Longitudinal Data Analysis and Time Series Commons^™

Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Computationally Tractable Multivariate Random Effects Model For Clustered Binary Data, Brent A. Coull, E. Andres Houseman, Rebecca A. Betensky

Harvard University Biostatistics Working Paper Series

No abstract provided.

Individualized Treatment Rules: Generating Candidate Clinical Trials, Maya L. Petersen, Steven G. Deeks, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Statistical methods have rarely been applied to learn individualized treatment rules, or rules for altering treatments over time in response to changes in individual covariates. Termed dynamic treatment regimes in the statistical literature, such individualized treatment rules are of primary importance in the practice of clinical medicine. History-Adjusted Marginal Structural Models (HA-MSM) estimate individualized treatment rules that assign, at each time point, the first action of the future static treatment plan that optimizes expected outcome given a patient's covariates. However, as we discuss here, the optimality of these rules can depend on the way in which treatment was assigned in …

Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh

Harvard University Biostatistics Working Paper Series

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic …

The Longitudinal Effect Of Self-Monitoring And Locus Of Control On Social Network Position In Friendship Networks, Gary J. Moore

Theses and Dissertations

The purpose of this research was to identify how enduring personality characteristics predict a person's location in a network, locations which in turn affect outcomes such as performance. Specifically, this thesis examines how self-monitoring and locus of control influence an individual's location in a friendship social network over time. Hierarchical Linear Modeling (HLM) was used to analyze 28 groups of students and instructors at a military training course over six and one half weeks. Self-monitoring predicted betweenness centrality in five of six time periods while locus of control predicted betweenness centrality in three of six time periods. The moderation of …

A Diagnostic Test For The Mixing Distribution In A Generalised Linear Mixed Model, Eric J. Tchetgen, Brent A. Coull

Harvard University Biostatistics Working Paper Series

We introduce a diagnostic test for the mixing distribution in a generalised linear mixed model. The test is based on the difference between the marginal maximum likelihood and conditional maximum likelihood estimates of a subset of the fixed effects in the model. We derive the asymptotic variance of this difference, and propose a test statistic that has a limiting chi-square distribution under the null hypothesis that the mixing distribution is correctly specified. For the important special case of the logistic regression model with random intercepts, we evaluate via simulation the power of the test in finite samples under several alternative …

On The Violation Of Bounds For The Correlation In Generalized Estimating Equation Analyses Of Binary Data From Longitudinal Trials, Justine Shults, Wenguang Sun, Xin Tu, Jay Amsterdam

UPenn Biostatistics Working Papers

It is well-known that the correlation among binary outcomes is constrained by the marginal means, yet approaches such as generalized estimating equations (GEE) do not check that the constraints for the correlations are satisfied. We explore this issue for Markovian dependence in the context of a GEE analysis of a clinical trial that compares Venlafaxine with Lithium in the prevention of major depressive episode. We obtain simplified expressions for the constraints for the logistic model and the equicorrelated and first-order autoregressive correlation structures. We then obtain the limiting values of the GEE and quasi-least squares (QLS) estimates of the correlation …

Use Of Unbiased Estimating Equations To Estimate Correlation In Generalized Estimating Equation Analysis Of Longitudinal Trials, Wenguang Sun, Justine Shults, Mary Leonard

UPenn Biostatistics Working Papers

In a recent publication, Wang and Carey (Journal of the American Statistical Association, 99, pp. 845-853, 2004) presented a new approach for estimation of the correlation parameters in the framework of generalized estimating equations (GEE). They considered correlated continuous, binary and count data with a generalized Markov correlation structure that includes the first-order autoregressive AR(1) and Markov structures as special cases. They made detailed comparisons with pseudo-likelihood (PL) and the first stage of quasi-least squares (QLS), a two-stage approach in the framework of generalized estimating equations (GEE). In this note we extend their comparisons for the second (bias corrected) stage …

Foreign-Born Population In Selected Ohio Cities, 1870 To 2000 A Brief Descriptive Report, Mark Salling, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

No abstract provided.

Using The Census Bureau's Public Use Microdata For Migration Analysis, Mark Salling, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

Using the Census Bureau's Public Use Microdata for Migration Analysis, Proceedings of the annual conference of the Urban and Regional Information Systems Association, Vancouver, BC, Canada, September 2006, pp.336-348.

Methods For The Estimation Of Missing Values In Time Series, David S. Fung

Theses: Doctorates and Masters

Time Series is a sequential set of data measured over time. Examples of time series arise in a variety of areas, ranging from engineering to economics. The analysis of time series data constitutes an important area of statistics. Since, the data are records taken through time, missing observations in time series data are very common. This occurs because an observation may not be made at a particular time owing to faulty equipment, lost records, or a mistake, which cannot be rectified until later. When one or more observations are missing it may be necessary to estimate the model and also …

Foreign Migration To The Cleveland-Akron-Lorain Metropolitan Area From 1995 To 2000, Mark Salling, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

This report is one of a series on migration to and from the region using the five percent Public Use Microdata Sample (PUMS) of the 2000 Census of Population and Housing and provides a description of foreign migrants moving to the Cleveland-Akron-Lorain (CAL) Consolidated Metropolitan Area (CMSA) from 1995 to 2000.* The report identifies the countries of origin of migrants and compares the demographic, socioeconomic, and housing characteristics of the foreign migrants to the CAL with other groups, including foreign migrants to Ohio and the nation, and, at times, to domestic migrants to and from the CAL.

Estimating A Treatment Effect With Repeated Measurements Accounting For Varying Effectiveness Duration, Ying Qing Chen, Jingrong Yang, Su-Chun Cheng

UW Biostatistics Working Paper Series

To assess treatment efficacy in clinical trials, certain clinical outcomes are repeatedly measured for same subject over time. They can be regarded as function of time. The difference in their mean functions between the treatment arms usually characterises a treatment effect. Due to the potential existence of subject-specific treatment effectiveness lag and saturation times, erosion of treatment effect in the difference may occur during the observation period of time. Instead of using ad hoc parametric or purely nonparametric time-varying coefficients in statistical modeling, we first propose to model the treatment effectiveness durations, which are the varying time intervals between the …

Marginal Regression Modeling Under Irregular, Biased Sampling, Petra Buzkova, Thomas Lumley

UW Biostatistics Working Paper Series

In longitudinal studies observations are often obtained at continuous subject-specific times. Frequently the availability of outcome data may be related to the outcome measure or other covariates that are related to the outcome measure. Under such biased sampling designs unadjusted regression analysis yield biased estimates. Building on the work of Lin & Ying (2001) that integrates counting processes techniques with longitudinal data settings we propose a class of estimators that can handle biased sampling. We call those estimators ``inverse--intensity--rate--ratio--weighted'' (IIRR) estimators. Of major focus is a mean--response model where we examine the marginal effect of the covariate X at time …

Longitudinal Data Analysis For Generalized Linear Models Under Irregular, Biased Sampling: Situations With Follow-Up Dependent On Outcome Or Auxiliary Outcome-Related Variables, Petra Buzkova, Thomas Lumley

UW Biostatistics Working Paper Series

In longitudinal studies, observations are often obtained at subject-specific observation times. Those times can be continuous times, not at a set of prespecified times. Frequently the observation times may be related to the outcome measure or other auxiliary variables that are related to the outcome measure but undesirable to condition upon in the regression model for outcome. Regression analysis unadjusted for such sampling designs yield biased estimates. Based on estimating equations, we propose a class of estimators in generalized linear regression models that can handle biased sampling under continuous observation times. We call those estimators ``inverse--intensity rate--ratio--weighted'' (IIRR) estimators. The …

Semiparametric Loglinear Regression For Longitudinal Measurements Subject To Irregular, Biased Follow-Up, Petra Buzkova, Thomas Lumley

UW Biostatistics Working Paper Series

We propose a method for analysis of loglinear regression models for longitudinal data that are subject to continuous and irregular follow-up. Frequently, if the follow-up is irregular, the availability of outcome data may be related to the outcome measure or other covariates that are related to the outcome measure. Under such biased sampling designs unadjusted regression analysis yield biased estimates. We examine the marginal association of the covariates X at time t and the logarithm of the mean of response Y at time t. We focus on semiparametric regression with unspecified baseline function of time. To predict the follow-up times …

A Nonstationary Negative Binomial Time Series With Time-Dependent Covariates: Enterococcus Counts In Boston Harbor, E. Andres Houseman, Brent Coull, James P. Shine

Harvard University Biostatistics Working Paper Series

Boston Harbor has had a history of poor water quality, including contamination by enteric pathogens. We conduct a statistical analysis of data collected by the Massachusetts Water Resources Authority (MWRA) between 1996 and 2002 to evaluate the effects of court-mandated improvements in sewage treatment. Motivated by the ineffectiveness of standard Poisson mixture models and their zero-inflated counterparts, we propose a new negative binomial model for time series of Enterococcus counts in Boston Harbor, where nonstationarity and autocorrelation are modeled using a nonparametric smooth function of time in the predictor. Without further restrictions, this function is not identifiable in the presence …

Semiparametric Estimation In General Repeated Measures Problems, Xihong Lin, Raymond J. Carroll

Harvard University Biostatistics Working Paper Series

This paper considers a wide class of semiparametric problems with a parametric part for some covariate effects and repeated evaluations of a nonparametric function. Special cases in our approach include marginal models for longitudinal/clustered data, conditional logistic regression for matched case-control studies, multivariate measurement error models, generalized linear mixed models with a semiparametric component, and many others. We propose profile-kernel and backfitting estimation methods for these problems, derive their asymptotic distributions, and show that in likelihood problems the methods are semiparametric efficient. While generally not true, with our methods profiling and backfitting are asymptotically equivalent. We also consider pseudolikelihood methods …

Sample Size And Power Calculations For Body Weight In Beef Cattle, Claudia Cristina Paro Paz, Alfredo Ribeiro De Freitas, Irineu Umberto Packer, Daniela Tambasco-Talhari, Luciana Correa De Almeida Regitano, Mauricio Mello Alencar

COBRA Preprint Series

Estimates of minimum sample sizes are calculated in order to test differences in rates of changes over time for longitudinal designs. In this study, body weight of crossbred beef cattle, considering 14 measurements on individuals, taken at birth, weaning (7 months of age) and monthly from 8 to 19 months of age, were analyzed by an usual mixed model for repeated measures. The number of individuals n required to detect significant differences (delta) between any two consecutive measurements on the individual, was obtained by a SAS program considering a t-variate normal distribution (t = 14), sample variance–covariance matrix among the …

Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen

U.C. Berkeley Division of Biostatistics Working Paper Series

The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is mediated by a given intermediate variable (the indirect effect of the treatment), and the component that is not mediated by that intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Under the assumption of no-unmeasured confounders for treatment and the intermediate variable, Robins & Greenland (1992) define an individual direct effect as the counterfactual effect of …

G-Computation Estimation Of Nonparametric Causal Effects On Time-Dependent Mean Outcomes In Longitudinal Studies, Romain Neugebauer, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Two approaches to Causal Inference based on Marginal Structural Models (MSM) have been proposed. They provide different representations of causal effects with distinct causal parameters. Initially, a parametric MSM approach to Causal Inference was developed: it relies on correct specification of a parametric MSM. Recently, a new approach based on nonparametric MSM was introduced. This later approach does not require the assumption of a correctly specified MSM and thus is more realistic if one believes that correct specification of a parametric MSM is unlikely in practice. However, this approach was described only for investigating causal effects on mean outcomes collected …

Causal Inference In Longitudinal Studies With History-Restricted Marginal Structural Models, Romain Neugebauer, Mark J. Van Der Laan, Ira B. Tager

U.C. Berkeley Division of Biostatistics Working Paper Series

Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because MSM parameters provide explicit representations of causal effects. We introduce History-Restricted Marginal Structural Models (HRMSMs) for longitudinal data for the purpose of defining causal parameters which may often be better suited for Public Health research. This new class of MSMs allows investigators to analyze the causal effect of a treatment on an outcome based on a fixed, shorter and user-specified history of exposure compared to MSMs. By default, the latter represents the treatment causal effect of interest based on a treatment history defined by the …

Statistical Analysis Of Longitudinal And Multivariate Discrete Data, Deepak Mav

Mathematics & Statistics Theses & Dissertations

Correlated multivariate Poisson and binary variables occur naturally in medical, biological and epidemiological longitudinal studies. Modeling and simulating such variables is difficult because the correlations are restricted by the marginal means via Fréchet bounds in a complicated way. In this dissertation we will first discuss partially specified models and methods for estimating the regression and correlation parameters. We derive the asymptotic distributions of these parameter estimates. Using simulations based on extensions of the algorithm due to Sim (1993, Journal of Statistical Computation and Simulation, 47, pp. 1–10), we study the performance of these estimates using infeasibility, coverage probabilities of the …

Social Indicators In Cleveland's Ward 17, Mark Salling, Sharon Bliss, Joseph Ahern

All Maxine Goodman Levin School of Urban Affairs Publications

No abstract provided.

A Bayesian Mixture Model Relating Dose To Critical Organs And Functional Complication In 3d Conformal Radiation Therapy, Tim Johnson, Jeremy Taylor, Randall K. Ten Haken, Avraham Eisbruch

The University of Michigan Department of Biostatistics Working Paper Series

A goal of radiation therapy is to deliver maximum dose to the target tumor while minimizing complications due to irradiation of critical organs. Technological advances in 3D conformal radiation therapy has allowed great strides in realizing this goal, however complications may still arise. Critical organs may be adjacent to tumors or in the path of the radiation beam. Several mathematical models have been proposed that describe a relationship between dose and observed functional complication, however only a few published studies have successfully fit these models to data using modern statistical methods which make efficient use of the data. One complication …

Bayesian Hierarchical Distributed Lag Models For Summer Ozone Exposure And Cardio-Respiratory Mortality, Yi Huang, Francesca Dominici, Michelle L. Bell

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994.

At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by …

Cholesky Residuals For Assessing Normal Errors In A Linear Model With Correlated Outcomes: Technical Report, E. Andres Houseman, Louise Ryan, Brent Coull

Harvard University Biostatistics Working Paper Series

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and …

Optimal Sampling Times In Bioequivalence Studies Using A Simulated Annealing Algorithm , Leena Choi, Brian Caffo, Charles Rohde

Johns Hopkins University, Dept. of Biostatistics Working Papers

In pharmacokinetic (PK) studies, blood samples are taken over time on subjects after the administration of a drug to measure the time-course of the plasma drug concentrations. In bioequivalence studies, the trapezoidal rule on the sampled time points is often used to estimate the area under the plasma concentration-time curve, a quantity of principle interest. This manuscript investigates the choice of sampling time points to estimate the area under the curve. In particular, we explore the relative merits of several objective functions, those functions which are minimized with respect to the sampling times to obtain an optimal study design. We …

On Time Series Analysis Of Public Health And Biomedical Data, Scott L. Zeger, Rafael A. Irizarry, Roger D. Peng

Johns Hopkins University, Dept. of Biostatistics Working Papers

A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department or annual expenditures on health care in the United States. Time series models are used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. Time series methods are necessary to account for the correlation among repeated responses over time. This paper gives an overview of time series ideas and methods used in public health research.