bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation of parameters and missing values under a regression model with non‐normally distributed and non‐randomly incomplete data
Authors: Azen Stanley P. , van Guilder Michael , Hill Mary Ann 
Published: April 2007

AbstractWe carried out a simulation study to compare the performance of three algorithms (complete cases, ALLVALUE, and expectation maximization, EM) in estimating regression parameters and missing values for situations that have varying amounts of missing data, distributions (normal, mixture of normals and lognormal), patterns of incomplete data (random, related and censored), and degrees of correlational structure among the dependent and independent variables. We found that the EM and complete cases algorithms performed equally well regardless of the correlational structure, when the percentage of incomplete data was only 5 per cent. When this percentage increased to 25 per cent, the EM algorithm was generally best for estimation, but the complete cases algorithm was safe and conservative. This finding may be attributed to the study design, which required that the slopes be the same in the population of all cases, and in the population of complete cases. In addition, the one‐step imputing method (ALLVALUE) was competitive only for situations with weak correlational structure and/or little missing data. In that situation the bias caused with use of all available information was less than that caused with use of only complete cases. On the other hand, for imputation, the EM algorithm performed optimally, even in situations of censored or log‐normally distributed data.
A model‐based approach to the imputation of missing data: Home injury incidences
Authors: Conn Judith M. , Lui Kung‐Jong , McGee Daniel L. 
Published: April 2007

AbstractMissing or incomplete data cases are a problem in all types of statistical analyses. In disease surveillance, this problem inhibits determining the actual incidence of a disease event and monitoring the disease occurrence. Several statistical techniques have been developed to impute values for incomplete data cases. We present a model‐based approach to the imputation of missing data elements as applied to determining the incidence of home injury deaths.
An analysis of disease surveillance data that uses the geographic locations of the reporting units
Authors: Raubertas Richard F. 
Published: April 2007

AbstractThe primary purpose of a disease surveillance system is to provide data for the detection of changes in the incidence of the disease. Methods for the analysis of data from surveillance systems are reviewed. A new procedure is proposed for use when the system includes geographically dispersed reporting units, such as hospitals or administrative regions. The method is based on the cusum statistic, and combines information from different units according to their geographic relationships.
Analysis of surveillance data: A rationale for statistical tests with comments on confidence intervals and statistical models
Authors: Hall David B. 
Published: April 2007

AbstractIn the examination of differences between subgroups in surveillance data, whether through simple counting or through sophisticated statistical modelling, the comparison is not between simple random samples from two or more populations. The rationale for statistical tests rests on an appeal to a model of random permutation of demographic and disease factors for the observed population during the surveillance period. The testing evaluates chance as a possible explanation for the observed results. In the analysis of internal structure in a surveillance data set, statistical tests produce a conceptually simple result that lends itself to concise presentation and flexible interpretation. Tests limit emphasis on probabilistic manipulation and on parameter estimates. They cannot stand alone, and thus encourage descriptive presentation of observations. In contrast, statistical models and confidence intervals emphasize parameters rather than distributions and compete with the data for limited space.
Cholesterol‐based personal risk assessment in coronary heart disease
Authors: Caudill Samuel P. , Smith S. Jay , Cooper Gerald R. 
Published: April 2007

AbstractUsing data from the National Health and Nutrition Examination Survey (NHANES II) 1976–1980, we demonstrate how cross‐sectional total serum cholesterol surveillance data can be used by an individual to assess current and future personal cholesterol risk status. We propose statistical models, based on a person's current measured cholesterol level and the relationship between cross‐sectional age and cholesterol percentile estimates, that will allow prediction of future cholesterol levels or the age at which specified cholesterol risk levels will be reached if no cholesterol‐altering intervention is taken. These models incorporate the observed variation in the NHANES II data and expected intraperson biological variation and intralaboratory analytical variation. We illustrate the adequacy of the models using data from the longitudinal Framingham Study.
Detection of aberrations in the occurrence of notifiable diseases surveillance data
Authors: Stroup Donna F. , Williamson G. David , Herndon Joy L. , Karon John M. 
Published: April 2007

AbstractThe detection of unusual patterns in the occurrence of diseases and other health events presents an important challenge to public health surveillance. This paper discusses three analytic methods for identifying aberrations in underlying distributions. The methods are illustrated on selected infectious diseases included in the National Notifiable Diseases Surveillance System of the Centers for Disease Control. Results suggest the utility of such an analytic approach. Further work will determine the sensitivity of such methods to variations in the occurrence of disease. These methods are useful for evaluating and monitoring public health surveillance data.
A time series analysis of gonorrhea surveillance data
Authors: Schnell Daniel , Zaidi Akbar , Reynolds Gladys 
Published: April 2007

AbstractGonorrhea is the most frequently reported communicable disease in the United States. In response to rapidly rising rates in the late 1960s, the Public Health Service instituted a gonorrhea control programme. An important component of the programme is the screening of women for gonococcal infections. We use a time series intervention model to estimate the initial increase in reporting of cases in women associated with the control programme. From the middle 1970s to the middle 1980s, a regular seasonal pattern in the data is conspicuous. We use a second time series model to quantify the seasonal variation during this period and to construct forecasts.
Surrogate endpoints in clinical trials: Cancer
Authors: Ellenberg Susan S. , Hamilton J. Michael 
Published: April 2007

AbstractInvestigators use a surrogate endpoint when the endpoint of interest is too difficult and/or expensive to measure routinely and when they can define some other, more readily measurable, endpoint, that is sufficiently well correlated with the first to justify its use as a substitute. A surrogate endpoint is usually proposed on the basis of a biologic rationale. In cancer studies with survival time as the primary endpoint, surrogate endpoints frequently employed are tumour response, time to progression, or time to reappearance of disease, since these events occur earlier and are unaffected by use of secondary therapies. In early drug development studies, tumour response is often the true primary endpoint. We discuss the investigation of the validity of carcinoembryonic antigen (a tumour marker present in the blood) as a surrogate for tumour response. In considering the validity of surrogate endpoints, one must distinguish between study endpoints that provide a basis for reliable comparisons of therapeutic effect, and clinical endpoints that are useful for patient management but have insufficient sensitivity and/or specificity to provide reproducible assessments of the effects of particular therapies.
Surrogate endpoints in clinical trials: Cardiovascular diseases
Authors: Wittes Janet , Lakatos Edward , Probstfield Jeffrey 
Published: April 2007

AbstractA surrogate endpoint in a cardiovascular clinical trial is defined as endpoint measured in lieu of some other so‐called ‘true’ endpoint. A surrogate is especially useful if it is easily measured and highly correlated with the true endpoint. Often the ‘true’ endpoint is one with clinical importance to the patient, for example, mortality or a major clinical outcome, while a surrogate is one biologically closer to the process of disease, for example, ejection fraction. Use of the surrogate can often lead to dramatic reductions in sample size and much shorter studies than use of the true endpoint. We discuss several problems common in trials with surrogate endpoints. Most important is the effect of missing data, especially in the face of informative censoring. Possible solutions are the assignment of scores or formal penalties to missing data.
Surrogate endpoints in clinical trials: Ophthalmologic disorders
Authors: Hillis Argye , Seigel Daniel 
Published: April 2007

AbstractWe examined three examples of surrogate observations in ophthalmology. The first'represents a simple case: the status of one eye is used as a surrogate for the (unobservable) status of the opposite eye in the same individual. The second and third examples represent possible extrapolation of long term results on the basis of early changes. These examples are used to illustrate the assumptions intrinsic in the use of surrogate variables.
Surrogate endpoints in clinical trials: Definition and operational criteria
Authors: Prentice Ross L. 
Published: April 2007

AbstractI discuss the idea of using surrogate endpoints in the context of clinical trials to compare two or more treatments or interventions in respect to some ‘true’ endpoint, typically a disease occurrence. In order that treatment comparison based on a surrogate response variable have a meaningful implication for the corresponding true endpoint treatment comparison, a rather restrictive criterion is proposed for use of the adjective ‘surrogate’. Specifically, I propose that a surrogate for a true endpoint yield a valid test of the null hypothesis of no association between treatment and the true response. This criterion essentially requires the surrogate variable to ‘capture’ any relationship between the treatment and the true endpoint, a notion that can be operationalized by requiring the true endpoint rate at any follow‐up time to be independent of treatment, given the preceding history of the surrogate variable. I then discuss this operational criterion in the examples of the accompanying papers1–3 and in the setting of trials aimed at the primary and secondary prevention of cancer.
How study design affects outcomes in comparisons of therapy. I: Medical
Authors: Colditz Graham A. , Miller James N. , Mosteller Frederick 
Published: April 2007

AbstractWe analysed 113 reports published in 1980 in a sample of medical journals to relate features of study design to the magnitude of gains attributed to new therapies over old. Overall we rated 87 per cent of new therapies as improvements over standard therapies. The mean gain (measured by the Mann—Whitney statistic) was relatively constant across study designs, except for non‐randomized controlled trials with sequential assignment to therapy, which showed a significantly higher likelihood that a patient would do better on the innovation than on standard therapy (p = 0.004). Randomized controlled trials that did not use a double‐blind design had a higher likelihood of showing a gain for the innovation than did double‐blind trials (p = 0.02). Any evaluation of an innovation may include both bias and the true efficacy of the new therapy, therefore we may consider making adjustments for the average bias associated with a study design. When interpreting an evaluation of a new therapy, readers should consider the impact of the following average adjustments to the Mann—Whitney statistic: for trials with non‐random sequential assignment a decrease of 0.15, for non‐double‐blind randomized controlled trials a decrease of 0.11.
How study design affects outcomes in comparisons of therapy. II: Surgical
Authors: Miller James N. , Colditz Graham A. , Mosteller Frederick 
Published: April 2007

AbstractWe analysed the results of 221 comparisons of an innovation with a standard treatment in surgery published in six leading surgery journals in 1983 to relate features of study design to the magnitude of gain. For each comparison we measured the gain attributed to the innovation over the standard therapy by the Mann—Whitney statistic and the difference in proportion of treatment successes. For primary treatments (aimed at curing or ameliorating a patient's principal disease), an average gain of 0.56 was produced by 20 randomized controlled trials. This was less than the 0.62 average for four non‐randomized controlled trials, 0.63 for 19 externally controlled trials, and 0.57 for 73 record reviews (0.50 represents a toss‐up between innovation and standard). For secondary therapies (used to prevent or treat complications of therapy), the average gain was 0.53 for 61 randomized controlled trials, 0.58 for eleven non‐randomized controlled trials, 0.54 for eight externally controlled trials, and 0.55 for 18 record reviews. Readers of studies evaluating new treatments, particularly for primary treatments, may consider adjustment of the gain according to the study type.
A two‐stage trial design for testing treatment, self‐selection and treatment preference effects
Authors: Rücker Gerta 
Published: April 2007

AbstractWe propose a two‐stage randomized clinical trial design for separating treatment effects from those resulting from choosing treatment. At the first stage all patients are randomly allocated to one of two groups, the random group and the option group. At the second stage, patients in the random group are randomized a second time to treatment A or B, whereas patients in the option group are given a free choice between the two treatments. If there are differences in treatment response between the random group and the option group, there are two potential sources of bias: self‐selection by choosing treatment, and effects of suggestion by receiving the preferred treatment. A linear model is presented to estimate these effects separately along with test statistics which are approximately normally distributed.
A two‐period crossover design for the comparison of two active treatments and placebo
Authors: Koch Gary G. , Amara Ingrid A. , Brown Byron W. , Colton Theodore , Gillings Dennis B. 
Published: April 2007

AbstractThis paper discusses a two‐period crossover design for the comparison of two active treatments (A and B) and placebo (P) for relief of recurrent symptoms of a chronic health disorder. It is based on blocks of ten patients for which the treatment sequences A:B and B:A are each assigned to three patients and the sequences A:P, P:A, B:P, and P:B are each assigned to one patient; thus, treatment periods have a 2:2:1 allocation for A, B, and P. The principal model for analysis of this design involves additive subject effects, period effects, and treatment effects. Analysis of within‐patient differences provides an estimate of the comparison between active treatments with variance (2vw/7r) and an estimate of the comparison between an active treatment and placebo with variance (4vW/7r); here vw, is the within‐patient variance and r is the number of blocks of ten patients. Analyses which address carryover effects and treatment effects adjusted for carryover effects are also described. An example using simulated data on relief of recurrent gastrointestinal pain illustrates the methodology.
Empirical comparisons of proportional hazards and logistic regression models
Authors: Ingram Deborah D. , Kleinman Joel C. 
Published: April 2007

AbstractWe compare parameter estimates from the proportional hazards model, the cumulative logistic model and a new modified logistic model (referred to as the person‐time logistic model), with the use of simulated data sets and with the following quantities varied: disease incidence, risk factor strength, length of follow‐up, the proportion censored, non‐proportional hazards, and sample size. Parameter estimates from the person‐time logistic regression model closely approximated those from the Cox model when the survival time distribution was close to exponential, but could differ substantially in other situations. We found parameter estimates from the cumulative logistic model similar to those from the Cox and person‐time logistic models when the disease was rare, the risk factor moderate, and censoring rates similar across the covariates. We also compare the models with analysis of a real data set that involves the relationship of age, race, sex, blood pressure, and smoking to subsequent mortality. In this example, the length of follow‐up among survivors varied from 5 to 14 years and the Cox and person‐time logistic approaches gave nearly identical results. The cumulative logistic results had somewhat larger p‐values but were substantively similar for all but one coefficient (the age‐race interaction). The latter difference reflects differential censoring rates by age, race and sex.
The bias of the sample proportion following a group sequential phase II clinical trial
Authors: Chang Myron N. , Wieand Harry S. , Chang Vivian T. 
Published: April 2007

AbstractGroup sequential testing procedures have seen wide use in Phase II clinical trials. The sample proportion p̂ of responders is the commonly used estimator for the binomial response probability p. It can be shown that p̂ is the maximum likelihood estimator (MLE) of p. It is well known that MLE can be in general (Whitehead1) and p̂ in particular (Dupont2) biased estimators, if their computation follows a group sequential procedure. In this paper we numerically investigate the bias of p̂. We find that the magnitude of the bias of p̂ is less than 0.025 in all cases we investigated. We apply the idea in Whitehead1 to propose a bias‐adjusted estimator that reduces the bias substantially and reduces the mean square error as well in a certain range of p. We also evaluate the uniformly minimum variance unbiased (UMVU) estimator. If one does not mind a bias of 0.025, one may find the sample proportion a suitable estimator for p because of its simplicity and easy explanation. If one is concerned with bias, the bias‐adjusted estimator may be a good choice.
How to establish equivalence between treatments: A one‐sided clinical trial in paediatric oncology
Authors: Rodary Chantal , Com‐Nougue Catherine , Tournade Marie‐France 
Published: April 2007

AbstractThe design of a clinical trial to establish the equivalence of two treatments differs from that of an efficacy trial. The conventional null hypothesis of equivalent treatment efficacy is replaced by a null hypothesis of inequivalence which must be tested by appropriate statistics. In addition, the maximum allowable value of the true difference between the efficacy of two equivalent treatments must be specified. In oncology, such a trial is necessary when a standard treatment is replaced by a new less toxic one of equivalent efficacy. The statistical formulation is one‐sided. A trial comparing two pre‐operative treatments in childhood nephroblastoma is presented here and analysed according to this methodology.
Goodman and Kruskal's λ: A new look at an old measure of association
Authors: Makuch Robert W. , Rosenberg Philip S. , Scott Gwendolyn 
Published: April 2007

AbstractWe examine Goodman and Kruskal's λ using Efron's approach to regression and analysis of variance (ANOVA) for zero‐one outcome data. For a binary response cross‐classified by a single nominal predictor, we present a computationally simple ANOVA table in which λ is analogous to Pearson's R‐square. We characterize the relationship between λ and the commonly used apparent error rate in logistic regression, and show that λ is based implicitly on a prediction rule for a saturated model with classification level 0.5. This relationship suggests that we can correct the apparent error rate for chance by defining a natural generalization of λ that we call PRE, the proportional reduction in error. We illustrate the use of λ and PRE in an analysis of prognostic factors for one‐year survival in children with the acquired immunodeficiency syndrome (AIDS).
Converting relative risks to absolute risks: A graphical approach
Authors: Dupont William D. 
Published: April 2007

AbstractThis paper presents a graphical method for converting relative risks to absolute risks. These absolute risk estimates are a function of the patient's current age, the patient's risk of developing cancer relative to some baseline population, the age specific cancer hazard in the baseline population, and the patient's competing mortal risk from all other causes. Graphs for breast cancer morbidity in women, cardiovascular mortality in men, and lung cancer morbidity in men illustrate the method. These graphs provide the probability of developing cancer in the next twenty years given the patient's current age and relative risk. They are derived under the proportional hazards model. A graph for lung cancer in men that uses a plausible exponential hazards model is also provided. The paper illustrates the importance of competing mortal hazard from other causes on absolute cancer risk. The strengths and weaknesses of this method are discussed. The graphs presented in this paper may be used as an aid in clinical decision making and in patient counselling.
Cautions on the reanalysis of epidemiologic databases
Authors: Michalek Joel E. , Mihalko Daniel , Tripathi Ram C. 
Published: April 2007

AbstractThe statistical and epidemiologic literatures contain many examples of the reanalyses of medical databases. Some are confirmatory, such as the many reanalyses of the Stanford heart transplant data. Others, however, concern issues that differ from those that the data originally intended to address. We demonstrate in this paper that the second kind of reanalysis, if undertaken uncritically, can produce biased results. Utilizing a log‐linear model, we show analytically that the reanalysis of a designed study without adjustment for the design variable can produce biased results. We express the magnitude of this bias as a function of second‐ and third‐order interactions in the data. We demonstrate further that preliminary testing for the existence of second‐ and third‐order interactions can provide substantial bias reduction.
Estimation of sojourn time distributions and false negative rates in screening programmes which use two modalities
Authors: Alexander Freda E. 
Published: April 2007

AbstractDay and Walter derived methods of joint maximum likelihood estimation for the sojourn time distribution and the false negative rate for a screening programme. Their methods are not directly applicable to a programme which uses alternate screening by two modalities whose sojourn times and false negative rates will differ. A modification is proposed and the results applied to data from the Edinburgh Randomised Trial of Breast Cancer Screening. This enables the effects of mammography and clinical examination to be separated. It is estimated that in a programme using both modalities 79 per cent of tumours arising in regularly screened women would be detected by screening and if the clinical examination were omitted this figure would be reduced by 5 per cent. The confidence intervals are, however, quite wide.