bluuebunny/crossref_metadata_2025 huggingface dataset:
Conclusions. 1: The relationship between clinical trials and clinical practice: The risks of underestimating its complexity
Authors: Liberati Alessandro
Published: April 2007

Two main points are addressed in the following remarks. The first is that the survival of randomized clinical trials (RCTs) as the gold standard by which to assess the effectiveness of medical technology is being seriously challenged. It is incumbent upon methodologists and clinical researchers to understand why this is happening and commit themselves to improving the process through which new possibilities for improved treatments are selected, prioritized and implemented. The second point is that trialists should abandon the idea that the availability of relevant information is a sufficient condition for a timely and widespread acceptance of RCTs results. Contrary to what health services research has repeatedly indicated, trialists still, by and large, seem to believe in a simplistic model that assumes a one‐way linear and rapid relationship between scientific knowledge and clinical practice. In the concluding section the paper pinpoints some issues to be further discussed from the point of view of monitoring and conducting trials.
Conclusions. 2
Authors: O'Neill Robert T.
Published: April 2007

I have been asked to take an overall view of the presentations in this ‘early stopping rules’ workshop and to extract some messages from them. in attempting to meet the charge to summarize what i have heard and what i have concluded over the last two days, i will focus on the following issues: the underlying theme of the workshop concerns whether the statistical strategies proposed for monitoring trials are a help or a hindrance to the successful completion of clinical trials; how much of a problem is this and what should be done about it; whether the current diversity in practical operational aspects of data monitoring and analysis is responsible for some trials being stopped earlier than planned and thus should we begin to develop some consensus on ‘good practices and procedures’ in this area; and finally to place this topic within the broader public health framework of clinical trials of treatments for any life‐threatening disease, what are the regulatory impacts and consequences of early stopping, especially when decisions regarding the licensure and marketing of treatments depends upon results from monitored clinical trials that might be stopped early.
Assessment of predictive models for binary outcomes: An empirical approach using operative death from cardiac surgery
Authors: Marshall Guillermo , Grover Frederick L. , Henderson William G. , Hammermeister Karl E.
Published: April 2007

Predictive models in medical research have gained popularity among physicians as an important tool in medical decision making. Eight methodological strategies for creating predictive models are compared in a large, complex data base consisting of preoperative risk and operative outcome data on 12, 712 patients undergoing coronary artery bypass grafting and entered into the Department of Veterans Affairs Cardiac Surgery Risk Assessment Program between April 1987 and March 1990. The models under consideration were developed to predict operative death (any death within 30 days following the surgical procedure or later if the result of a perioperative complication). The two strategies with the best predictive power among the eight examined were stepwise logistic regression alone and data reduction by cluster analysis combined with clinical judgement followed by a logistic regression model. The additive model based on unadjusted relative risks, the model based on Bayes' Theorem, and the logistic model using all candidate variables were good alternatives. Whether or not we imputed values did not have a significant impact on the predictive power of the models.
Precision of incidence predictions based on poisson distributed observations
Authors: Hakulinen Timo , Dyba Tadeusz
Published: April 2007

Disease incidence predictions are useful for a number of administrative and scientific purposes. The simplest ones are made using trend extrapolation, on either an arithmetic or a logarithmic scale. This paper shows how approximate confidence prediction intervals can be calculated for such predictions, both for the total number of cases and for the age‐adjusted incidence rates, by assuming Poisson distribution of the age and period specific numbers of incident cases. Generalizations for prediction models, for example, using power families and extra‐Poisson variation, are also presented. Cancer incidence predictions for the Stockholm‐Gotland Oncological Region in Sweden are used as an example.
Monte carlo methods for exloring sensitivity to distributional assumptions in a bayesian analysis of a series of 2 × 2 tables
Authors: Raghunathan T. E.
Published: April 2007

This paper develops Monte Carlo methods for a Bayesian analysis of a series of 2 × 2 tables under a variety of distributional assumptions. I assume that the data in each table were generated from a pair of binomial distributions and the logarithm of odds of a favourable response follows a bivariate distribution with means that are linear functions of covariates and an arbitrary covariance matrix. I use Gibbs and importance sampling methods to obtain various characteristics of the posterior distribution of the quantities of interest. I apply the method to analyse the data from a population case‐control study. Given the size of the population at risk I also derive the posterior distribution of the risk difference defined as the difference in the probabilities of disease development in the exposed and unexposed groups.
Efficient power computation for exact and mid‐P tests for the common odds ratio in several 2 × 2 tables
Authors: Hirji Mkarim F. , Tang Man‐Lai , Vollset Stein E. , Elashoff Robert M.
Published: April 2007

When designing a study that may generate a set of sparse 2 × 2 tables, or when confronted with ‘negative’ results upon exact analysis of such tables, we need to compute the power of exact tests. In this paper we provide an efficient approach for computing exact unconditional power for four exact tests on the common odds ratio in a series of 2 × 2 tables. These tests are the traditional exact test; a test based on a probability ordering of the sample space; and two tests based on ordering the sample space according to distance from the mean, or median. For each test, we consider both a conservative version and a mid‐P adjusted version. We explore three computational options for power determination: exact power computation, calculation of exact upper and lower bounds for power, and Monte Carlo confidence bounds for power. We present an interactive program implementing these options. For study design, the program may be run several times to arrive at a sample configuration with adequate power.
Efficient confidence bounds for ROC curves
Authors: Schafer Helmut
Published: April 2007

The specificity and sensitivity of a quantitative diagnostic marker depends on the selected cut‐off point. The ROC curve is generated by plotting sensitivity against specificity as the cut‐off point runs through the whole range of possible marker values. Confidence bounds for ROC curves may be constructed by combining two separate confidence intervals for sensitivity and specificity. In the present paper, a more efficient method, which is based on a statistical test introduced by Greenhouse and Mantel, is presented. The resulting 95 per cent confidence intervals are up to 40 per cent smaller. A formula is given to calculate the sample size necessary to obtain a confidence interval of a stipulated length at a specified point of the ROC curve. As an example, the performance of a tumour marker in the diagnosis of bone marrow metastases is investigated.
Sampling guidelines for testing secondary attack rates associated with short‐latency infectious diseases
Authors: Schork Nicholas J.
Published: April 2007

This paper discusses power and efficiency properties of the model proposed by Longini and Koopman for quantifying and testing secondary attack rates gathered from final size distribution data on infections in households. Sample size requirements are offered for a generalized likelihood ratio test that makes use of the secondary attack rate parameter implicated in the Longini‐Koopman model. The appropriateness of this statistic is also considered. In addition, a sequential likelihood ratio test of a secondary attack rate parameter is derived for infectious diseases with short latency or asymptomatic periods. A comparison of the proposed sequential and fixed sample tests is described. Finally, applications involving final size distributions of household influenza infections are used to illustrate the practical merits of the proposed tests.
The choice of cancer treatment based on covariate information
Authors: Yakovlev Andrej Yu. , Goot Roman E. , Osipova Tamara T.
Published: April 2007

This paper is concerned with methods for choosing optimal treatment for particular patients; the central idea is that information contained in covariates may be useful for this kind of decision making. For each patient estimated conditional survivor functions for each mode of treatment may be compared and the optimal treatment may be defined as the one providing higher predicted survival. Regression survival models form the basis for a pertinent statistical decision rule, and a procedure for retrospective validation of such a rule is given. The usefulness of the proposed approach is illustrated with an application to the treatment of patients with cancer of cervix uteri. The results of data analysis suggest that the choice of optimal treatment based on covariate information is of considerable promise for practical use.
On closed test procedures for dose‐response analysis
Authors: Rom Dror M. , Costello Robert J. , Connell Laura T.
Published: April 2007

This paper concerns the testing of a dose‐response effect in medical studies. We consider two situations. The first is when the drug effect on a parameter of interest is likely to increase (or decrease) with increasing doses. We propose a procedure based on the closure principle of Peritz combined with an application of a closed testing procedure of Marcus et al. to a test proposed by Tukey et al. The second situation is the analysis of a drug effect where one might observe a reversal at higher doses. For this situation, we propose simultaneous examination of contrasts among the doses at each of several stages of the testing scheme. We calculate critical values for this procedure incorporating the correlation structure among the contrasts. Both procedures strongly control the family‐wise error rate at the pre‐determined α level. They also provide information about the shape of the dose response lacking in other commonly used procedures for testing against an ordered alternative hypothesis. We illustrate the procedures on two datasets.
The application of REML in clinical trials
Authors: Brown H. K. , Kempton R. A.
Published: April 2007

Residual maximum likelihood (REML) is a technique for estimating variance components in multi‐classified data. In contrast to analysis of variance it can be routinely applied to unbalanced data and avoids some of the problems of biased variance estimates found with standard maximum likelihood estimation. The full REML method is of particular value for the analysis of unbalanced clinical trials as it allows recovery of all the available information on treatment effects which can lead to significant improvements in their precision. The use of REML has until recently been limited by heavy computational requirements and lack of readily available software. This is no longer such a restriction, however, as REML procedures are now available in several widely‐used statistical packages, including BMDP, Genstat and SAS. This paper describes the REML technique and discusses its application to three common types of clinical trial: crossover, repeated measures and multicentre.
Statistical analysis of proliferative index data in clinical trials
Authors: Grambsch Patricia , Louis Thomas A. , Bostick Roberd M. , Grandits Greg A. , Fosdick Lisa , Darif Mohamed , Potter John D.
Published: April 2007

Measurements of proliferative activity in colonic epithelial cells are being used as surrogate endpoints in clinical trials for colon cancer prevention. Proliferative index data exemplify an important type of clinical trial endpoint. The outcome variable is a proportion in which the denominator is an ancillary statistic and in which measurement error and technician judgement are important sources of variability. The paper proposes a statistical model for a repeated measures clinical trial with this type of endpoint, in the context of proliferative activity data. The model is a two‐stage random effects linear model in the log scale. In addition to fixed effects covariates, it explicitly incorporates two major sources of variability: the number of epithelial cells counted and the reader effect. Although the resulting likelihood is complicated, one can fit an approximate likelihood with minimal loss of efficiency using standard packages. We apply the model to a pilot randomized clinical trial.
Median analysis of blood pressure for a sample including treated hypertensives
Authors: White Ian R. , Chaturvedi Nishi , McKeigue Paul M.
Published: April 2007

We consider the analysis of epidemiological studies in which the outcome of interest is a physiological disturbance (such as blood pressure) which may have been corrected by medical intervention (such as antihypertensive treatment). It is important to take account of such intervention in studying the determinants of hypertension. We recommend using the median as a measure of location and assuming that the underlying values of subjects under intervention lie above the median. Some well‐established exact methods for simple problems are presented. An example is given in which the blood pressure differences between Afro‐Caribbeans and Europeans are underestimated by about one‐quarter if adjustment is not made for treated hypertensives.
Multilevel time series models with applications to repeated measures data
Authors: Goldstein Harvey , Healy Michael J. R. , Rasbash Jon
Published: April 2007

The analysis of repeated measures data can be conducted efficiently using a two‐level random coefficients model. A standard assumption is that the within‐individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model for such data is proposed which consists of a standard multilevel model for repeated measures data augmented by an autocorrelation model for the level 1 residuals. First‐ and second‐order autoregressive models are considered in detail, together with a seasonal component. Both discrete and continuous time are considered and it is shown how the autocorrelation parameters can themselves be structured in terms of further explanatory variables. The models are fitted to a data set consisting of repeated height measurements on children.
A simulation study comparing two approximations for a quasi t‐quantile, used in repeated measures anova
Authors: Silverberg Arthur R.
Published: April 2007

In the analysis of variance it is not unusual to form a denominator of an approximate t‐statistic from a linear combination of mean squares. Two examples include the Behrens‐Fisher problem and the repeated measures analysis of variance. One solution to the problem of finding the appropriate degrees of freedom is to use Satterthwaite's approximation while another solution, due to Cochran, is to form a weighted t‐statistic. Based upon computer simulations I have found that the magnitude of the bias of the Satterthwaite approximation was less than that of the Cochran approximation in 68/75 cases considered. When the bias of the Cochran approximation was smaller than the bias of the Satterthwaite approximation, I found that the estimated bias of the Satterthwaite approximation no more than 0.5 per cent in the cases considered. I recommend performing the additional calculations required for the Satterthwaite approximations when combining two mean squares, especially when one mean square is based upon 12 or fewer degrees of freedom.
Alternative models for ordinal logistic regression
Authors: Greenland Sander
Published: April 2007

Armstrong and Sloan have reviewed two types of ordinal logistic models for epidemiologic data: the cumulative‐odds model and the continuation‐ratio model. I review here certain aspects of these models not emphasized previously, and describe a third type, the stereotype model, which in certain situations offers greater flexibility coupled with interpretational advantages. I illustrate the models in an analysis of pneumoconiosis among coal miners.
A simple frailty model for family studies with covariates
Authors: Babiker Abdel , Cuzick Jack
Published: April 2007

A simple, easily analysed frailty model based on logistic regression and employing only binary frailties is developed and analysed. Methods for testing for frailty and estimates of frailty parameters and covariate effects are presented. The effects of ignoring or mis‐specifying frailty are examined in detail and shown to downwardly bias the estimates of covariate effects in a variety of situations, even when frailty is overestimated. When the true frailty is large it can be difficult to estimate accurately because of the flatness of the likelihood. However, it is shown that this does not have a large influence on the estimates of the effects of the observed covariates. Methods for dealing with multiple frailties, for example, sibships and spouse pairs, are also developed. The results are illustrated on a large data set examining the risk factors associated with palmar keratoses.
Helping doctors to draw appropriate inferences from the analysis of medical studies
Authors: Burton Paul R.
Published: April 2007

Most clinicians and many medical statisticians interpret standard frequentist confidence intervals by invoking the Bayesian concept of subjective probability. Fortunately, the assumptions that render this interpretation acceptable are often quite reasonable in the setting of the practical day‐to‐day analysis of medical data. This article takes the subjective interpretation of confidence intervals to its logical conclusion and argues that the inferential understanding of clinicians and public health physicians could potentially be improved if, where it was appropriate, standard inferential statements ‐ point estimates, 95 per cent confidence intervals and P‐values ‐ were supplemented by estimates of the subjective posterior probability, assuming a uniform prior density, that the true value of a parameter to be estimated exceeds one or a series of thresholds that are clinically critical or easily interpretable. Many decision makers in the health care arena draw totally inappropriate inferences from analyses where the point estimate indicates a clinically valuable effect but the null hypothesis cannot formally be rejected, and, although the proposed approach could be of potential value in a range of settings, it is argued that it could be of particular use in the rational interpretation of underpowered studies that must inform critical clinical or public health decisions.
Testing for baseline balance in clinical trials
Authors: Senn Stephen
Published: April 2007

Once the data from a clinical trial are available for analysis it is common practice to carry out ‘tests of baseline homogeneity’ on prognostic covariates before proceeding to analyse the effects of treatment on outcome variables. It is argued that this practice is philosophically unsound, of no practical value and potentially misleading. Instead it is recommended that prognostic variables be identified in the trial‐plan and fitted in an analysis of covariance regardless of their baseline distribution (statistical significance).
An optimal three‐stage design for phase II clinical trials
Authors: Ensign Lisa Garnsey , Gehan Edmund A. , Kamen Douglas S. , Thall Peter F.
Published: April 2007

A phase II clinical trial in cancer therapeutics is usually a single‐arm study to determine whether an experimental treatment (E) holds sufficient promise to warrant further testing. When the criterion of treatment efficacy is a binary endpoint (response/no response) with probability of response p, we propose a three‐stage optimal design for testing H0: p ≤ p0 versus H1: p ≥ p1, where p1 and p0 are response rates such that E does or does not merit further testing at given levels of statistical significance (α) and power (1 − β). The proposed design is essentially a combination of earlier proposals by Gehan and Simon. The design stops with rejection of H1 at stage 1 when there is an initial moderately long run of consecutive treatment failures; otherwise there is continuation to stage 2 and (possibly) stage 3 which have decision rules analogous to those in stages 1 and 2 of Simon's design. Thus, rejection of H1 is possible at any stage, but acceptance only at the final stage. The design is optimal in the sense that expected sample size is minimized when p = p0, subject to the practical constraint that the minimum stage 1 sample size is at least 5. The proposed design has greatest utility when the true response rate of E is small, it is desirable to stop early if there is a moderately long run of early treatment failures, and it is practical to implement a three‐stage design. Compared to Simon's optimal two‐stage design, the optimal three‐stage design has the following features: stage 1 is the same size or smaller and has the possibility of stopping earlier when 0 successes are observed; the expected sample size under the null hypothesis is smaller; stages 1 and 2 generally have more patients than stage 1 of the two‐stage design, but a higher probability of early termination under H0; and the total sample size and criteria for rejection of H1 at stage 3 are similar to the corresponding values at the end of stage 2 in the two‐stage optimal design.
Effect of verification bias on positive and negative predictive values
Authors: Zhou Xiao‐Hua
Published: April 2007

The pairing of sensitivity and specificity expresses the efficacy of a test, and positive and negative predictive values measure the accuracy of a diagnostic test when applied to a particular patient. To calculate these measures, one has to know the true disease status of each patient. In practice, however, some patients may not be selected for verification of disease status. It has been shown that the estimated sensitivity and specificity may be biased if one includes in the study sample only the patients with verified disease statuses. This paper concerns the properties of the estimators of positive and negative predictive values using only patients with verified disease statuses. First, I show that these estimators are unbiased and provide consistent estimators for the variances of these estimators under the assumption that the probability of selecting a patient for a disease verification procedure does not depend directly on the true disease status of the patient. Then, I use the ML method to study the sensitivity of the naive estimators to the departure from the conditional independence assumption.
Analysing survival in the presence of an auxiliary variable
Authors: Finkelstein Dianne M. , Schoenfeld David A.
Published: April 2007

A primary endpoint of AIDS trials is the length of survival. Often there is auxiliary information available on measures of disease progression, which significantly alter the risk of mortality. This paper explores the use of this information in obtaining a refined estimate of survival, and a test based on this estimate. The methods are applied to an AIDS clinical trial, and results of simulations are provided which compare the approach to standard methods for survival analysis.
Projection of incidence rates to a larger population using ecologic variables
Authors: Frey Carolin M. , Feuer Eric J. , Timmel Marilyn J.
Published: April 2007

There is wide acceptance of direct standardization of vital rates to adjust for differing age distributions according to the representation within age categories of some referent population. One can use a similar process to standardize, and subsequently project vital rates with respect to continuous, or ratio scale ecologic variables. We obtained from the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) programme, a 10 per cent subset of the total U.S. population, country‐level breast cancer incidence during 1987‐1989 for white women aged 50 and over. We applied regression coefficients that relate ecologic factors to SEER incidence to the full national complement of county‐level information to produce an age and ecologic factor adjusted rate that may be more representative of the U.S. than the simple age‐adjusted SEER incidence. We conducted a validation study using breast cancer mortality data available for the entire U.S. and which supports the appropriateness of this method for projecting rates.
Interval censoring in longitudinal data of respiratory symptoms in aluminium potroom workers: A comparison of methods
Authors: Samuelsen Sven Ove , Kongerud Johny
Published: April 2007

In a longitudinal study of workers in seven Norwegian aluminium plants, the time to development of asthmatic symptoms could only be determined to lie in the interval between two consecutive health examinations. In a previous paper we analysed the data by survival techniques for interval censored data. In the present paper the data are reanalysed in two ways and compared to the previous analyses. First, it is assumed that occurrence of symptoms coincided with reporting, in which case the data can be analysed as right censored. Secondly, the follow‐up times are completely disregarded and the effects of covariates are analysed on the binary outcomes of symptoms. Comparing the estimated effects of the covariates between the three approaches, only minor differences were found. However, the estimates on incidence were strongly influenced by whether the data were analysed as right or interval censored.
Standardized rates of recurrent outcomes
Authors: Stukel Thérèse A. , Glynn Robert J. , Fisher Elliott S. , Sharp Sandra M. , Lu‐Yao Grace , Wennberg John E.
Published: April 2007

Longitudinal studies are often concerned with estimating the rate of an event that may recur. Examples are nonmelanoma skin cancer rates, screening rates for breast cancer using mammography and hospital admission rates. We propose simple estimators for directly and indirectly standardized summary rates and relative rates of recurrent events and their variances. We also develop an estimator of the excess rate in an area if the rate in another area applied. For non‐recurrent events, the estimators are identical to the usual standardized summary rates.5 The estimators are independent of the underlying distribution of the event of interest and allow for unequal follow‐up times and event rate heterogeneity among individuals. The method is not computationally intensive and does not require specialized software. We illustrate the application of the method in a retrospective cohort study of hospital utilization patterns of Medicare enrollees in Boston and New Haven over a three and a half year period.
A comparison of two phase I trial designs
Authors: Korn Edward L. , Midthune Douglas , Chen T. Timothy , Rubinstein Lawrence V. , Christian Michaele C. , Simon Richard M.
Published: April 2007

Phase I cancer chemotherapy trials are designed to determine rapidly the maximum tolerated dose of a new agent for further study. A recently proposed Bayesian method, the continual reassessment method, has been suggested to offer an improvement over the standard design of such trials. We find the previous comparisons did not completely address the relative performance of the designs as they would be used in practice. Our results indicate that with the continual reassessment method, more patients will be treated at very high doses and the trials will take longer to complete. We offer some suggested improvements to both the standard design and the Bayesian method.
Reduction in burden of illness: A new efficacy measure for prevention trials
Authors: Chang M. N. , Guess H. A. , Heyse J. F.
Published: April 2007

A new efficacy measure is developed for use in prevention trials of interventions which may affect both disease incidence and disease severity. We assign a severity score to each incident case and sum severity scores over all incident cases within each treatment group to create a burden‐of‐illness score for each treatment group. Efficacy is evaluated by the difference between the burden‐of‐illness per randomized subject in the two randomized treatment groups. Since the numbers of summands in each burden‐of‐illness score is a random variable, standard methods of analysis are not directly applicable. The asymptotic distribution and sampling properties of the net reduction in the burden‐of‐illness score are derived for trials designed to stop either after a fixed length of follow‐up or after the occurrence of a fixed number of cases. We illustrate the method with data from a clinical trial of a human rotavirus vaccine.
A unified method for monitoring and analysing controlled trials
Authors: Grossman Jason , Parmar Mahesh K. B. , Spiegelhalter David J. , Freedman Laurence S.
Published: April 2007

Group sequential methods are becoming increasingly popular for monitoring and analysing large controlled trials, especially clinical trials. They not only allow trialists to monitor the data as it accumulates, but also reduce the expected sample size. Such methods are traditionally based on preserving the overall type I error by increasing the conservatism of the hypothesis tests performed at any single analysis. Using methods which are based on hypothesis testing in this way makes point estimation and the calculation of confidence intervals difficult and controversial. We describe a class of group sequential procedures based on a single parameter which reflects initial scepticism towards unexpectedly large effects. These procedures have good expected and maximum sample sizes, and lead to natural point and interval estimates of the treatment difference. Hypothesis tests, point estimates and interval estimates calculated using this procedure are consistent with each other, and tests and estimates made at the end of the trial are consistent with interim tests and estimates. This class of sequential tests can be considered in both a traditional group sequential manner or as a Bayesian solution to the problem.
Deviance estimates of sample size for equivalence tests in vaccine trials
Authors: Garner J. Barry
Published: April 2007

This paper proposes a sample size procedure for both equivalence and conventional tests for the comparison of two binomial proportions, based on the signed square root of the deviance. When the comparison is based on the odds ratio, I describe an alternate ‘close’ conditional exact method that gives results that support those given by the deviance method. I summarize the advantages of the deviance‐based method and also show that in general equivalence situations the sample size estimate depends upon the measure of comparison selected, odds ratio, risk ratio or risk difference.
Bayesian hierarchical analysis of within‐units variances in repeated measures experiments
Authors: Have Thomas R. Ten , Chinchilli Vernon M.
Published: April 2007

We develop hierarchical Bayesian models for biomedical data that consist of multiple measurements on each individual under each of several conditions. The focus is on investigating differences in within‐subject variation between conditions. We present both population‐level and individual‐level comparisons. We extend the partial likelihood models of Chinchilli et al. with a unique Bayesian hierarchical framework for variance components and associated degrees of freedom. We use the Gibbs sampler to estimate posterior marginal distributions for the parameters of the Bayesian hierarchical models. The application involves a comparison of two cholesterol analysers each applied repeatedly to a sample of subjects. Both the partial likelihood and Bayesian approaches yield similar results, although confidence limits tend to be wider under the Bayesian models.
Stratification of summary statistic tests according to missing data patterns
Authors: Dawson Jeffrey D.
Published: April 2007

Summary statistics, such as slope or area under the time‐response curve, reduce the dimensionality of repeated measures data and can thereby simplify the comparison of groups in longitudinal studies. Since summary statistic distributions vary according to the amount, timing, and type of any missingness that occurs, one must choose between analysing the data unconditionally or conditionally on the missingness patterns. This paper uses simulations to compare such unstratified and stratified summary statistic analyses with respect to their size and power under models that allow for both non‐informative and informative missingness mechanisms. Of particular interest is the robustness of these methods to violations of the assumptions that one must make if they are to have proper test size. It is found that stratification of the analysis tends to result in an increase of power, and improves the robustness to violations of missing data assumptions.
Simultaneous back‐projection of AIDS incidence data for two or more groups
Authors: Marschner Ian C. , Newstead Stuart V. , Becker Niels G.
Published: April 2007

Back‐projection of AIDS incidence data is useful for estimating characteristics of the HIV epidemic curve and forms a basis for projections of the AIDS epidemic curve. Its application to subgroups of the population is limited by its imprecision for groups with a small number of cases. Back‐projection can be made more precise by pooling data from different groups and linking their HIV infection intensities by a model. Here we propose a method based on proportional infection intensities and study its performance with simulations and applications to AIDS in different States of Australia and haemophiliacs in the U.S.A. This method of simultaneous back‐projection is shown to reduce substantially the width of confidence intervals for HIV infection intensities and for total numbers infected.
Estimates of disease incidence in women based on antenatal or neonatal seroprevalence data: HIV in new york city
Authors: Ades A. E. , Medley G. F.
Published: April 2007

Piecewise constant incidence models were developed to estimate the force of infection in women from age‐and time‐specific antenatal or neonatal seroprevalence data. Differential inclusion of infected women in sero‐surveys compared to uninfected women was taken into account, with respect to both changes in inclusion rate following infection, and changes in relative inclusion rate over calendar time. These models were applied to anonymous HIV seroprevalence data collected from neonates born to black and Hispanic women in New York City 1988‐1992, with incidence and fertility parameters estimated by maximum likelihood. Estimates of inclusion rate parameters accorded well with what is known about the natural history of HIV. The data could not distinguish between additive and multiplicative combination of the effects of age and time on incidence. Incidence was strongly dependent on age with the highest incidence in women aged 20‐34 years. There was strong evidence that incidence had been falling in Hispanic women since 1982‐1984. The results illustrate the extent to which trends in incidence over time may be confounded by changes in the relative inclusion rate of infected and uninfected women.
Screening tests: Can we get more by doing less?
Authors: Tu Xin M. , Litvak Eugene , Pagano Marcello
Published: April 2007

Estimating the prevalence of the human immunodeficiency virus (HIV) in a group is challenging; this is especially so when the prevalence is small. One reason is that the presence of measurement errors resulting from the limited precision of tests makes estimation, using traditional methods, impossible in some screening situations. Measurement error is real, ignoring it leads to severe bias, and inference about the prevalence becomes unsatisfactory. Indeed, in a low prevalence situation the expected number of false positives is very high, often even higher than the number of true positives. The second reason is that in the low prevalence areas the large sample is needed in order to obtain non‐zero estimate. This is usually a very costly, and often unrealistic, solution. This paper considers the advantages and disadvantages of pooled testing as an alternative solution to this problem. We show that by pooling sera samples we not only achieve a cost saving but also, which is counterintuitive, an increase in the estimation accuracy. We also discuss the statistical issues associated with the resulting estimator.
Seasonal and other short‐term influences on United States AIDS incidence
Authors: Bacchetti Peter
Published: April 2007

This paper models monthly AIDS diagnosis counts in terms of smooth secular trend, calendar month effects, and the number of workdays per month. A parameterization of month effects allows separation of true seasonal effects from a linear trend over the calendar year and an arbitrary June effect. There is strong evidence for seasonal patterns, other calendar month effects, and workday effects. Examination of subgroups defined by reporting delay, initial diagnosis, risk group, and region shows evidence for seasonal patterns in some diagnosis groups, for effects due to imputed diagnosis dates, and for effects due to patients' choices of when to seek diagnosis.
Prediction and validation in the public health modelling of HIV/AIDS
Authors: Bailey Norman T. J.
Published: April 2007

Mathematical models are an integral part of long‐range scientific research and are broadly equivalent to the hypotheses to be tested. Validation consists: (1) in checking whether theoretical expectations are sufficiently close to observed values; and (2) in showing that theoretical constructions that pass the first test can also make verifiable predictions of future events. When modelling is used in operational situations to assist practical decision‐making, as in the public health surveillance, prediction and control of infectious diseases, especially HIV/AIDS, it is easy to use the first criterion, but not so simple to implement the second. The paper discusses various methods of improving the validation of a specific classical compartmental model of HIV/AIDS geared to good serial public health data on AIDS incidence. These methods include model fitting to existing data, cross‐checking findings with independent research results, general circumstantial support, and the possibility in special situations of the quasi‐prediction of present or recent data using models fitted only to sufficiently distant past data.
Dependent HIV incidences in back‐projection of AIDS incidence data
Authors: Becker Niels G. , Chao Xu
Published: April 2007

Techniques for reconstructing plausible HIV incidence curves from AIDS incidence data are called methods of back‐projection, or back‐calculation. Approaches to back‐projection tend to make the simplifying assumption that the quarterly HIV incidences are independent, which is not even approximately true. Here we investigate whether smoothed non‐parametric back‐projection based on this simplifying assumption gives sensible back‐projections and appropriate measures of precision for these reconstructed HIV incidence curves. Simple models for HIV transmission are shown to have much greater variation than the corresponding non‐homogeneous Poisson process arising from the independence assumption. Nevertheless, bearing in mind that the objective is to reconstruct the HIV epidemic curve for the current epidemic, it is argued that such back‐projection does give sensible HIV curves. This conclusion is supported by a simulation study, which also finds that confidence intervals for the HIV incidences are wider for transmission data than those determined from independent Poisson data.
Using time of first positive HIV test and other auxiliary data in back‐projection of AIDS incidence
Authors: Marschner Ian C.
Published: April 2007

Estimation of HIV incidence by the method of back‐projection typically uses data on the time of diagnosis of AIDS cases, together with known information about the incubation distribution of AIDS. This paper discusses back‐projection using auxiliary data on AIDS cases, particularly the time of first positive HIV test. We discuss the possibility that certain types of auxiliary data, including time of first positive test, can be useful in back‐projection because they provide extra information about the incubation period of AIDS cases. Under a back‐projection model, theoretical efficiency calculations are given comparing back‐projection with and without the time of first positive HIV test of AIDS cases. These calculations suggest that such data have the potential to significantly improve HIV incidence estimates, particularly in the recent past. Smoothed non‐parametric estimates of both HIV incidence and time‐dependent testing rates are described. These can be obtained using the EM algorithm, in conjunction with a smoothing step or a penalized likelihood. The benefit of these methods in practice needs to be assessed as such data become available.
Backcalculation models of age‐specific HIV incidence rates
Authors: Rosenberg Philip S.
Published: April 2007

This paper extends the use of backcalculation to estimate past incidence of HIV infection in different age groups from age‐specific counts of AIDS incidence. The approach is flexible and allows the distribution of age at HIV infection to change over time. In this new approach, the incubation distributions used to backcalculate HIV infection rates depend on the age at HIV infection, because younger age is associated with slower progression. The effect of age on progression is estimated from a joint analysis of natural history data from several cohort studies of gay and bisexual men. As in previous applications, the incubation distributions also change over time to accommodate treatment effects and the 1987 revision of the surveillance definition. The method is applied to AIDS incidence data for the United States population. Estimated infection incidence for the entire population declined from peak levels seen in the mid‐1980s. However, persons under age 25 years have accounted for an increasing proportion of new HIV infections as the epidemic has progressed. Quantitative estimates were sensitive to the assumed incubation distribution, but the trend toward younger age at HIV infection was apparent using several models of the incubation distribution. Although the models are computationally intensive, they provide useful information about seroincidence trends in different age groups.
Incorporating HIV test data into forecasts of the AIDS epidemic in Scotland
Authors: Raab Gillian M. , Fielding Katherine L. , Allardice Gwen
Published: April 2007

For AIDS cases in Scotland the date of HIV antibody positive diagnosis (HIV test date) is recorded on the AIDS case reports. Data are also available on the total numbers of individuals with HIV positive test reports in Scottish laboratories in each year since testing became available. These data are incorporated into a model of the HIV epidemic described in terms of testing rates by calendar year. The HIV infection curve is modelled as a step function, and the testing rates are allowed to differ between steps. Results are presented for intravenous drug users (IDUs) and for homosexual/bisexual men. The estimated rates of testing for the IDUs (estimates from 19 per cent to 44 per cent per year) are considerably higher than those for homosexual/bisexual men (estimates of 10 per cent to 17 per cent per year). Data on the year of testing for AIDS diagnoses gave relatively little improvement in estimates of the HIV infection curve. However, when this information is combined with data on the total number of HIV positive diagnoses per year, there is a dramatic improvement in the estimate of the HIV infection curve. This is particularly marked for infections in the most recent period and for the estimates of cumulative infections up to the present. However, these improvements are gained at the cost of assumptions of similar testing rates applying to all sections of the HIV infected populations, which will be difficult to check in practice. This suggests that these methods should not be used in isolation but in combination with other evidence about the spread of HIV infection in a population.
The effect of covariates on the induction time of AIDS using improved imputation of exact seroconversion times
Authors: Gauvreau Kimberlee , Degruttola Victor , Pagano Marcello , Bellocco Rino
Published: April 2007

This paper explores the effects of covariates such as age, gender, chronological time of seroconversion, and mode of virus transmission on the interval between HIV seroconversion and the development of AIDS. For many cohorts which are available to provide information about such effects, both the time of seroconversion and the time of onset of AIDS may be right‐ or interval‐censored. To analyse these doubly‐censored data, we propose a method in which we first estimate the marginal distribution of the chronological time of seroconversion and use this distribution to impute the seroconversion time for each subject. Subsequently, we estimate the distribution of induction times conditional on the seroconversion distribution. We apply this technique to a cohort of HIV seropositive subjects recruited from various outpatient facilities throughout Italy and show that age at seroconversion has the greatest effect on induction time.
Distribution of the latency period for perinatally acquired AIDS
Authors: Mawhinney Samantha , Pagano Marcello
Published: April 2007

In the United States, over 86 per cent of paediatric cases of acquired immunodeficiency syndrome (AIDS) were infected through maternal or perinatal transmission and current estimates suggest that approximately 6000 children are born to infected women each year. In industrialized countries, less than 25 per cent of infants maternally exposed to the human immunodeficiency virus (HIV) are infected. The exact time of infection is unknown. However an endpoint, birth, exists from which the latency period or the time to diagnosis with clinical AIDS can be measured. In New York City, 7 years of reliable surveillance data were available for children with perinatally acquired AIDS. Using these data, the only estimable aspect of the latency is a conditional distribution, which conditions on diagnosis occurring before age 7. Because newborns carry maternal antibodies, a positive antibody test at birth is only an indication of maternal infection. Screening all newborns for HIV antibodies, while maintaining the anonymity of the mother and child, provides data on the number of children maternally exposed. The above conditioning can be removed by combining the surveillance and newborn screening data to obtain the unconditional latency distribution. We estimate a median diagnosis age of 4.1 years, somewhat longer than previously believed.