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Biomedical subjects

Stephen W Lagakos

Publications and source records attributed to Stephen W Lagakos.

12 recordsLinked to original sources
PubMed2007

Nonparametric estimation of the mean function of a stochastic process with missing observations.

In an attempt to identify similarities between methods for estimating a mean function with different types of response or observation processes, we explore a general theoretical framework for nonparametric estimation of the mean function of a response process subject to incomplete observations. Special cases of the response process include quantitative responses and discrete state processes such as survival processes, counting processes and alternating binary processes. The incomplete data are assumed to arise from a general response-independent observation process, which includes right-censoring, interval censoring, periodic observation, and mixtures of these as special cases. We explore two criteria for defining nonparametric estimators, one based on the sample mean of available data and the other inspired by the construction of Kaplan-Meier (or product-limit) estimator [J. Am. Statist. Assoc. 53 (1958) 457] for right-censored survival data. We show that under regularity conditions the estimated mean functions resulting from both criteria are consistent and converge weakly to Gaussian processes, and provide consistent estimators of their covariance functions. We then evaluate these general criteria for specific responses and observation processes, and show how they lead to familiar estimators for some response and observation processes and new estimators for others. We illustrate the latter with data from an recently completed AIDS clinical trial.

Acquired Immunodeficiency Syndrome↗
PubMed2006

Statistical methods for panel data from a semi-Markov process, with application to HPV.

Continuous-time, multistate processes can be used to represent a variety of biological processes in the public health sciences; yet the analysis of such processes is complex when they are observed only at a limited number of time points. Inference methods for such panel data have been developed for time homogeneous Markov models, but there has been little research done for other classes of processes. We develop likelihood-based methods for panel data from a semi-Markov process, where transition intensities depend on the duration of time in the current state. The proposed methods account for possible misclassification of states. To illustrate the methods, we investigate a three- and a four-state models in detail and apply the results to model the natural history of oncogenic genital human papillomavirus infections in women.

Clinical Trials as Topic↗
PubMed2004

Evaluation of log-rank tests for infrequent observations from a multi-state process, with application to HPV vaccine efficacy.

Genital infection by human papillomavirus (HPV) is a common sexually transmitted disease, with over 25 per cent prevalence among young women in the US. Infections are usually without symptoms and transient (or reversible), but a small proportion of infections persist and are believed to be responsible for nearly all cervical cancers and precursor lesions such as cervical intraepithelial neoplasia (CIN). Therefore, successful vaccines against persistent HPV infections could have a great impact in preventing cervical cancers. In trials being planned, ongoing, and recently completed, a log-rank or a similar test may be employed to assess a vaccine effect in comparison to placebo, with an infection 'event' defined to capture persistent but not transient infections. However, it is not clear how best to define such an event, because (1) diagnostic tests cannot distinguish a persistent from a transient infection, (2) participants are only examined periodically, and (3) there can be misclassification errors in the detection of infections. This paper evaluates several definitions of persistent infection that are based on periodically observed infection statuses by postulating a multi-state model for persistent and transient infections. The type I error and the power of tests on vaccine efficacy based on these operational definitions are then examined under various scenarios of how a vaccine might affect the infection-disease process. We find that none of the candidates performs satisfactorily, thus raising concerns that clinical trials based only on infection endpoints will not be reliable.

Biometry↗
PubMed2004

Comparisons of test statistics arising from marginal analyses of multivariate survival data.

We investigate the properties of several statistical tests for comparing treatment groups with respect to multivariate survival data, based on the marginal analysis approach introduced by Wei, Lin and Weissfeld ["Regression Analysis of multivariate incomplete failure time data by modelling marginal distributions," JASA vol. 84 pp. 1065-1073]. We consider two types of directional tests, based on a constrained maximization and on linear combinations of the unconstrained maximizer of the working likelihood function, and the omnibus test arising from the same working likelihood. The directional tests are members of a larger class of tests, from which an asymptotically optimal test can be found. We compare the asymptotic powers of the tests under general contiguous alternatives for a variety of settings, and also consider the choice of the number of survival times to include in the multivariate outcome. We illustrate the results with simulations and with the results from a clinical trial examining recurring opportunistic infections in persons with HIV.

AIDS-Related Opportunistic Infections↗
PubMed2004

Analyzing time-to-event data in a clinical trial when an unknown proportion of subjects has experienced the event at entry.

In some clinical trials, where the outcome is the time until development of a silent event, an unknown proportion of subjects who have already experienced the event will be unknowingly enrolled due to the imperfect nature of the diagnostic tests used to screen potential subjects. For example, commonly used diagnostic tests for evaluating HIV infection status in infants, such as DNA PCR and HIV Culture, have low sensitivity when given soon after infection. This can lead to the inclusion of an unknown proportion of HIV-infected infants into clinical trials aimed at the prevention of transmission from HIV-positive mothers to their infants through breastfeeding. The infection status of infants at the end of the trial, when they are more than a year of age, can be determined with certainty. For those infants found to be infected with HIV at the end of the trial, it cannot be determined whether this occurred during the study or whether they were already infected when they were enrolled. In these settings, estimates of the cumulative risk of the event by the end of the study will overestimate the true probability of event during the study period and hypothesis tests comparing two or more intervention strategies can also be biased. We present inference methods for the distribution of time until the event of interest in these settings, and investigate issues in the design of such trials when there is a choice of using both imperfect and perfect diagnostic tests.

Biometry↗
PubMed2004

Evaluating the role of human papillomavirus vaccine in cervical cancer prevention.

Persistent genital infection with human papillomavirus (HPV) is a natural candidate as a surrogate marker for cervical cancer because of the strong epidemiologic and molecular evidence that HPV infection is the causative agent for almost all cervical cancers. However, while infection with high-risk types of HPV appears to be necessary for the development of cervical cancer, most infections are controlled by host immune response and do not lead to cancer in the vast majority of infected women. Because diagnostic tests cannot distinguish a persistent infection in the pathogenesis of cervical cancer from a transient infection, it is difficult to describe the disease mechanism as a progressive process based on observations. Therefore, the disease pathogenesis pathway does not fit into the usual surrogate marker framework, raising practical concerns about using HPV infection as a surrogate for a clinical endpoint in vaccine trials. In this paper, we describe the challenges in defining HPV infection as a surrogate endpoint in a HPV vaccine trial that is aimed at reducing cervical cancer rates and examine potential effects of the vaccine. We then outline some issues in the design and analysis of HPV vaccine trials, including the use of operationally defined HPV infection events meant to capture persistent infections. We conclude with a recommendation for a multistate model that uses HPV infection to help explain the mechanisms of vaccine action rather than validate it as an endpoint substitute.

Clinical Trials as Topic↗
PubMed2003

Use of incomplete post-treatment data in the analysis of viral eradication studies.

In some studies of chronic viral infections where the objective is to estimate the distributions of time until viral eradication and viral resistance to treatment, patients must have treatment terminated in order to assess eradication status. Such patients then have their viral load continually monitored during a post-treatment period. If no virus is detected during this period, viral eradication is presumed to have occurred whereas detection of virus is interpreted to mean that the virus had been suppressed but not eradicated prior to treatment interruption. If the post-treatment period is long, as would be the case with diseases such as hepatitis C and HIV, there will be patients who have not completed the post-treatment period by the time the data are analysed. This paper proposes non-parametric and semi-parametric methods to incorporate partial post-treatment data in the estimation of the subdistributions of the time until eradication and resistance. The new methods extend previous methods for the analysis of eradication studies that do not account for incomplete post-treatment information, and are illustrated with data from a recent hepatitis C clinical trial.

Antiviral Agents↗
PubMed2003

Inference for a linear regression model with an interval-censored covariate.

Interval-censored observations of a response variable are a common occurrence in medical studies, and usually result when the response is the elapsed time until some event whose occurrence is periodically monitored. In this paper we consider a multivariate regression setting in which the explanatory variable is interval censored. Use of an ad hoc method of analysis for such data, such as taking the midpoint of the interval-censored covariate and applying ordinary least-squares, is not in general valid. We develop a likelihood approach, together with a two-step conditional algorithm, to jointly estimate the regression coefficients as well as the marginal distribution of the covariate. The resulting estimators are asymptotically normal. The performance of the method is assessed via simulations, and illustrated using data from a recent HIV/AIDS clinical trial to assess the association between waiting time between indinavir failure and subsequent viral load at enrolment. Extensions of the procedure to other parametric distributions are discussed.

Adult↗
PubMed2002

Durability of response to treatment among antiretroviral-experienced subjects: 48-week results from AIDS Clinical Trials Group Protocol 359.

The 24-week extension of AIDS Clinical Trials Group Protocol 359, a study of human immunodeficiency virus (HIV)-infected, indinavir-experienced patients, was designed to study the durability of "salvage" treatment regimens. Patients received saquinavir in combination with either ritonavir or nelfinavir and, in addition, delavirdine, adefovir, or both. Patients who demonstrated a virologic response at weeks 12-16 were eligible to continue therapy in the extension through week 48. Of the 105 eligible subjects who were enrolled in the extension, 86 (82%) completed 48 weeks, and 49 (57%) of those 86 had HIV RNA levels <or=500 copies/mL at week 48. For these 86 subjects who completed 48 weeks, the median change in CD4 cell count from baseline was +72 cells/mm(3). Greater body weight, higher CD4 cell count, and greater degree of phenotypic susceptibility to indinavir and saquinavir at baseline were significantly associated with durable virologic suppression. These results show that some patients who experience treatment failure can demonstrate durable virologic and immunologic responses with salvage antiretroviral regimens.

Antiviral Agents↗