health (U.S.) (Grant R01MH087488)Agency for Healthcare Research and Quality (Grant R01HS017695)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research

How Quickly Do Physicians Adopt New Drugs? The Case of Second-Generation Antipsychotics

Objective The authors examined physician adoption of second-generation antipsychotic medications and identified physician-level factors associated with early adoption. Methods The authors estimated Cox proportional-hazards models of time to adoption of nine second-generation antipsychotics by 30,369 physicians who prescribed antipsychotics between 1996 and 2008, when the drugs were first introduced, and analyzed the total number of agents prescribed during that time. The models were adjusted for physicians’ specialty, demographic characteristics, education and training, practice setting, and prescribing volume. Data were from IMS Xponent, which captures over 70% of all prescriptions filled in the United States, and the American Medical Association Physician Masterfile. Results On average, physicians waited two or more years before prescribing new second-generation antipsychotics, but there was substantial heterogeneity across products in time to adoption. General practitioners were much slower than psychiatrists to adopt second-generation antipsychotics (hazard ratios (HRs) range .10−.35), and solo practitioners were slower than group practitioners to adopt most products (HR range .77−.89). Physicians with the highest antipsychotic-prescribing volume adopted second-generation antipsychotics much faster than physicians with the lowest volume (HR range .15−.39). Psychiatrists tended to prescribe a broader set of antipsychotics (median=6) than general practitioners and neurologists (median=2) and pediatricians (median=1). Conclusions As policy makers search for ways to control rapid health spending growth, understanding the factors that influence physician adoption of new medications will be crucial in the efforts to maximize the value of care received by individuals with mental disorders as well as to improve medication safety.National Institute of Mental Health (U.S.) (R01 MH093359)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research)Agency for Healthcare Research and Quality (R01HS017695)National Institute of Mental Health (U.S.) ((NIMH) R34 MH082682)National Institute of Mental Health (U.S.) ((NIMH) P30 MH090333)National Institute of Mental Health (U.S.) ((NIMH) R01 MH087488)National Science Foundation (U.S.) (0915674