bluuebunny/crossref_metadata_2025 huggingface dataset:
Statistical methods for monitoring the AIDS epidemic
Authors: Zeger Scott L. , See Lai‐Chu , Diggle Peter J.
Published: April 2007

This article describes statistical methods for monitoring the epidemic of the acquired immunodeficiency syndrome (AIDS). A log‐linear model is proposed to estimate AIDS incidence and its growth rate while taking account of delays in case reporting. An empirical Bayes approach for estimating the epidemic growth rate in low prevalence subgroups is introduced. These methods are illustrated with an analysis of AIDS incidence trends for seven risk groups in each of six geographic regions using the Centers for Disease Control AIDS case registry data as of September 1987. The analysis finds that AIDS incidence is currently doubling about once every two years and that the relative composition of new cases is shifting away from the older epidemics such as in north‐eastern homosexual communities.
Models for the HIV infection and AIDS epidemic in the United States
Authors: Taylor Jeremy M. G.
Published: April 2007

Statistical models of the HIV infection epidemic in the U.S. which account for the observed incidence of AIDS cases in the years 1978–1987 are considered. The models assume a known distribution of times from infection to AIDS. The best model estimates that there were approximately 563,000 to 1,110,000 individuals infected in the U.S. in April 1987. These estimates do not take into account underreporting of AIDS cases.

The sensitivity of the conclusions to the model's assumptions is ascertained by investigating a variety of parametric models for the infection epidemic, a variety of likely distributions for the time from infection to AIDS, and some plausible alternatives for the history of AIDS cases in the U.S. It is concluded that there is too much uncertainty in the data and the models to be able to give highly accurate predictions of the number of people currently infected in the U.S., however, the results from the best fitting models suggest that there are less than the 1 to 1·5 million infected as estimated by the Centers for Disease Control. A Bayesian scheme is suggested for incorporating the uncertainty in the models.
Disease prevention models of voluntary confidential screening for human immunodeficiency virus (HIV)
Authors: Gail Mitchell H. , Preston Dale , Piantadosi Steve
Published: April 2007

Epidemic models are developed to evaluate the potential benefits of voluntary confidential testing (VCT) for HIV. The number of tests required to prevent one case (called the economic ratio ER), and the number of cases prevented by the screening programme are calculated. Methods differ from commonly used epidemic models because susceptibles and infecteds are subdivided according to testing status, to permit modelling of the efficacy of knowledge of HIV status in retarding epidemic spread. The methods also allow for subdivision into several homogeneous subpopulations whose members may be attracted in varying degrees to members of other subpopulations. Thus the common assumption of free mixing across subpopulations is relaxed. The following conclusions which refer to a population of 100,000 persons over a period of 5 to 15 years, are robust to wide variations in assumed parameter values and other aspects of modelling:

VCT prevents hundreds or thousands of infections in isolated high risk populations and ER values are typically less than 100, making VCT very attractive economically.

VCT prevents only a few infections in isolated low risk populations with initial prevalence 0‐1 per cent or less, and the ER values are well above 2000. However, in ‘low risk’ populations with 1 per cent initial prevalence, tens or hundreds of infections may be prevented, and ER values fall below 2000 for plausible spread rates, indicating that VCT may be economically feasible in such settings.

In a mixed population of gays, bisexuals and heterosexuals, a VCT programme that aims primarily at the gay/bisexual subpopulations prevents more disease in the gay/bisexual populations, prevents more disease in the heterosexual populations, and requires fewer tests per case prevented than a VCT programme that tests all subpopulations equally.
Heterogeneity in the probability of HIV transmission per sexual contact: The case of male‐to‐female transmission in penile—vaginal intercourse
Authors: Wiley James A. , Herschkorn Stephen J. , Padian Nancy S.
Published: April 2007

Recent studies have indicated variation in the infectivity β of HIV among heterosexual couples. We represent this heterogeneity by modelling β as a random variable. Using data on the number of contacts and seroconversion of couples, we fit the model by maximum‐likelihood estimation with a beta distribution and a discrete distribution for β. The estimates indicate the heterogeneity is extreme. The limitations of the model and the implications of the results for epidemiologic spread and further research are discussed.
A model of the spread of HIV infection and the demographic impact of AIDS
Authors: Bongaarts John
Published: April 2007

The objective of the computer simulation model described here is to project, for periods up to one or more decades, the annual incidence and prevalence of HIV infection and AIDS in a population with given epidemiological, behavioural and demorgraphic characteristics. In addition, the epidemic's impact on a range of demographic variables is calculated. The epidemiological components of the model use a compartmental approach and they are described with sets of linear differential equations. The demographic framework in which the epidemiological components are integrated, is based on a standard cohort component method of population projection. The simulated population is stratified by age, gender, sexual behaviour, marital status and infection/disease status. The concluding section provides an illustrative application of the model to a Central African population. In this hypothetical simulation covering the period from 1975 to 2000, HIV prevalence in the adult population rises from 0 to 21 per cent. By the end of the projection period mortality is about double the level that would have prevailed in the absence of the epidemic, but, owing to the very high birth rates that prevail in most of Africa, the growth rate of the population remains substantially positive.
Analysis of surveillance data with poisson regression: A case study
Authors: Parker R. A.
Published: April 2007

One way to examine the ability of a statistical technique to detect changes in surveillance data is to analyse data sets with known changes and observe how accurately these changes can be detected. The elimination of restrictions on legal abortions should have reduced mortality associated with abortions, particularly mortality associated with illegal abortions. The sensitivity of Poisson regression to detect changes in abortion associated mortality from 1962 to 1984 was assessed for the entire United States of America and for specific states. Although it is clear that this change occurred using data from the entire United States, only the largest of the individual state data sets examined (370 events over 23 years) consistently demonstrated the expected pattern. Inconsistent patterns were found in data sets from two states with between one‐fourth and one‐half this number of events. The legal change was not detected at all in three states with a small number of events (1 event per year or less). From this case study, a minimum of two or three events per year seems to be necessary before Poisson regression can detect outliers. Comparisons of the four tests used suggest that tests based on model deviance are superior to tests based on comparison of observed and expected number of events.
A surveillance system based on a short memory scheme
Authors: Shore David Lee , Quade Dana
Published: April 2007

A surveillance system is proposed to detect an increase in the mean of a Poisson distribution of cases of a disease. This system, called short memory (SM), is based on conditional binomial tests which are performed sequentially at fixed time intervals. The probability of rejection at each test defines the run length distribution which has a geometric tail. A standard SM scheme outperforms other SM schemes. The CUSUM outperforms the SM schemes when the baseline mean is specified correctly. This type of misspecification does not affect the SM scheme.
The application of time series forecasting methods to an estimation problem using provisional mortality statistics
Authors: Katzoff Myron
Published: April 2007

Provisional estimates of mortality for selected causes of death are published each month by the National Center for Health Statistics. These estimates are based upon a ten per cent sample of death certificates in the United States. Final mortality results, based upon all the death certificates for a calendar year, are available one to two years after publication of the provisional estimates. This paper explores the potential of time series forecasting techniques for improving mortality estimates by using the correlation structure between the provisional and final series to obtain mortality estimates that are expected to be closer to final values than currently used provisional estimates.
Time series analysis of syphilis surveillance data
Authors: Zaidi Akbar A. , Schnell Daniel J. , Reynolds Gladys H.
Published: April 2007

To manage a public health programme effectively it is necessary to set objectives and priorities according to the resources available. To utilize available resources optimally in a disease control programme we should both estimate the present and predict the future magnitude of the health problem. The CDC has an effective programme to control syphilis, a sexually transmitted disease. To keep track of control activities, CDC receives the number of syphilis cases from all states every three months. Primary and secondary syphilis cases declined from an all time high of 106,539 cases in 1947 to 6399 cases in 1956. Since then, syphilis cases increased to 27,921 in 1986. Congenital syphilis cases declined from 17,600 in 1941 to 239 in 1983, but increased to 408 in 1986. We built time series models for primary and secondary syphilis cases in men and women and congenital syphilis cases in children under one year of age. These models were used to forecast syphilis cases in all three categories. This analysis suggests that no change in the trend of male syphilis cases has occurred, but syphilis cases in women and congenital syphilis cases in children under one year of age during 1987 have increased.
Covariate imbalance and random allocation in clinical trials
Authors: Senn S. J.
Published: April 2007

A model is developed to estimate the effect of covariate imbalance on the size of a test of treatment efficacy in randomized clinical trials comparing two treatments when dispersion parameters are known. It is concluded that tests of homogeneity on the covariates should not be performed, that covariate imbalance is just as much a problem for large studies as for small ones in terms of effect on size, and that the effect of correlation between covariates and measures of efficacy is more complex than has previously been sugested. The best way to adjust for covariate imbalance is by an analysis of covariance.
A comparison of estimated proportional hazards models and regression trees
Authors: Segal Mark R. , Bloch Daniel A.
Published: April 2007

We present examples of the usage of regression trees for censored response via two real world datasets, one a rheumatoid arthritis survival study and the other a hip replacement study, and draw comparisons with the results of Cox proportional hazards modelling. The two methods pursue different goals. Motivation of the tree techniques is the desire to extract meaningful prognostic groups while the proportional hazards model enables assessment of the impact of risk factors. The methods are thus complementary. For the arthritis study the two techniques corroborate one another, although the flavour of the conclusions derived differ. For the hip replacement study, however, the regression tree approach reveals structure that would not emerge from a routine proportional hazards analysis. We also discuss the treatment of data analytic issues such as the handling of missing values and influence in the presence of non‐uniform censoring.
A log‐linear model for ordinal data to characterize differential change among treatments
Authors: Francom S. F. , Chuang‐Stein C. , Landis J. R.
Published: April 2007

We propose a family of log‐linear models for ordinal data that contain parameters reflecting change patterns to compare treatments relative to change from baseline. Under the most general model, rates of change can depend not only upon the direction of change, but also upon the level of the baseline classification. We describe methods for selection of a parsimonious model and for tests of hypotheses concerning treatment differences. Interpretation of treatment differences in the follow‐up response profiles, within baseline strata, employs the concept of stochastic ordering. Data from two clinical trials illustrate the proposed procedure.
Estimation of a common odds ratio in paired‐cluster randomization designs
Authors: Donner Allan , Hauck Walter
Published: April 2007

We develop two estimators of a common odds ratio Ψ for designs in which the investigator randomly assigns each of two clusters to interventions within strata. The estimators rely on an empirical adjustment for clustering to provide improved estimators of Ψ relative to the standard Woolf and Mantel—Haenszel estimators, respectively. The results of a simulation study show that the suggested adjustment improves the accuracy of both of these well‐known estimators under conditions likely to arise in practice. We find the clustered Woolf estimator as particularly effective in terms of mean squared error reduction. We also discuss interval estimation.
The control of confounding by intermediate variables
Authors: Robins James
Published: April 2007

In epidemiologic studies of the effect of an exposure on disease, the crude association of exposure with disease may fail to reflect a causal association due to confounding by one or more covariates. Most previous discussions of confounding in the epidemiologic literature have considered only point exposure studies, that is, studies that measure exposure and covariate status only once, at start of follow‐up. In this paper we offer definitions of confounding suitable for longitudinal studies that obtain data on exposure, covariate, and vital status at several points in time. An important difference between longitudinal studies and point exposure studies is that, in longitudinal studies, a time‐dependent covariate can be simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease. In this paper I propose an estimator, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable.
Graphical generalized residuals in fitting distributions: Applications to epidemiological follow‐up data
Authors: Elandt‐Johnson Regina C. , Smith Fraser B.
Published: April 2007

Three kinds of generalized residuals based on probability integral transformation are defined and their roles in graphical goodness of fit testing are discussed. Although they are useful in fitting parametric distributions, their value is questionable, however, in fitting Cox's proportional hazard rate (PHR) models. They almost always appear to give a good fit because of (a) almost non‐parametric nature of the PHR models; (b) heavy right‐hand censoring in epidemiological data; and (c) scale adjustment in graphical presentation. Thus, the apparent overall fit has little inferential meaning, but the residuals are useful in exploratory analysis. A simple method, based on non‐parametric stratified residual plots, for selection of risk factors is discussed. It is, in fact, equivalent to comparison of several empirical survival functions, one for each set of values (stratum) of a covariable under consideration. It is also suggested that for the preliminary PHR model, the continuous variables should be discretized and indicator variables used in the model to check analytically the graphical perception of the possible functional form of the contribution of each covariable to the PHR model.
Regression models for time to seroconversion following experimental bovine leukaemia virus infection
Authors: Lassauzet Marie‐Liesse G. , Johnson Wesley O. , Thurmond Mark C.
Published: April 2007

This paper develops a parametric model for time to seroconversion after experimental bovine leukaemia virus (BLV) infection, and examines the effects of inoculation route, volume of inoculum, type of inoculation material, and antigen status of donor on seroconversion time. We used parametric and nonparametric statistical methodology to analyse interval data on 150 animals from 13 published reports. The log‐logistic model fitted the observed times to seroconversion better than the log‐normal or Weibull models, which were the considered alternatives.
Unidirectional multiple comparisons of poisson rates
Authors: Suissa Samy , Salmi Rachid
Published: April 2007

This paper presents an exact significance test procedure for unidirectional multiple comparisons of Poisson variables. It is sufficiently general for application to situations where the ‘denominator’, which might be the amount of follow‐up time in cohort studies or the expected number of events in studies that use standardized rates, differs from one group to the other, and where a group‐specific alternative hypothesis is of interest. We use unidirectional Z statistics, of the form proposed by Dunnett, along with the conditional distribution needed in computing the exact attained significance levels, to address three distinct unidirectional hypotheses. In one case, we compare one exposed with several reference groups, while in the other two cases, we compare several exposed groups with one reference group. We also provide a multiple stage testing approach. Data from studies in cancer and trauma epidemiology illustrate the procedures.
Regression with an ordered categorical response
Authors: Hastie T. J. , Botha J. L. , Schnitzler C. M.
Published: April 2007

A survey on Mseleni joint disease in South Africa involved the scoring of pelvic X‐rays of women to measure osteoporosis. The scores were ordinal by construction and ranged from 0 to 12. It is standard practice to use ordinary regression techniques with an ordinal response that has that many categories. We give evidence for these data that the constraints on the response result in a misleading regression analysis. McCullagh's11proportional‐odds model is designed specifically for the regression analysis of ordinal data. We demonstrate the technique on these data, and show how it fills the gap betweenordinary regressionandlogistic regression(for discrete data with two categories). In addition, we demonstrate non‐parametric versions of these models that do not make any linearity assumptions about the regression function.
Sample sizes for constructing confidence intervals and testing hypotheses
Authors: Bristol David R.
Published: April 2007

Although estimation and confidence intervals have become popular alternatives to hypothesis testing and p‐values, statisticians usually determine sample sizes for randomized clinical trials by controlling the power of a statistical test at an appropriate alternative, even those statisticians who recommend the use of confidence intervals for inference. There is merit in achieving consistency in the techniques for data analysis and sample size determination. To that end, this paper compares sample size determination with use of the length of the confidence interval with that obtained by control of power.
Statistical analysis of the stages of HIV infection using a Markov model
Authors: Longini Ira M. , Clark W. Scott , Byers Robert H. , Ward John W. , Darrow William W. , Lemp George F. , Hethcote Herbert W.
Published: April 2007

We use a staged Markov model to estimate the distribution and mean length of the incubation period for acquired immunodeficiency syndrome (AIDS) from a cohort of 603 human immunodeficiency virus (HIV) infected individuals who have been followed through various stages of infection. The model partitions the infected period into four progressive stages: (1) infected but antibody‐negative; (2) antibody‐positive but asymptomatic; (3) pre‐AIDS symptoms and/or abnormal haematologic indicator; and (4) clinical AIDS. We also model a fifth stage: death due to AIDS. The estimated mean (median) waiting times in each stage of infection are stage 1, 2·2 (1·5) months; stage 2, 52·6 (36·5) months; stage 3, 62·9 (43·6) months; and stage 4, 23·6 (16·3) months. We estimate the mean AIDS incubation period (from infection to development of clinical AIDS) as 9·8 years with a 95 per cent confidence interval of [8·4, 11·2] years. The paper also considers the estimated density function of the AIDS incubation period and the estimated survival functions for individuals in each stage of infection. This work represents one of the most complete statistical descriptions to date of the natural history of HIV infection.
Test for batch‐to‐batch variation in stability analysis
Authors: Chow Shein‐Chung , Shao Jun
Published: April 2007

We propose several statistical tests for batch‐to‐batch variation in the stability analysis of a drug product and present applications with data from new drug application stability and marketing stability analyses.
Measuring measuring errors
Authors: Healy M. J. R.
Published: April 2007

The assessment of errors of measurement and their effects needs to take into account the structure of the data in which the errors occur. This is illustrated and the appropriate analysis of variance specified for a variety of contexts. The notion of an acceptable level of measurement error is discussed in terms of decision theory and illustrated by a simplified example.
A linear regression model for the analysis of life times
Authors: Aalen Odd O.
Published: April 2007

A linear model is suggested for the influence of covariates on the intensity function. This approach is less vulnerable than the Cox model to problems of inconsistency when covariates are deleted or the precision of covariate measurements is changed. A method of non‐parametric estimation of regression functions is presented. This results in plots that may give information on the change over time in the influence of covariates. A test method and two goodness of fit plots are also given. The approach is illustrated by simulation as well as by data from a clinical trial of treatment of carcinoma of the oropharynx.
Bootstrapped confidence intervals for the Cox model using a linear relative risk form
Authors: Barlow William E. , Sun Wei‐Huei
Published: April 2007

A linear relative risk form for the Cox model is sometimes more appropriate than the usual exponential form. The usual asymptotic confidence interval may not have the appropriate coverage, however, due to flatness of the likelihood in the neighbourhood of β. For a single continuous covariate, we derive bootstrapped confidence intervals with use of two resampling methods. The first resamples the original data and yields both one‐step and fully iterated estimates of β. The second resamples the score and information quantities at each failure time to yield a one‐step estimate. We computed the bootstrapped confidence intervals by three different methods and compared these intervals to one based on the asymptotic standard error and to a likelihood‐based interval. The bootstrapped intervals did not perform well and underestimated the true coverage in most cases.
Exponential survival trees
Authors: Davis Roger B. , Anderson James R.
Published: April 2007

This paper concerns a recursive partitioning algorithm for incomplete survival data. The splitting criterion employed is exponential log‐likelihood loss. The method has been integrated into a computer program similar in structure to the commercial package CART. Special features incorporated into the program include a modification to prevent zero hazard estimates during the cross‐validation procedure and a method based on the chi‐square distribution for final tree selection. Simulation results demonstrate the program's capability to identify the underlying hazard structure in survival data. An example utilizing data from patients with diffuse large cell lymphoma is presented.
An overview of issues related to the correction of non‐differential exposure measurement error in epidemiologic studies
Authors: Willett Walter
Published: April 2007

Procedures to correct estimates of association in epidemiologic studies for the effects of exposure measurement error have rarely been employed in practice. The application of correction procedures would be enhanced by methods that allow the inclusion of covariates, provide corrected confidence intervals, are compatible with commonly employed analytic methods, and that are clearly communicated to potential users. Before using such a procedure, it is important to clearly specify the conceptual ‘true’ exposure, determine the nature of the measurement error, and decide whether a reproducibility study or validity study is required to quantify the error. The careful use of correction procedures promises to improve our knowledge of the quantitative relationships between many exposures and disease since it is likely that we have substantially underestimated the effects of many exposures and overstated our confidence in null results.
Covariance analysis in generalized linear measurement error models
Authors: Caroll Raymond J.
Published: April 2007

We summarize some of the recent work on the errors‐in‐variables problem in generalized linear models. The focus is on covariance analysis, and in particular testing for and estimation of treatment effects. There is a considerable difference between the randomized and non‐randomized models when testing for an effect. In randomized studies, simple techniques exist for testing for a treatment effect. In some instances, such as linear and multiplicative regression, simple methods exist for estimating the treatment effect. In other examples such as logistic regression, estimating a treatment effect requires careful attention to measurement error. In non‐randomized studies, there is no recourse to understanding and modelling measurement error. In particular ignoring measurement error can lead to the wrong conclusions, for example the true but unobserved data may indicate a positive effect for treatment, while the observed data indicate the opposite. Some of the possible methods are outlined and compared.
A measurement‐error model for binary and ordinal regression
Authors: Tosteson Tor D. , Stefanski Leonard A. , Schafer Daniel W.
Published: April 2007

Exposure assessment poses special problems in air pollution epidemiology. This paper proposes a probit regression model for binary and ordinal outcomes that uses exposure validation information to develop estimates for the coefficient of the true exposure when only the inaccurate ‘surrogate’ measure of exposure is available for the individuals in the health study. This method is closely related to recently developed measurement‐error methods, and is based on the assumption that the outcome and the surrogate exposure are conditionally independent given the true exposure. A test statistic is proposed for checking this conditional independence assumption when more than one surrogate is available, and an interpretation of the coefficient estimate is provided in the event that the assumption is violated. The methods are applied to an example involving nitrogen dioxide exposure and wheeze in children.
Further results on covariate measurement errors in cohort studies with time to response data
Authors: Pepe Margaret Sullivan , Self Steven G. , Prentice Ross L.
Published: April 2007

The impact of covariate measurement errors on the estimation of relative risk regression parameters is discussed. First the dependence of the induced relative risk process on the cumulative baseline failure rate function is noted. Next induced relative risk models under some specific failure time and measurement error models are described, including the much simplified models that are appropriate under a ‘rare disease’ assumption. The presentation then turns to the joint estimation of relative risk parameters of primary interest along with measurement error parameters. A partial likelihood product is proposed for such estimation and asymptotic properties are indicated. Guidance is also presented as to the appropriate size of a ‘validation’ sample relative to the full cohort size. Finally some more general considerations are presented as to the usefulness and interpretation of deattenuated regression coefficients.
Correspondence analysis as a screening method for indicants for clinical diagnosis
Authors: Crichton Nicola J. , Hinde John P.
Published: April 2007

In clinical diagnosis, a patient's symptoms are observed and the probabilities of various diseases are assessed. A widely used method of formalizing this approach is independent Bayes in which symptoms are assumed to be independent conditional on the disease category. Correspondence analysis provides a method for examining the dependence between symptoms and assists in the selection of a reduced set of symptoms for the application of the independent Bayes method. This approach is illustrated on two data sets concerned with patients attending Accident and Emergency departments with chest pain and acute abdominal pain, respectively.
On the regression analysis of tumour recurrence rates
Authors: Seeber G. U. H.
Published: April 2007

Regression models with mixture (random) components are proposed for the statistical analysis of recurrent events when waiting times between successive events are unknown. These models allow adjustment of parameter estimates for unobserved heterogeneity in the population (due for example to missing covariates) or overdispersion resulting from inexact distributional assumptions. The models are illustrated by a study of recurrence rates of superficial bladder cancer in men.
Constructing a cut‐off point for a quantitative diagnostic test
Authors: Schäfer Helmut
Published: April 2007

This paper is concerned with the problem of constructing a suitable cut‐off point for a quantitative diagnostic test from a sample of diseased and non‐diseased individuals, when the specificity and/or sensitivity required for the resulting diagnostic classification rule are specified. Statistical procedures are discussed which yield an explicit cut‐off point, together with the necessary confidence limits on the true specificity and sensitivity of the test operating at this point (by contrast to confidence limits referring to an unknown optimal choice of the cut‐off point). Sample size formulae are derived, showing a relative efficiency of up to 1.5 for a new procedure introduced in this paper over the usual procedure based on tolerance limits.
Estimation of haemophilia‐associated AIDS incidence in Japan using individual dates of diagnosis
Authors: Tango Toshiro
Published: April 2007

This paper presents a procedure for obtaining short‐term projections and lower bounds on the size of the acquired immunodeficiency syndrome (AIDS) epidemic. The method is similar to that proposed by Brookmeyer and Gail1, 2 but adapted to the situation where individual dates of AIDS diagnosis are available. It gives results that are not dependent on the choice of time intervals by contrast to the procedure proposed by Brookmeyer and Gail which was based on the tabulated numbers of AIDS cases. Applications are illustrated with haemophilia‐associated AIDS incidence in Japan where only 45 cases have been diagnosed by 31 December 1987.
Misclassification in more than one factor in a case‐control study: A combination of Mantel‐Haenszel and maximum likelihood approaches
Authors: Duffy S. W. , Rohan T. E. , Day N. E.
Published: April 2007

Misclassification can arise in the case‐control setting in both the risk factor of interest, and in a potential confounder. If true status is not ascertainable, but an independent replicated sample is available, adjusted maximum likelihood estimates of the individual binomial proportions can be obtained, and hence a relative risk estimate of the Mantel—Haenszel type constructed. An interval estimate on the latter can be calculated using the asymptotic properties of the likelihood function. The method is illustrated by application to a case‐control study of breast cancer risk and such lifestyle attributes as diet, smoking and alcohol consumption, in which misclassification was anticipated and consequently repeatability data were collected.
Polio trial: An early efficient clinical trial
Authors: Meier Paul
Published: April 2007

The Salk Vaccine Field Trial was a randomized, placebo‐controlled trial designed to test the efficacy of the Salk killed virus vaccine. Although the trial was not without some problems, both in design and analysis, none was important enough to raise serious questions about the results. Thus, the design of the Field Trial was well adapted to the demanding requirements. The trial owes its success in a large part to substantial public involvement. In designing future, large‐scale, simple trials, we may still be able to benefit from the lessons of the Polio Vaccine Trial.
Cost and efficiency considerations in community‐based trials of vitamin A in developing countries
Authors: Tielsch James M. , West Keith P.
Published: April 2007

Recent investigations conducted in Indonesia have identified an important role for adequate vitamin A nutriture in preventing mortality in young children. A randomized, community‐based trial of vitamin A supplementation in northern Sumatra demonstrated a 34 per cent reduction in mortality among pre‐school aged children who received twice yearly vitamin A supplementation. These results have stimulated renewed interest in vitamin A as a potentially important mediator of improved child survival. Cost and efficiency considerations are important in these types of trials but must be balanced with logistical constraints that are unique to these environments. A number of these issues are discussed and some general guidelines are proposed for such investigations.
Selection of patients for randomized controlled trials: Implications of wide or narrow eligibility criteria
Authors: Yusuf Salim , Held Peter , Teo K. K. , Toretsky Elizabeth Rudin
Published: April 2007

This paper discusses the various philosophies that influence the selection of patients for entry into randomized controlled trials. Although a number of different and often competing issues have to be considered depending upon the trial, keeping entry criteria simple, wide and at times even flexible is usually preferable. Such a strategy can be a positive virtue by helping to attain the large numbers of patients that are usually needed to reliably detect the sorts of moderate benefits that are plausible, at a reasonable cost and by providing answers that are relevant to many different categories of patients with a particular condition.
The use of a run‐in to enhance compliance
Authors: Lang Janet M.
Published: April 2007

With a run‐in, potential subjects are given practice with all or part of the study protocol prior to randomization. The purpose is to exclude poor compliers from the trial and randomize only proven, good compliers. The feasibility of a run‐in and the net benefit derived from that strategy depend upon the difficulty of the protocol; the availability of compliance aids during the trial; and the impact of the chosen common practices for all subjects during the run‐in on the post‐randomization event rates of the groups and the post‐randomization blinding of study subjects to their treatment assignments. Two detailed examples of the use of a run‐in are presented along with six general recommendations to guide the use of a run‐in.
An evaluation of central laboratories in three VA cooperative studies
Authors: Peduzzi Peter , Hartigan Pamela , Johnson Gary
Published: April 2007

We compared central laboratory with local determinations of key clinical measurements in three VA Cooperative Studies. Electrocardiographic evidence of new myocardial infarction was assessed in the study of Aspirin Therapy and Unstable Angina, ejection fraction measurement in the Coronary Artery Bypass Surgery Trial and lesion size in the Angioplasty Compared with Medicine (ACME) Trial. The findings in the Aspirin Trial indicated that central coding of all serial electrocardiograms in 1266 patients to detect new acute myocardial infarction by computer algorithm was not cost‐effective when compared with the local investigator's diagnosis on the basis of a central Electrocardiographic Committee as the reference standard. In the other two trials, the contribution of the central laboratories was important because the assessments of the local investigators generally underestimated the degree of abnormality in the Bypass Trial and overestimated it in the ACME Trial. The VA results have clearly demonstrated two cases in which the decision for central evaluation was prudent, but one case in which it was not cost‐effective. These equivocal findings indicate the need to evaluate the contribution of central laboratories when used as an adjunct to local determinations. Such evaluations may provide guidelines for decision‐making in the design of future trials.
Using cost‐effectiveness analysis to improve the efficiency of allocating funds to clinical trials
Authors: Detsky Allan S.
Published: April 2007

This study applied a cost‐effectiveness model to seven randomized trials. The model demonstrates the effect of design choices made in the planning stages of a clinical trial on the costs and benefits derived from conducting the trial. The study focused on one parameter used to calculate sample size: the minimum clinically important difference in event rates between control and experimental therapies. The study shows that the model can be operationalized to these trials. A computerized software package and manual has been developed to simplify the calculations. While there was some variation in the incremental cost‐effectiveness ratios across the seven trials in this study, all ratios may be below the funding threshold. This analytical technique can be used to demonstrate explicitly the resource consequences of the design of randomized trials and perhaps to set funding priorities.
The applicability of the results of streamlined trials to clinical practice
Authors: Wilhelmsen Lars
Published: April 2007

During recent decades we have seen an encouraging development of trial methodology. Less effort, however, has been devoted to the application of findings of trials to the practice of medicine. We cannot expect results of controiled trials alone to determine standard therapy, for clinical judgements are also required. Both beneficial and adverse effects are often only detectable on the group level. There are generally very few indications that drug effects differ qualitatively in subgroups of patients. It is important not only to prove or disprove effects, but also to find out how to implement a proper therapy for a majority of eligible patients. Four examples from treatment after myocardial infraction show that drug usage may not always be based upon results of clinical trials. The adoption of Swedish guidelines for thrombolytic therapy in acute myocardial infarction is taken as an example of an apparently optimal application of trial results in practice.
Incorporating historical control data in planning phase II clinical trials
Authors: Thall Peter F. , Simon Richard
Published: April 2007

Phase II studies of new medical treatments often use historical data on the standard treatment for comparative evaluation. Incorrectly disregarding inherent variability in the historical data may lead to erroneous conclusions regarding the efficacy of the experimental treatment. We propose an approach to phase II trial design which accounts for both inter‐study and intra‐study variation. Our results indicate that it is sometimes best to randomize a proportion of the patients to a control arm. We choose this proportion to maximize the precision of the estimated experimental treatment effect. We evaluate operating characteristics of the design numerically, and provide illustrations based on historical data from cancer chemotherapy trials.
Modelling peritonitis rates and associated risk factors for individuals on continuous ambulatory peritoneal dialysis
Authors: Vonesh Edward F.
Published: April 2007

A mixed effects Poisson regression model is proposed for analysing potential risk factors associated with peritonitis, a bacterial infection of the peritoneum which is common among individuals on continuous ambulatory peritoneal dialysis (CAPD). The model incorporates a set of fixed effects corresponding to concomitant information collected across individuals as well as a random effect due to individuals. The method of maximum likelihood is used to estimate the unknown parameters. When applied to clinical data obtained on 81 CAPD patients from four centres, the mixed effects model demonstrated a much better fit than the corresponding fixed effects Poisson regression model.
Application of the Cox model as a predictor of relative risk of coronary heart disease in the albany study
Authors: Chang Hwa‐Gan H. , Lininger Lloyd L. , Doyle Joseph T. , Maccubbin Patricia A. , Rothenberg Richard B.
Published: April 2007

Patients in long term studies of coronary heart disease may have different levels of risk during the course of study. Smoking habits, blood pressure, and obesity may change drastically during this period. The multiple logistic model, the most commonly used model for the analysis of coronary heart disease studies, does not consider survival time in assessment of the dependent covariates and does not account for the censoring which usually occurs in such studies. We propose a Cox model with time‐dependent covariates to model the risk of coronary heart disease in the Albany study. The Cox model we fitted evaluates the patients' risk on the basis of the data at the last visit. With this methodology, we can evaluate whether it is advantageous for individuals to modify their risk of disease by their effecting changes in their covariates, that is to stop smoking, lose weight, change diet and so on. The important covariates that explain the risk of coronary heart disease were the same in our model as in the models used in the earlier reports. The estimated relative risks were slightly higher in most cases and lend more support to the need to encourage patients to achieve a better covariate state.
Disease entities, mixed multi‐normal distributions, and the role of the hyperkinetic state in the pathogenesis of hypertension
Authors: Schork Nicholas J. , Weder Alan B. , Schork M. Anthony , Bassett David R. , Julius Stevo
Published: April 2007

This paper concerns the theory and relevance of finite mixtures of univariate and multivariate normal distributions in medical research and suggests that multivariate normal mixture analysis, hitherto not extensively explored, is an appealing approach to the investigation of etiologically obscure, multifactorial diseases such as hypertension. We elaborate a statistical strategy to resolve and test for a normal mixture distribution in a seemingly heterogenous population actually comprising homogenous subpopulations. We use this strategy to validate the hypothesis that in the population at large there is a subgroup of individuals with the characteristic of a hyperkinetic circulatory state, defined as the association of an elevated cardiac index or heart rate with high blood pressure. This subgroup may have implications for the pathogenesis of hypertension. We discuss directions and implications for future research into the pathogenesis of hypertension.
The assessment of methods of measurement
Authors: Chinn Susan
Published: April 2007

Criteria are given for the choice of scale prior to estimation of repeatability. Recommendations of Bland and Altman should then be used for expressing repeatability and agreement of methods of measurement on the same scale. Repeatability of measurements on different scales should be compared using the appropriate ratio of variances, or intraclass correlation coefficient. A reference range for diagnosis requires a high ratio of between‐subject variation to total variation. The index of separation between diseased and healthy subjects should be used whenever possible. Changes within patients should be compared with reference change ranges, and not against the diagnostic range.
Yates's correction for continuity and the analysis of 2 × 2 contingency tables
Authors: Haviland Mark G.
Published: April 2007

Despite recommendations to the contrary, medical researchers still routinely use the Yates‐corrected chisquare statistic in analyses of 2 × 2 contingency tables. Research has shown that these ‘corrected’ statistics are overly conservative and that the conventional Pearson chi‐square generally provides adequate control over type I error probabilities. This paper makes a straightforward argument against use of Yates's correction for continuity and Fisher's exact probability test.
Application of new two‐sample tests to data from a randomized placebo‐controlled heart‐failure trial
Authors: Tandon P. K. , Stander Herbert , Schwarz Richard P.
Published: April 2007

In a randomized placebo‐controlled double‐blind trial of 230 congestive heart failure patients, four treatments were evaluated for efficacy, with exercise tolerance time (ETT) as the primary outcome. Various two‐sample tests were applied to the analysis of ETT data. It is shown in this paper that the conventional two‐sample tests (t and rank‐sum) are insensitive to situations where the effect of the experimental therapy is not consistent across a patient population. Tests recommended by O'Brien are more appropriate for these data. It is also shown that the application of the O'Brien tests led to the identification of sub‐groups where the observed effect of the experimental therapy was most pronounced.
MODEL SELECTION AND THE ESTIMATION OF ODDS RATIOS IN THE PRESENCE OF EXTRANEOUS FACTORS
Authors: Schall Robert , Zucchini Walter
Published: April 2007

This paper deals with model selection and the estimation of odds ratios from cross‐classified frequencies in the presence of extraneous factors. The odds ratio is estimated in different ways dependent on whether the extraneous factor is modelled as an effect modifier, a confounder, or neither. Routinely this choice is based on statistical tests of null hypotheses. By contrast, we propose selection of the model which is estimated to maximize the accuracy of the estimator of the odds ratio, on average. We demonstrate how a non‐parametric bootstrap method can be used to carry out the selection, and illustrate the methodology using an example on use of oral contraceptives and myocardial infarction.
Estimation of parasitic infection dynamics when detectability is imperfect
Authors: Nagelkerke Nico J. D. , Chunge Ruth N. , Kinoti Stephen N.
Published: April 2007

The simultaneous estimation of infection rate, cure rate and detectability of parasitic infections is considered. A new method for this estimation based on a simple statistical model assuming constant transition rates between parasite states is proposed. Repeated observations on the infection status of the same individuals is required for this method. A maximum likelihood approach is used for parameter estimation and the calculation of standard errors of the estimates. The method is illustrated by a longitudinal study of the presence of Giardia lamblia infection in Kenyan children.