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Continuous-time multistate models are widely used for analyzing interval-censored data on disease progression over time. Sometimes, diseases manifest differently and what appears to be a coherent collection of symptoms is the expression of multiple distinct disease subtypes. To address this complexity, we propose a mixture hidden Markov model, where the observation process encompasses states representing common symptomatic stages across these diseases, and each underlying process corresponds to a distinct disease subtype. Our method models both the overall and the type-specific disease incidence/prevalence accounting for sampling conditions and exactly observed death times. Additionally, it can utilize partially available disease-type information, which offers insights into the pathway through specific hidden states in the disease process, to aid in the estimation. We present both a frequentist and a Bayesian way to obtain the estimates. The finite sample performance is evaluated through simulation studies. We demonstrate our method using the Nun Study and model the development and progression of dementia, encompassing both Alzheimer's disease (AD) and non-AD dementia., Comment: 27 pages, 4 figures, 6 tables

The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes (DTRs), which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, DTR estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the weighted proportional odds model (WPOM): a regression-based, approximate doubly-robust approach to single-stage DTR estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of WPOM with our adjusted weights via simulation studies. We further extend WPOM to multi-stage DTR estimation with household interference, namely dWPOM (dynamic WPOM). Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.

In precision medicine, Dynamic Treatment Regimes (DTRs) are treatment protocols that adapt over time in response to a patient's observed characteristics. A DTR is a set of decision functions that takes an individual patient's information as arguments and outputs an action to be taken. Building on observed data, the aim is to identify the DTR that optimizes expected patient outcomes. Multiple methods have been proposed for optimal DTR estimation with continuous outcomes. However, optimal DTR estimation with binary outcomes is more complicated and has received comparatively little attention. Solving a system of weighted generalized estimating equations, we propose a new balancing weight criterion to overcome the misspecification of generalized linear models' nuisance components. We construct binary pseudo-outcomes, and develop a doubly-robust and easy-to-use method to estimate an optimal DTR with binary outcomes. We also outline the underlying theory, which relies on the balancing property of the weights; provide simulation studies that verify the double-robustness of our method; and illustrate the method in studying the effects of e-cigarette usage on smoking cessation, using observational data from the Population Assessment of Tobacco and Health (PATH) study.

The R software has become popular among researchers due to its flexibility and open-source nature. However, researchers in the fields of public health and epidemiological studies are more customary to commercial statistical softwares such as SAS, SPSS and Stata. This paper provides a comprehensive comparison on analysis of health survey data using the R survey package, SAS, SPSS and Stata. We describe detailed R codes and procedures for other software packages on commonly encountered statistical analyses, such as estimation of population means and regression analysis, using datasets from the Canadian Longitudinal Study on Aging (CLSA). It is hoped that the paper stimulates interest among health science researchers to carry data analysis using R and also serves as a cookbook for statistical analysis using different software packages., Comment: There is a data usage issue with the paper and it is requested by the data owner (CLSA) to withdraw the paper at this time

In this paper, we propose new nonparametric approach to network inference that may be viewed as a fusion of block sampling procedures for temporally and spatially dependent processes with the classical network methodology. We develop estimation and uncertainty quantification procedures for network mean degree using a "patchwork" sample and nonparametric bootstrap, under the assumption of unknown degree distribution. We investigate asymptotic properties of the proposed patchwork bootstrap procedure and present cross-validation methodology for selecting an optimal patch size. We validate the new patchwork bootstrap on simulated networks with short and long tailed mean degree distributions, and revisit the Erdos collaboration data to illustrate the proposed methodology., Comment: The paper has been withdrawn by the authors: a general revision of methodology is needed

Discussion of Conditional growth charts [math.ST/0702634], Comment: Published at http://dx.doi.org/10.1214/009053606000000650 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

This longitudinal study focused on predicting student outcomes through multiple test scores and vocational preferences using standardized instruments and self-reports of career plans. A total of 444 students in the class of 1986 were enrolled in either a non-vocational or vocational curriculum at one of 4 high schools in a small, rural, working-class school district during the 4 academic years between 1982-83 and 1985-86 and completed from 1 to 4 standardized tests as part of the district's comprehensive testing program. Three hundred and fourteen of these students actually graduated in 1986; these graduates were all administered the Differential Aptitude Test, Ohio Vocational Interest Survey, California Achievement Test, and Stanford Achievement Test; and they were mailed a follow-up questionnaire. Using the stepwise method of discriminant analysis to predict group membership, classification efficiency ranged from 61% to 74%, indicating that a significant portion of the class might have benefited from an alternative curriculum. Career choice and actual plans were more likely influenced by socioeconomic status, rural location, and expressed vocational preference than by actual test scores. Results suggest that some of the non-vocational students might have been more successful in a vocational curriculum. School counselors must help students achieve more congruence between test scores, expressed vocational choice, and their mitigating environmental circumstances. Four data tables and a 23-item list of references are included. (Author/TJH)