Showing papers in "Biometrics in 1994"
TL;DR: In this paper, an adjusted rank correlation test is proposed as a technique for identifying publication bias in a meta-analysis, and its operating characteristics are evaluated via simulations, and the test statistic is a direct statistical analogue of the popular funnel-graph.
Abstract: An adjusted rank correlation test is proposed as a technique for identifying publication bias in a meta-analysis, and its operating characteristics are evaluated via simulations. The test statistic is a direct statistical analogue of the popular "funnel-graph." The number of component studies in the meta-analysis, the nature of the selection mechanism, the range of variances of the effect size estimates, and the true underlying effect size are all observed to be influential in determining the power of the test. The test is fairly powerful for large meta-analyses with 75 component studies, but has only moderate power for meta-analyses with 25 component studies. However, in many of the configurations in which there is low power, there is also relatively little bias in the summary effect size estimate. Nonetheless, the test must be interpreted with caution in small meta-analyses. In particular, bias cannot be ruled out if the test is not significant. The proposed technique has potential utility as an exploratory tool for meta-analysts, as a formal procedure to complement the funnel-graph.
13,373 citations
TL;DR: This chapter discusses how to make practical use of spatial statistics in day-to-day analytical work, and some examples from the scientific literature suggest a straightforward and efficient way to do this.
Abstract: Spatial statistics — analyzing spatial data through statistical models — has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work is very difficult.
2,238 citations
TL;DR: Resampling-Based Adjustments: Basic Concepts and Practical Applications.
Abstract: Resampling-Based Adjustments: Basic Concepts. Continuous Data Applications: Univariate Analysis. Continuous Data Applications: Multivariate Analysis. Binary Data Applications. Further Topics. Practical Applications. Appendices. References. List of Algorithms. List of Examples. Indexes.
2,098 citations
1,663 citations
1,331 citations
TL;DR: In this paper, the Cox Regression Model is used to model survival data and the GLIM macros for survival analysis are used to calculate the maximum likelihood estimation score statistics and information.
Abstract: Some non-parametric procedures. Modelling survival data. The Cox Regression Model. Design of clinical trials. Some other models for survival data. Model checking. Time dependent co-variates. Interval censored survival data. Multi-state survival models. Some additional topics. Use of computer software in survival analysis. Appendices: Example data sets. Maximum liklihood estimation score statistics and information. GLIM macros for survival analysis.
1,067 citations
TL;DR: This article discusses the asymptotic behavior of likelihood ratio tests for nonzero variance components in the longitudinal mixed effects linear model described by Laird and Ware (1982, Biometrics 38, 963-974).
Abstract: This article discusses the asymptotic behavior of likelihood ratio tests for nonzero variance components in the longitudinal mixed effects linear model described by Laird and Ware (1982, Biometrics 38, 963-974). Our discussion of the large-sample behavior of likelihood ratio tests for nonzero variance components is based on the results for nonstandard testing situations by Self and Liang (1987, Journal of the American Statistical Association 82, 605-610).
972 citations
TL;DR: A general method for statistical testing in experiments with an adaptive interim analysis based on the observed error probabilities from the disjoint subsamples before and after the interim analysis, and rules for assessing the sample size in the second stage of the trial are given.
Abstract: SUMMARY A general method for statistical testing in experiments with an adaptive interim analysis is proposed. The method is based on the observed error probabilities from the disjoint subsamples before and after the interim analysis. Formally, an intersection of individual null hypotheses is tested by combining the twop-values into a global test statistic. Stopping rules for Fisher's product criterion in terms of critical limits for the p-value in the first subsample are introduced, including early stopping in the case of missing effects. The control of qualitative treatment-stage interactions is considered. A generalization to three stages is outlined. The loss of power when using the product criterion instead of the optimal classical test on the whole sample is calculated for the test of the mean of a normal distribution, depending on increasing proportions of the first subsample in relation to the total sample size. An upper bound on the loss of power due to early stopping is derived. A general example is presented and rules for assessing the sample size in the second stage of the trial are given. The problems of interpretation and precautions to be taken for applications are discussed. Finally, the sources of bias for estimation in such designs are described.
765 citations
TL;DR: A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data and a maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of random effects.
Abstract: A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous response that follows a linear regression model incorporating random effects. A maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of random effects. An analysis of a dataset where students are clustered or nested within classrooms is used to illustrate features of random-effects analysis of clustered ordinal data, while an analysis of a longitudinal dataset where psychiatric patients are repeatedly rated as to their severity is used to illustrate features of the random-effects approach for longitudinal ordinal data.
TL;DR: In this paper, the authors propose smoothing with correlated data and with parametric components and alternatives, and investigate multiple regression by additive models, including smoothing in high dimensions, using the kernel method.
Abstract: Preface Part I. Regression Smoothing: 1. Introduction 2. Basic idea of smoothing 3. Smoothing techniques Part II. The Kernel Method: 4. How close is the smooth to the true curve? 5. Choosing the smoothing parameter 6. Data sets with outliers 7. Smoothing with correlated data 8. Looking for special features (qualitative smoothing) 9. Incorporating parametric components and alternatives Part III. Smoothing in High Dimensions: 10. Investigating multiple regression by additive models Appendices References List of symbols and notation.
TL;DR: Simulation results indicate excellent agreement with corrected asymptotic estimates and appropriate test size in the case-cohort design of Prentice, and the technique is illustrated with data evaluating the efficacy of mammography screening in reducing breast cancer mortality.
Abstract: Large cohort studies of rare outcomes require extensive data collection, often for many relatively uninformative subjects. Sampling schemes have been proposed that oversample certain groups. For example, the case-cohort design of Prentice (1986, Biometrika 73, 1-11) provides an efficient method of analysis of failure time data. However, the variance estimate must explicitly correct for correlated score contributions. A simple robust variance estimator is proposed that allows for more complicated sampling mechanisms. The variance estimate uses a jackknife estimate of the variance of the individual influence function and is shown to be equivalent to a robust variance estimator proposed by Lin and Wei (1989, Journal of the American Statistical Association 84, 1074-1078) for the standard Cox model. Simulation results indicate excellent agreement with corrected asymptotic estimates and appropriate test size. The technique is illustrated with data evaluating the efficacy of mammography screening in reducing breast cancer mortality.
TL;DR: A semiparametric model for longitudinal data which is illustrated by its application to data on the time evolution of CD4 cell numbers in HIV seroconverters, finding that the onset of HIV infection is associated with a sudden drop in CD4 cells followed by a longer-term slower decay.
Abstract: The paper describes a semiparametric model for longitudinal data which is illustrated by its application to data on the time evolution of CD4 cell numbers in HIV seroconverters. The essential ingredients of the model are a parametric linear model for covariate adjustment, a nonparametric estimation of a smooth time trend, serial correlation between measurements on an individual subject, and random measurement error. A back-fitting algorithm is used in conjunction with a cross-validation prescription to fit the model. A notable feature in the application is that the onset of HIV infection is associated with a sudden drop in CD4 cells followed by a longer-term slower decay. The model is also used to estimate an individual's curve by combining his data with the population curve. Shrinkage toward the population mean trajectory is controlled in a natural way by the estimated covariance structure of the data.
TL;DR: A nonparametric estimation technique is proposed which uses the concept of sample coverage in order to estimate the size of a closed population for capture-recapture models where time, behavior, or heterogeneity may affect the capture probabilities.
Abstract: A nonparametric estimation technique is proposed which uses the concept of sample coverage in order to estimate the size of a closed population for capture-recapture models where time, behavior, or heterogeneity may affect the capture probabilities. The technique also provides a unified approach to catch-effort models that allows for heterogeneity among removal probabilities. Real data examples are given for illustration. A simulation study investigates the behavior of the proposed procedure.
TL;DR: Methods for fitting a broad class of models of this type, in which both the repeated CD4-lymphocyte counts and the survival time are modelled using random effects are proposed, are proposed and applied to results of AIDS clinical trials.
Abstract: The purpose of this article is to model the progression of CD4-lymphocyte count and the relationship between different features of this progression and survival time. The complicating factors in this analysis are that the CD4-lymphocyte count is observed only at certain fixed times and with a high degree of measurement error, and that the length of the vector of observations is determined, in part, by the length of survival. If probability of death depends on the true, unobserved CD4-lymphocyte count, then the survival process must be modelled. Wu and Carroll (1988, Biometrics 44, 175-188) proposed a random effects model for two-sample longitudinal data in the presence of informative censoring, in which the individual effects included only slopes and intercepts. We propose methods for fitting a broad class of models of this type, in which both the repeated CD4-lymphocyte counts and the survival time are modelled using random effects. These methods permit us to estimate parameters describing the progression of CD4-lymphocyte count as well as the effect of differences in the CD4 trajectory on survival. We apply these methods to results of AIDS clinical trials.
TL;DR: A class of nonparametric tests for linkage between a marker and a gene assumed to exist and to govern susceptibility to a disease is described, which does not require knowledge of the mode of disease inheritance and does not requirement unambiguous determination of identity-by-descent at the marker.
Abstract: We describe a class of nonparametric tests for linkage between a marker and a gene assumed to exist and to govern susceptibility to a disease. The tests are formed by assigning a score to each possible pattern of marker allele sharing (identity-by-descent) among affected pedigree members, and then averaging the scores over all patterns compatible with the observed marker genotype and genealogical relationship of the affected members. Different score functions give different tests. One function, which examines marker allele similarity across pairs of affected pedigree members, gives a test similar to that of Fimmers et al. (1989, in Multipoint Mapping and Linkage Based on Affected Pedigree Members: Genetic Analysis Workshop, R. C. Elston, M. A. Spence, S. E. Hodge, and J. W. MacCluer (eds), 123-128; City: Alan R. Liss). A second function examines allele similarity across arbitrary subsets, not just pairs, of affected members. The resulting test can be more powerful than the one based solely on pairs of affected members. The approach has several advantages: it does not require knowledge of the mode of disease inheritance; it does not require unambiguous determination of identity-by-descent at the marker; it does not suffer from variability due to chance allele similarity among affected members who are unrelated, such as spouses; it allows marker genotypes of unaffected members to contribute information on allele sharing among the affected; it permits calculation of exact P-values. Computational requirements limit the tests to many pedigrees with few (< 16) affected members.
TL;DR: The estimation of hazard rates under random censoring with the kernel method is discussed and a practically feasible method incorporating the new boundary kernels and local bandwidth choices is implemented and illustrated with survival data from a leukemia study.
Abstract: We discuss the estimation of hazard rates under random censoring with the kernel method. Two practically relevant problems that occur when applying unmodified kernel estimators are boundary effects near the endpoints of the support of the hazard rate, and a substantial increase in the variance from left to right over the range of abscissae where the hazard rate is estimated. A new class of boundary kernels is proposed for the first problem. Explicit formulas for these kernels are developed, and it is shown that this boundary correction works well in practice. A data-adaptive varying bandwidth selection procedure is proposed for the second problem. This procedure generally will lead to increasing bandwidths near the left endpoint and toward the right endpoint, and will lead to smaller integrated mean squared error of the hazard rate estimator as compared to a fixed bandwidth method. A practically feasible method incorporating the new boundary kernels and local bandwidth choices is implemented and illustrated with survival data from a leukemia study.
TL;DR: Assessment of Agent Monitoring Strategies for the Blue Grass and Pueblo Chemical Agent Destruction Pilot Plants and some applications of Robust Statistical Methods to Analytical Chemistry.
Abstract: Statistical Treatment of Analytical DataStatistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical IndustryStatistical Methods for Six SigmaIntroduction to Statistical Analysis of Laboratory DataOrganic Trace AnalysisChemometricsChemometricsStatistical Methods in PracticeStatistical Data AnaylsisData Analysis for Omic Sciences: Methods and ApplicationsNotes on Statistics and Data Quality for Analytical ChemistsStatistical Methods in Analytical Chemistry (Volume 123).Assessment of Agent Monitoring Strategies for the Blue Grass and Pueblo Chemical Agent Destruction Pilot PlantsApplied Chemometrics for ScientistsChemometrics in Environmental Chemistry Statistical MethodsSome Applications of Robust Statistical Methods to Analytical ChemistryAdvanced Statistical Methods for the Analysis of Large Data-SetsStatistics and Chemometrics for Analytical ChemistryStatistical Methods in Analytical ChemistryLaboratory StatisticsStatistical Methods in Analytical ChemistryStatistical Methods in Analytical ChemistryStatistical Methods in Analytical ChemistryStatistical Methods in Analytical ChemistryComputational Techniques for Analytical Chemistry and BioanalysisStatistical and Multivariate Analysis in Material ScienceMathematical Methods for Physical and Analytical ChemistryStatistics for Analytical ChemistryStatistical Techniques for Data AnalysisChemometricsStatistics and Chemometrics for Analytical ChemistryThe Analysis of Covariance and AlternativesStatistics and Chemometrics for Analytical ChemistryIntroduction to Multivariate Statistical Analysis in ChemometricsStatistics for Analytical ChemistryComputational and Statistical Methods for Protein Quantification by Mass SpectrometryChemometrics in SpectroscopyPractical Statistics for the Analytical ScientistAdvanced Excel for Scientific Data AnalysisStatistics for the Quality Control Chemistry Laboratory
TL;DR: This book is intended for graduate students in any field where statistics is used and covers major techniques including post hoc testing and discriminates between different research designs.
Abstract: This is an introductory text on the analysis of variance and also a reference tool for the researcher into variance analysis. It stresses application rather than theory, covers major techniques including post hoc testing and discriminates between different research designs. The book is intended for graduate students in any field where statistics is used.
TL;DR: In this paper, the authors present the foundations of risk theory and apply them to long-term processes in the insurance business, such as the calculation of a compound claim, the number of claims, and the amount of claims.
Abstract: Content: Foundations of risk theory. Some preliminary ideas. The number of claims. The amount of claims. Calculation of a compound claim d.f.f. Simulation. Applications involving short-term claim fluctuation. Stochastic analysis of insurance business. Inflation. Investment. Claims with an extended time horizon. Premiums. Expenses, taxes, dividends. The insurance process. Applications to long-term processes. Managing uncertainty. Life insurance. Pension schemes. Appendices. Bibliography. Subject index. Author index.
TL;DR: In this paper, a small-sample criterion (AICC) was developed for selecting multivariate regression models. But this criterion adjusts the Akaike information criterion to be an exact unbiased estimator for the expected Kullback-Leibler information, and a small sample comparison shows that AICC provides better model order choices than other available model selection methods.
Abstract: We develop a small-sample criterion (AICc) for selecting multivariate regression models. This criterion adjusts the Akaike information criterion to be an exact unbiased estimator for the expected Kullback-Leibler information. A small-sample comparison shows that AICC provides better model order choices than other available model selection methods. Data from an agricultural experiment are analyzed.
TL;DR: The purpose of this book is to review the contribution of statistical science to the understanding of the acquired immunodeficiency syndrome (AIDS) and to summarize and interpret the major epidemiological findings.
Abstract: The purpose of this book is to review the contribution of statistical science to our understanding of the acquired immunodeficiency syndrome (AIDS) and to summarize and interpret the major epidemiological findings. Chapters are included on risk factors for infection and the probability of HIV transmission surveys to determine seroprevalence and seroincidence screening and accuracy of tests for HIV statistical issues in surveillance of AIDS incidence back-calculation procedures for estimating and projecting AIDS incidence epidemic transmission models and synthesizing data sources and methods for assessing the scope of the epidemic. (EXCERPT)
TL;DR: In this article, a quick overview of statistical models commonly used in causal analyses of nonexperimental data in the social and biomedical sciences is provided, including simple bivariate regression multiple regression multiple classification analysis path analysis logit regression multinomial logit regressions and survival models.
Abstract: This book provides a quick overview of statistical models commonly used in causal analyses of nonexperimental data in the social and biomedical sciences. Topics covered are simple bivariate regression multiple regression multiple classification analysis path analysis logit regression multinomial logit regression and survival models (proportional hazard models and hazard models with time dependence). The methods described are illustrated using data from the 1974 Fiji Fertility Survey conducted as part of the World Fertility Survey. (EXCERPT)
TL;DR: This work proposes practical Bayesian guidelines for deciding whether E is promising relative to S in settings where patient response is binary and the data are monitored continuously, and provides decision boundaries, a probability distribution for the sample size at termination, and operating characteristics under fixed response probabilities with E.
Abstract: A Phase IIB clinical trial typically is a single-arm study aimed at deciding whether a new treatment E is sufficiently promising, relative to a standard therapy, S, to include in a large-scale randomized trial. Thus, Phase IIB trials are inherently comparative even though a standard therapy arm usually is not included. Uncertainty regarding the response rate theta s of S is rarely made explicit, either in planning the trial or interpreting its results. We propose practical Bayesian guidelines for deciding whether E is promising relative to S in settings where patient response is binary and the data are monitored continuously. The design requires specification of an informative prior for theta s, a targeted improvement for E, and bounds on the allowed sample size. No explicit specification of a loss function is required. Sampling continues until E is shown to be either promising or not promising relative to S with high posterior probability, or the maximum sample size is reached. The design provides decision boundaries, a probability distribution for the sample size at termination, and operating characteristics under fixed response probabilities with E.