Statistics and Probability Commons^™

Reversal In Declining Trend Of Adult Mortality In Many States Of India, 1970-2001: Is It Due To Aids?, Abhaya Indrayan, Ajay Kumar Bansal

COBRA Preprint Series

Objectives: To investigate the reversal in adult mortality trend from declining to rising in some segments of population in India, and to use an indirect demographic method to examine if this increase could be due to AIDS mortality. Also, to estimate the total excess deaths.

Design: Cross-sectional data on age-specific death rate in 5-year age-intervals from 25 to 44 years for the years 1970 to 1998 for rural/urban and male/female segments for each of 16 major states of India obtained from the government reports, and their projections till the year 2001.

Methods: In view of reversal of trend in some …

Optimal Cutpoint Estimation With Censored Data, Mithat Gonen, Camelia Sima

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

We consider the problem of selecting an optimal cutpoint for a continuous marker when the outcome of interest is subject to right censoring. Maximal chi square methods and receiver operating characteristic (ROC) curves-based methods are commonly-used when the outcome is binary. In this article we show that selecting the cutpoint that maximizes the concordance, a metric similar to the area under an ROC curve, is equivalent to maximizing the Youden index, a popular criterion when the ROC curve is used to choose a threshold. We use this as a basis for proposing maximal concordance as a metric to use with …

A New Class Of Rank Tests For Interval-Censored Data, Guadalupe Gomez, Ramon Oller Pique

Harvard University Biostatistics Working Paper Series

No abstract provided.

The Highest Confidence Density Region And Its Usage For Inferences About The Survival Function With Censored Data, Lu Tian, Rui Wang, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Change-Point Problem And Regression: An Annotated Bibliography, Ahmad Khodadadi, Masoud Asgharian

COBRA Preprint Series

The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as "the change-point problem" or, in the Eastern literature, as "disorder".

The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis.

Numerous methodological approaches …

A Simple Index Of Smoking, Abhaya Indrayan Dr., Rajeev Kumar Mr., Shridhar Dwivedi Dr.

COBRA Preprint Series

Background: Cigarette smoking is implicated in a large number of diseases and other adverse health conditions. Among the dimensions of smoking are number of cigarettes smoked per day, duration of smoking, passive smoking, smoking of filter cigarettes, age at start, and duration elapsed since quitting by ex-smokers. The practice so far is to study most of these separately. We develop a simple index that integrates these dimensions of smoking into a single metric, and suggest that this index be developed further. Method: The index is developed under a series of natural assumptions. Broadly, these are (i) the burden of smoking …

The Strength Of Statistical Evidence For Composite Hypotheses With An Application To Multiple Comparisons, David R. Bickel

COBRA Preprint Series

The strength of the statistical evidence in a sample of data that favors one composite hypothesis over another may be quantified by the likelihood ratio using the parameter value consistent with each hypothesis that maximizes the likelihood function. Unlike the p-value and the Bayes factor, this measure of evidence is coherent in the sense that it cannot support a hypothesis over any hypothesis that it entails. Further, when comparing the hypothesis that the parameter lies outside a non-trivial interval to the hypotheses that it lies within the interval, the proposed measure of evidence almost always asymptotically favors the correct hypothesis …

Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Multilevel Latent Class Models With Dirichlet Mixing Distribution, Chongzhi Di, Karen Bandeen-Roche

Johns Hopkins University, Dept. of Biostatistics Working Papers

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when …

Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Functional Random Effects Model For Flexible Assessment Of Susceptibility In Longitudinal Designs, Brent A. Coull

Harvard University Biostatistics Working Paper Series

No abstract provided.

Generalized Multilevel Functional Regression, Ciprian M. Crainiceanu, Ana-Maria Staicu, Chongzhi Di

Johns Hopkins University, Dept. of Biostatistics Working Papers

We introduce Generalized Multilevel Functional Linear Models (GMFLM), a novel statistical framework motivated by and applied to the Sleep Heart Health Study (SHHS), the largest community cohort study of sleep. The primary goal of SHHS is to study the association between sleep disrupted breathing (SDB) and adverse health effects. An exposure of primary interest is the sleep electroencephalogram (EEG), which was observed for thousands of individuals at two visits, roughly 5 years apart. This unique study design led to the development of models where the outcome, e.g. hypertension, is in an exponential family and the exposure, e.g. sleep EEG, is …

Limitations Of Remotely-Sensed Aerosol As A Spatial Proxy For Fine Particulate Matter, Christopher J. Paciorek, Yang Liu

Harvard University Biostatistics Working Paper Series

Recent research highlights the promise of remotely-sensed aerosol optical depth (AOD) as a proxy for ground-level PM2.5. Particular interest lies in the information on spatial heterogeneity potentially provided by AOD, with important application to estimating and monitoring pollution exposure for public health purposes. Given the temporal and spatio-temporal correlations reported between AOD and PM2.5 , it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM2.5 . Here we find only limited spatial associations of AOD from three satellite retrievals with PM2.5 over the eastern U.S. at the daily and yearly levels in 2004. We then …

Expanded Technical Report: Mapping Ancient Forests: Bayesian Inference For Spatio-Temporal Trends In Forest Composition Using The Fossil Pollen Proxy Record, Christopher J. Paciorek, Jason S. Mclachlan

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Note On Risk Prediction For Case-Control Studies, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We introduce a new method for prediction in case-control study designs, which is a simple extension of the work by van der Laan (2008). Case-control samples are biased since the proportion of cases in the sample is not the same as the population of interest. The case-control weighting for prediction proposed in this paper relies on knowledge of the true incidence probability P(Y=1) to eliminate the bias of the sampling design. In many practical settings, case-control weighting will outperform an existing method for prediction, intercept adjustment.

Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Measurement Error Caused By Spatial Misalignment In Environmental Epidemiology, Alexandros Gryparis, Christopher J. Paciorek, Ariana Zeka, Joel Schwartz, Brent A. Coull

Harvard University Biostatistics Working Paper Series

No abstract provided.

Practical Large-Scale Spatio-Temporal Modeling Of Particulate Matter Concentrations, Christopher J. Paciorek, Jeff D. Yanosky, Robin C. Puett, Francine Laden, Helen H. Suh

Harvard University Biostatistics Working Paper Series

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988-2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of PM10 …

Confidence Intervals For Negative Binomial Random Variables Of High Dispersion, David Shilane, Alan E. Hubbard, S N. Evans

U.C. Berkeley Division of Biostatistics Working Paper Series

This paper considers the problem of constructing confidence intervals for the mean of a Negative Binomial random variable based upon sampled data. When the sample size is large, we traditionally rely upon a Normal distribution approximation to construct these intervals. However, we demonstrate that the sample mean of highly dispersed Negative Binomials exhibits a slow convergence to the Normal in distribution as a function of the sample size. As a result, standard techniques (such as the Normal approximation and bootstrap) that construct confidence intervals for the mean will typically be too narrow and significantly undercover in the case of high …

A New Method For Constructing Exact Tests Without Making Any Assumptions, Karl H. Schlag

COBRA Preprint Series

We present a new method for constructing exact distribution-free tests (and con…fidence intervals) for variables that can generate more than two possible outcomes. This method separates the search for an exact test from the goal to create a non- randomized test. Randomization is used to extend any exact test relating to means of variables with fi…nitely many outcomes to variables with outcomes belonging to a given bounded set. Tests in terms of variance and covariance are reduced to tests relating to means. Randomness is then eliminated in a separate step. This method is used to create con…fidence intervals for the …

Trading Bias For Precision: Decision Theory For Intervals And Sets, Kenneth M. Rice, Thomas Lumley, Adam A. Szpiro

UW Biostatistics Working Paper Series

Interval- and set-valued decisions are an essential part of statistical inference. Despite this, the justification behind them is often unclear, leading in practice to a great deal of confusion about exactly what is being presented. In this paper we review and attempt to unify several competing methods of interval-construction, within a formal decision-theoretic framework. The result is a new emphasis on interval-estimation as a distinct goal, and not as an afterthought to point estimation. We also see that representing intervals as trade-offs between measures of precision and bias unifies many existing approaches -- as well as suggesting interpretable criteria to …

Using Longitudinal Data To Estimate The Effect Of Starting To Exercise On The Health Of Sedentary Older Adults, Paula Diehr, Calvin Hirsch

UW Biostatistics Working Paper Series

Background It is difficult to estimate the effect of exercise on future health from observational data because exercising may be both a cause and an effect of health status. Unadjusted analyses suffer from selection bias (healthier persons more likely to exercise), while adjusted analyses may adjust away some of the benefits of exercise.

Objective To obtain a "low-bias" interpretable estimate of the effect of exercise on future health.

Methods We used data from the Cardiovascular Health Study, a longitudinal study of 5,888 older adults. The number of blocks walked in the previous week, collected annually, were classified as Sedentary (less …

Estimation For Arbitrary Functionals Of Survival, Kyle Rudser, Michael L. Leblanc, Scott S. Emerson

UW Biostatistics Working Paper Series

No abstract provided.

"%Qls Sas Macro: A Sas Macro For Analysis Of Longitudinal Data Using Quasi-Least Squares"., Hanjoo Kim, Justine Shults

UPenn Biostatistics Working Papers

Quasi-least squares (QLS) is an alternative computational approach for estimation of the correlation parameter in the framework of generalized estimating equations (GEE). QLS overcomes some limitations of GEE that were discussed in Crowder (Biometrika 82 (1995) 407-410). In addition, it allows for easier implementation of some correlation structures that are not available for GEE. We describe a user written SAS macro called %QLS, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. %QLS also computes the lower and upper boundaries of the correlation parameter for analysis of …

Joint Spatial Modeling Of Recurrent Infection And Growth With Processes Under Intermittent Observation, Farouk S. Nathoo

COBRA Preprint Series

In this article we present new statistical methodology for longitudinal studies in forestry where trees are subject to recurrent infection and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations which poses computational difficulties for spatiotemporal modeling. A joint model for infection and growth is developed; wherein, a …

Why Match? Investigating Matched Case-Control Study Designs With Causal Effect Estimation, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Matched case-control study designs are commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case-control studies is a gain in efficiency. Methods for analyzing matched case-control studies have focused on utilizing conditional logistic regression models that provide conditional and not causal estimates of the odds ratio. This article investigates the use of case-control weighted targeted maximum likelihood estimation to obtain marginal causal effects in matched case-control study designs. We compare the use of case-control weighted targeted maximum likelihood estimation in matched and unmatched designs in an effort …

A Novel And Simple Rule Of Thumb For Multiplicity Control In Equivalence Testing Using Two One-Sided Tests, Carolyn Lauzon, Brian S. Caffo

Johns Hopkins University, Dept. of Biostatistics Working Papers

Equivalence testing is growing in use in scientific research outside of its traditional role in the drug approval process. Largely due to its ease of use and recommendation from the United States Food and Drug Administration guidance, the most common statistical method for testing (bio)equivalence is the two one-sided tests procedure (TOST). Like classical point-null hypothesis testing, TOST is subject to multiplicity concerns as more comparisons are made. In this manuscript, a condition that bounds the family-wise error rate (FWER) using TOST is given. This condition then leads to a simple solution for controlling the FWER. Specifically, we demonstrate that …

Fdr Controlling Procedure For Multi-Stage Analyses, Catherine Tuglus, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Multiple testing has become an integral component in genomic analyses involving microarray experiments where large number of hypotheses are tested simultaneously. However before applying more computationally intensive methods, it is often desirable to complete an initial truncation of the variable set using a simpler and faster supervised method such as univariate regression. Once such a truncation is completed, multiple testing methods applied to any subsequent analysis no longer control the appropriate Type I error rates. Here we propose a modified marginal Benjamini \& Hochberg step-up FDR controlling procedure for multi-stage analyses (FDR-MSA), which correctly controls Type I error in terms …

Estimating The Causal Effect Of Lower Tidal Volume Ventilation On Survival In Patients With Acute Lung Injury, Weiwei Wang, Daniel Scharfstein, Roy Brower, Dale Needham

Johns Hopkins University, Dept. of Biostatistics Working Papers

Acute lung injury (ALI) is a condition characterized by acute onset of severe hypoxemia and bliateral pulmonary infiltrates. ALI patients typically require mechanical ventilation in an intensive care unit. Low tidal volume ventilation (LTVV), a time-varying dynamic treatment regime, has been recommended as an effective ventilation strategy. This recommendation was based on the results of the ARMA study, a randomized clinical trial designed to compare low vs. high tidal volume strategies (ARDSNetwork, 2000) . After publication of the trial, some critics focused on the high non-adherence rates in the LTVV arm suggesting that non-adherence occurred because treating physicians felt that …

The Calculation Of The 97.5% Upper Confidence Bound: Application To Clustered Binary Data In A Binomial Non-Inferiority Two-Sample Trial., William F. Mccarthy

COBRA Preprint Series

This paper will discuss the analysis of a cluster randomized binomial non-inferiority two-sample trial. The determination of the intra-cluster correlation coefficient (ICC) and its use in the calculation of the 97.5% upper confidence bound for delta, the true difference in binomial proportions between the active control and the experimental treatment groups, will be outlined.