Search PubMed⌕ Search

Biomedical subjects

J F Lawless

Publications and source records attributed to J F Lawless.

11 recordsLinked to original sources
PubMed1996

Robust tests for treatment comparisons based on recurrent event responses.

Robust nonparametric tests are considered for use in longitudinal studies in which the response of interest is a recurrent event. The tests are robust in the sense that they do not rely on distributional assumptions regarding the processes generating the events. The methods we describe are presented in the context of a clinical trial with attention initially directed at the two-sample problem in which a single experimental treatment is compared to a control. We investigate a family of generalized pseudo-score statistics (Lawless and Nadeau, 1995, Technometrics 37, 158-168) in which weight functions may be chosen to generate tests sensitive to various types of departure from the null hypothesis that the mean functions for the treatment and control groups are identical. All tests we consider are evaluated by simulation with respect to the type I error rate and power under a variety of practical scenarios. An application involving data from a kidney transplant study illustrates these procedures. For trials with multiple treatment arms, we generalize these approaches and indicate test statistics appropriate for unstructured alternatives and tests based on linear contrasts of the treatment-specific mean functions. Extensions of this methodology for stratified designs are also indicated.

Antibodies↗
PubMed1996

Interim monitoring of longitudinal comparative studies with recurrent event responses.

A method of interim monitoring is described for longitudinal comparative studies in which the outcome of interest is a recurrent event and treatment comparisons are based on expected numbers of events. The nonparametric methods described by Cook, Lawless, and Nadeau (1996, Biometrics 52, 116-130) are generalized to provide a robust estimate of the covariance matrix for a sequence of test statistics calculated over time. The error spending function methodology of Lan and DeMets (1983, Biometrika 70, 659-663) is adopted to control the experimental type I error rate. A simulation study indicates satisfactory frequency properties of this procedure for the moderate to large scale trials for which it is intended. Extensions of this approach to handle stratified designs and studies with multitype recurrent events are indicated. Data from a kidney transplant study (Cole et al., 1994, Transplantation 57, 60-67) are used for illustrative purposes.

Antilymphocyte Serum↗
PubMed1994

Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease.

Multi-state Markov models can be useful in analysing disease history data. We apply the general estimation methods of Kalbfleisch and Lawless to panel data in which individuals are viewed over only a portion of their life history and complete information about transition times between states is unavailable. Methods to assess goodness-of-fit are proposed. To illustrate the methods, we consider models of HIV disease relating important immunological marker measurements to the onset of AIDS.

Acquired Immunodeficiency Syndrome↗
PubMed1993

Modelling overdispersion in toxicological mortality data grouped over time.

Toxicologists frequently conduct toxicity experiments in which different treatment conditions are applied to groups of animals and the resulting mortality in each group is measured at a number of discrete time points over the course of the experiment. Both survival analysis and generalized linear models have been proposed for analyzing this type of data. Whatever the approach taken, the model should allow for the presence of extra-multinomial variation arising from the use of groups of animals rather than individuals as the experimental units. We consider a number of models for overdispersion that can be incorporated into the generalized linear model framework for multinomial data. These models are extensions of ones proposed for binomial data by Williams (1982, Applied Statistics 31, 144-148) and Moore (1986, Biometrika 73, 583-588; 1987, Applied Statistics 36, 8-14). In addition, we examine robust asymptotic covariance matrix estimators for regression parameters, similar to those given in Liang and Zeger (1986, Biometrika 73, 13-22) and Zeger and Liang (1986, Biometrics 42, 121-130), and compare them to the model-based asymptotic estimators. Recommendations for analysis are given.

Analysis of Variance↗
PubMed1989

Estimating the incubation time distribution and expected number of cases of transfusion-associated acquired immune deficiency syndrome.

The number of cases of transfusion-associated acquired immune deficiency syndrome (TA-AIDS) that will be seen over the next few years is difficult to estimate, because of the uncertainty about the number of persons infected with the human immunodeficiency virus (HIV) via blood transfusion and about the duration of the incubation period from HIV infection via transfusion to diagnosis of AIDS. Presented here are a mathematical model and nonparametric and parametric statistical analyses of recent data on TA-AIDS that indicate clearly the existing estimability problems. The methods provide short-term projections of new TA-AIDS cases to be reported; the results suggest about 1100 new cases to be reported in the United States between July 1988 and June 1989 and about 1500 more between July 1989 and June 1990. Estimates of the number of eventual TA-AIDS cases to be seen are considerably more uncertain and require additional assumptions about the incubation distribution. Under the assumption that the probability of an infected person developing AIDS within 8 years of infection is 0.40 (an estimation derived from cohort studies in homosexual men and hemophiliacs), parametric and nonparametric analyses give, respectively, point estimates of 14,300 and 15,000 for the number of eventual cases of AIDS (in the age group 13-69) attributable to infection by blood transfusion prior to July 1985. The parametric analysis gives a corresponding 95 percent confidence interval.

Acquired Immunodeficiency Syndrome↗
PubMed1988

Regression and recursive partition strategies in the analysis of medical survival data.

Regression and clustering methods have both been used to explore the effects of explanatory variables on survival times for patients with cancer or other chronic diseases. This paper discusses effective and computationally feasible approaches for this task in situations where there are fairly large and complex data sets; the techniques stressed are all-subsets regression and a kind of recursive partition clustering. We compare the two approaches in a rather general way, in part by examining some survival data for patients with ovarian carcinoma, and conclude that both have strong points to recommend them.

Female↗
PubMed1987

ISMOD: an all-subsets regression program for generalized linear models. I. Statistical and computational background.

This paper describes a system written to carry out regression analyses under certain generalized linear models that are widely used in biomedical research. These include continuous response models such as the Weibull, log-logistic, log-normal and Cox proportional hazards models used in survival analysis, and also discrete Poisson, binomial and multinomial response regression models. The system fits models, generates residuals and other diagnostic output, and has an all-subsets regression feature. This paper describes the models implemented and gives statistical background; Part II describes the ISMOD system and presents examples of its application.

Biometry↗
PubMed1987

ISMOD: an all-subsets regression program for generalized linear models. II. Program guide and examples.

This paper describes a system written to carry out regression analyses under certain generalized linear models that are widely used in biomedical research. These include continuous response models such as the Weibull, log logistic, log normal and Cox proportional hazards models used in survival analysis, and also discrete Poisson, binomial and multinomial response regression models. The system fits models, generates residuals and other diagnostic output, and also has an all-subsets regression feature. This paper describes the ISMOD system and presents examples of its application; Part I describes the models implemented and gives statistical background.

Biometry↗
PubMed1983

Estimation in Markov models from aggregate data.

In this paper, situations in which individuals move through a finite set of states according to a continuous-time Markov process are considered. Only aggregate data are available: these consist of the number of individuals in each state at specified observation times. We develop conditional least squares and approximate maximum-likelihood-estimation procedures for time-homogeneous models, and extend the methods so that they can handle immigration of individuals into the system during observation. Asymptotic covariance estimates are presented, and some problems for future study are noted.

Animals↗
PubMedDate not supplied

Likelihood analysis of multi-state models for disease incidence and mortality.

Data related to life histories of individuals can be obtained in many different ways, and the usefulness of multi-state models for statistical analysis is generally highly dependent on the type and nature of the data. In this paper, we focus on this, and present an approach to estimation for certain 'difficult' situations associated with retrospective or incomplete prospective observation. The paper begins with the identification of some problem areas in the analysis of data on life history processes. We discuss maximum likelihood estimation in some simple contexts and introduce a pseudo-likelihood which enables the simple analysis of some sampling procedures. This approach is illustrated on standard retrospective and case-cohort designs.

Epidemiologic Methods↗