Results from using a new dyadic-dependence model to analyze sociocentric physician networks

Abstract

Professional physician networks can potentially influence clinical practices and quality of care. With the current focus on coordinated care, discerning influences of naturally occurring clusters and other forms of dependence among physicians' relationships based on their attributes and care patterns is an important area of research. In this paper, two directed physician networks: a physician influential conversation network (N = 33) and a physician network obtained from patient visit data (N = 135) are analyzed using a new model that accounts for effect modification of the within-dyad effect of reciprocity and inter-dyad effects involving three (or more) actors. The results from this model include more nuanced effects involving reciprocity and triadic dependence than under incumbent models and more flexible control for these effects in the extraction of other network phenomena, including the relationship between similarity of individuals' attributes (e.g., same-gender, same residency location) and tie-status. In both cases we find extensive evidence of clustering and triadic dependence that if not accounted for confounds the effect of reciprocity and attribute homophily. Findings from our analysis suggest alternative conclusions to those from incumbent models.

Keywords: Dyadic independence; Latent variables; Patient sharing; Physician influence; Sociocentric network; Transitivity.

Figure 1

Indegree versus outdegrees of physicians grouped by their expertise in women’s health (squares denote women health, and circles other specialties) and clinic (black=1, red=2, green=3, blue=4) in the physician influential conversation network. Women health specialists were cited more frequently (e.g. physician 27 in clinic 4 is a WH-expert and had a very high indegree of 24) than others when discussing women health issues.

Figure 2

Posterior mean estimate of physicians’ latent positions in the social space. The latent positions are estimated from an EP2 model: (a) without covariates, and (b) after adjusting for the “clinic” covariate. They gray arrows denote the interactions between the physicians in the influential conversations network.

Figure 3

Posterior predictive distributions of network characteristics from the physician influential conversations network (example 1) quantifying aspects of model-fit: the proportions of cycles among the two-paths, transitive triads among two stars and the total number of closed triplets (including cycles and transitive triads) among connected triplets (two stars or two paths) (L–R), respectively. The distributions are computed under the EP2 model (solid line) and the traditional model p₂ (dashed line). The solid dot represents the observed value of the statistic, revealing that the EP2 model much more successfully reproduces the triadic dependence in the network.

Figure 4

Posterior mean estimate of the latent positions of the physicians in the “social space” of the patient-sharing generated network. (a) The latent positions are estimated from an EP2 model after adjusting for the all covariates. The different colors denote different location of practices and gray lines denote the observed patient-sharing relationship between physicians in the generated physician network. (b) comparison of posterior predictive distribution of the overall reciprocity estimate for the reduced EP2 and full EP2 model.

Figure 5

(a–b) Posterior predictive distributions of overall triadic clustering in the physician patient-sharing generated network (example 2). The distributions are computed under our EP2 model (solid line) and the traditional p₂ model (dashed line). The solid dot represents the observed value of the statistic. (c) Posterior predictive distributions of physicians’ in-degree distribution. The gray lines are degree distributions computed under the full EP2 model. The black line denotes the observed degree distribution of the nodes in the patient-sharing generated physician network.