Population-level intervention and information collection in dynamic healthcare policy.

We develop a general framework for optimal health policy design in a dynamic setting. We consider a hypothetical medical intervention for a cohort of patients where one parameter varies across cohorts with imperfectly observable linear dynamics. We seek to identify the optimal time to change the current health intervention policy and the optimal time to collect decision-relevant information. We formulate this problem as a discrete-time, infinite-horizon Markov decision process and we establish structural properties in terms of first and second-order monotonicity. We demonstrate that it is generally optimal to delay information acquisition until an effect on decisions is sufficiently likely. We apply this framework to the evaluation of hepatitis C virus (HCV) screening in the general population determining which birth cohorts to screen for HCV and when to collect information about HCV prevalence.

Estimation of self-exciting point processes from time-censored data.

Self-exciting point processes, widely used to model arrival phenomena in nature and society, are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded only as bin counts on a finite partition of the observation interval. In this paper, we propose the recursive identification with sample correction (RISC) algorithm for the estimation of process parameters from time-censored data. In every iteration, a synthetic sample path is generated and corrected to match the observed bin counts. Then the process parameters update and a unique iteration is performed to successively approximate the stochastic characteristics of the observed process. In terms of finite-sample approximation error, the proposed RISC framework performs favorably over extant methods, as well as compared to a naïve locally uniform sample redistribution. The results of an extensive numerical experiment indicate that the reconstruction of an intrabin history based on the conditional intensity of the process is crucial for attaining superior performance in terms of estimation error.

Robust identification of controlled Hawkes processes.

The identification of Hawkes-like processes can pose significant challenges. Despite substantial amounts of data, standard estimation methods show significant bias or fail to converge. To overcome these issues, we propose an alternative approach based on an expectation-maximization algorithm, which instrumentalizes the internal branching structure of the process, thus improving convergence behavior. Furthermore, we show that our method provides a tight lower bound for maximum-likelihood estimates. The approach is discussed in the context of a practical application, namely the collection of outstanding unsecured consumer debt.

Optimal Information Collection Policies in a Markov Decision Process Framework.

BACKGROUND: The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change over time may be optimal. METHODS: We present a stochastic dynamic programming approach to simultaneously identify the optimal intervention and information collection policies. We use our framework to evaluate birth cohort hepatitis C virus (HCV) screening. We focus on how the presence of a time-varying parameter (HCV prevalence) affects the optimal information collection policy for a parameter assumed constant across birth cohorts: liver fibrosis stage distribution for screen-detected diagnosis at age 50. RESULTS: We prove that it may be optimal to delay information collection until a time when the information more immediately affects decision making. For the example of HCV screening, given initial beliefs, the optimal policy (at 2010) was to continue screening and collect information about the distribution of liver fibrosis at screen-detected diagnosis in 12 years, increasing the expected incremental net monetary benefit (INMB) by $169.5 million compared to current guidelines. CONCLUSIONS: The option to delay information collection until the information is sufficiently likely to influence decisions can increase efficiency. A dynamic programming framework enables an assessment of the marginal value of information and determines the optimal policy, including when and how much information to collect.