Semiparametric inference for a two-stage outcome-dependent sampling design with interval-censored failure time data.

This thesis considers the problem of treatment comparisons when only interval-censored failure time data are available.This type of data occurs frequently in clinical trials and other follow-up studies.We study several nonparametric procedures developed previously and compare them under different situations.In particular, we study the situation where the difference between the groups occurs at an early or late time period.For this problem, we generalize the log-rank tests developed for interval-censored data in Zhao and Sun ( 2004) and the weighted log-rank test presented in Kalbfleisch (2002).Numerical studies are conducted to evaluate the proposed test and compare it with the unweighted log-rank test, which indicate that the proposed method works well.This thesis also considerers the problem of finding an appropriate sample size to achieve a desired power.We present a simple-to-use formula to find the sample size for a prespecified power and level of significance for the case of interval-censored data.Since many researchers use missing data techniques such as imputation along with right censored methods to analyze interval-censored data, we also compare an imputed Kaplan-Meier Estimate of the survival function to Turnbull's Self Consistent Estimate.We present a large numerical study to show that these estimates often disagree at late time points when the mean survival time is large.

<abstract><p>Interval-censored failure time data as a general type of survival data often arises in medicine and other applied fields. Survival tree is a flexible predictive method for survival data because no specific assumptions are required.</p> <p>Generalized Log-Rank Test have good power with parameters for interval-censored failure time data. We construct a special test statistic of Generalized Log-Rank Tests, and propose a new survival tree with hyper-parameter by combining the test statistic with Conditional Inference Framework for interval-censored failure time data. The effect of tuning hyper-parameter are discussed and hyper-parameter tuning allows the tree method to be more general and flexible. Thus the tree method either improve upon or remain competitive with existing tree method for interval-censored failure time data-ICtree, which is a special case of ours. An extensive simulation is executed to assess the predictive performance of our tree methods. Finally, the tree methods are applied to a tooth emergence data.</p></abstract>

Interval-censored failure time data commonly occur in many periodic follow-up studies such as epidemiological experiments, medical studies and clinical trials. By intervalcensored data, we usually mean that one cannot observe the failure time of interest and instead we know that it belongs to a time interval. Correlated failure time data commonly occur when there are multiple events on one individual or when the study subjects are clustered into some small groups. In this situation, study subjects from same subgroup or the failure events from same individuals are usually regarded as dependent, but the subjects in different clusters or failure events from different individuals are assumed to be independent. Besides the correlation between the cluster, sometimes the cluster size may be informative or carry some information about the failure time of interest. Cured subgroup is another interesting topic that has been discussed by many authors. For this situation, unlike the assumptions in traditional survival model that all study subjects would experience the failure event of interest eventually if the follow-up time is long enough, some subjects may never experience or not be susceptible to the event. Such subjects are treated as cured and assumed to belong to a cured subgroup in a study population. The research in this dissertation focuses on regression analysis of correlated intervalcensored data with a cured subgroup via different approaches based on different data structures. In the first part of this dissertation, we discuss clustered interval-censored data with a cured subgroup and informative cluster size. To address this, we present a within-cluster resampling method and in the approach, the multiple imputation procedure is applied for estimation of unknown parameters. To assess the performance of the proposed method, a simulation study is conducted and suggests that it works well in practical situations. Also, the method is applied to a set of real data that motivated this study. In the second part of this dissertation, we consider the clustered interval-censored data with a cured subgroup via a non-mixture cure model. We present a maximum likelihood estimation procedure under the semiparametric transformation nonmixture cure model. To estimate the unknown parameters, an expectation maximization (EM) algorithm based on an augmentation of Poisson variable is developed. To assess the performance of the proposed method, a simulation study is conducted and suggests that it works well in practical situations. An application to a study conducted by the National Aeronautics and Space Administration that motivated this study is also provided. In the third part of this dissertation, we investigate the bivariate interval-censored data with a cured subgroup. A sieve maximum likelihood estimation procedure under the semiparametric transformation non-mixture cure model based on Bernstein polynomials is presented. A simulation study is conducted to assess the finite sample performance of the proposed method and suggests that the proposed model works well. Also, a real data application from the study of AIDS Clinical Trial Group 181 is provided.

Regression analysis of interval-censored failure time data commonly arises in biomedical studies, particularly when the available sample size is limited. Although many methods have been proposed for the semiparametric probit model with interval-censored data, there does not appear to exist an established approach that effectively borrows information from external sources to improve estimation efficiency. Such external information may arise, for example, in clinical trials where an auxiliary dataset from a related population is available but may differ from the target population in certain aspects, leading to heterogeneity between populations. To address this issue, a sieve maximum likelihood estimation procedure is developed for the semiparametric probit model with interval-censored data, and a transfer learning method is proposed to leverage auxiliary information from a source domain to improve estimation efficiency in the target domain while accounting for population heterogeneity. The proposed approach is based on a penalized likelihood formulation and uses monotone splines to approximate the unknown baseline function, providing flexibility in both modeling and computation. Simulation studies show that the proposed estimator substantially improves estimation accuracy compared with methods that rely solely on the target data, particularly when the target sample size is small. An application to an Alzheimer’s disease dataset further illustrates the practical usefulness of the proposed approach in biomedical studies.

This paper considers comparison of discrete failure time distributions when the survival time of interest measures elapsed time between two related events and observations on the occurrences of both events could be interval-censored. This kind of data is often referred to as doubly interval-censored failure time data. If the occurrence of the first event defining the survival time can be exactly observed, the data are usually referred to as interval-censored data. For the comparison problem based on interval-censored failure time data, Sun (1996) proposed a nonparametric test procedure. In this paper we generalize the procedure given in Sun (1996) to doubly interval-censored data case and the generalized test is evaluated by simulations.

It has attracted a great deal of interest in randomized survival studies to assess the causal treatment effect under non-compliance with time-to-event outcomes. In this paper, the problem when one faces case–cohort studies for which covariates are usually too expensive to be measured or obtained for the full cohort and also the disease rate is generally low is considered. Furthermore, only interval-censored data may be available for the failure event of interest, a situation for which there does not seem to exist an established estimation procedure. To address the problem, a sieve inverse probability weighting estimation procedure is proposed, and the resulting estimators are shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate the finite sample performance of the proposed approach and suggests that it works well in practice. In addition, the method is applied to a breast cancer screening study.

We propose a cost-effective sampling design and estimating procedure for validating personal risk models using right-censored cohort data. Validation involves using each subject's covariates, as ascertained at cohort entry, in a risk model (specified independently of the data) to assign him/her a probability of an adverse outcome within a future time period. Subjects are then grouped according to the magnitudes of their assigned risks, and within each group, the mean assigned risk is compared with the probability of outcome occurrence as estimated using the follow-up data. Such validation presents two complications. First, in the presence of right-censoring, estimating the probability of developing the outcomes before death requires competing risk analysis. Second, for rare outcomes, validation using the full cohort requires assembling covariates and assigning risks to thousands of subjects. This can be costly when some covariates involve analyzing biological specimens. A two-stage sampling design addresses this problem by assembling covariates and assigning risks only to those subjects most informative for estimating key parameters. We use this design to estimate the outcome probabilities needed to evaluate model performance and we provide theoretical and bootstrap estimates of their variances. We also describe how to choose two-stage designs with minimal efficiency loss for a parameter of interest when the quantities determining optimality are unknown at the time of design. We illustrate these methods by using subjects in the California Teachers Study to validate ovarian cancer risk models. We find that a design with optimal efficiency for one performance parameter need not be so for others, and trade-offs will be required. A two-stage design that samples all outcome-positive subjects and more outcome-negative than censored subjects will perform well in most circumstances. The methods are implemented in Risk Model Assessment Program, an R program freely available at http://med.stanford.edu/epidemiology/two-stage.html.

Regression analysis of interval-censored failure time data with time-dependent covariates

. Several non-parametric test procedures have been proposed for incomplete survival data: interval-censored failure time data. However, most of them have unknown asymptotic properties with heuristically derived and/or complicated variance estimation. This article presents a class of generalized log-rank tests for this type of survival data and establishes their asymptotics. The methods are evaluated using simulation studies and illustrated by a set of real data from a cancer study.

We adapted a rapid quality of care monitoring method to a fragile state with two aims: to assess the delivery of child health services in South Sudan at the time of independence and to strengthen local capacity to perform regular rapid health facility assessments. Using a two-stage lot quality assurance sampling (LQAS) design, we conducted a national cross-sectional survey among 156 randomly selected health facilities in 10 states. In each of these facilities, we obtained information on a range of access, input, process and performance indicators during structured interviews and observations. Quality of care was poor with all states failing to achieve the 80% target for 14 of 19 indicators. For example, only 12% of facilities were classified as acceptable for their adequate utilisation by the population for sick-child consultations, 16% for staffing, 3% for having infection control supplies available and 0% for having all child care guidelines. Health worker performance was categorised as acceptable in only 6% of cases related to sick-child assessments, 38% related to medical treatment for the given diagnosis and 33% related to patient counselling on how to administer the prescribed drugs. Best performance was recorded for availability of in-service training and supervision, for seven and ten states, respectively. Despite ongoing instability, the Ministry of Health developed capacity to use LQAS for measuring quality of care nationally and state-by-state, which will support efficient and equitable resource allocation. Overall, our data revealed a desperate need for improving the quality of care in all states.

Non-parametric tests of independence, as well as accompanying measures of association, are essential tools for the analysis of bivariate data. Such tests and measures have been developed for uncensored and right censored failure time data, but have not been developed for interval censored failure time data. Bivariate interval censored data arise in AIDS studies in which screening tests for early signs of viral and bacterial infection are done at clinic visits. Because of missed clinic visits, the actual times of first positive screening tests are interval censored. To handle such data, we propose an extension of Kendall's coefficient of concordance. We apply it to data from an AIDS study that recorded times of shedding of cytomegalovirus (CMV) and times of colonization of mycobacterium avium complex (MAC). We examine the performance of our proposed measure through a simulation study. Copyright © 1999 John Wiley & Sons, Ltd.

In stratified random sampling, Ahsan et al. [Mixed allocation in stratified sampling. Aligarh J Stat. 2005;25:87–97] introduced the concept of ‘Mixed allocation’ for fixed cost by minimizing the variance of the stratified sample mean which was based on the work of Clark and Steel [Optimum allocation of sample to strata and stages with simple additional constraints. Statistician. 2000;49(2):197–207] for two-stage univariate sampling design. The present manuscript studies the problem of obtaining a ‘compromise mixed allocation’ in multivariate stratified sampling using Fuzzy Programming Technique. To exhibit the application of the defined approach, a numerical illustration is considered and solved using Lingo 13.0. In order to demonstrate the superiority of proposed approach, the obtained results have been compared with four other existing approaches.

Tackling malnutrition is a major global health priority for a developing country like Bangladesh. The main purpose of the study was to know the current malnutrition status of children under five years of age in the Chattogram division. A two-stage stratified random sampling design was used to collect the Bangladesh Demographic and Health Survey data, 2022-2023, from which 1446 children aged 0 - 59 months and their mothers were included in this study according to the study area. The association between the selected factors and nutritional status was assessed, and logistic regression models were fitted for the three indicators, and a Chi-square test was performed to find the association of outcome variables with selected socio-demographic factors. Results reveal that about 31.88% of children were stunted, 18.53% of children were wasted, and 28.63% of children were underweight in the Chattogram division. Prevalence of stunting, wasting and underweight was lowest among children aged 0 - 15 months and highest at the age of 16 to 30 months. The major contributing factors for under-five malnutrition were found to be child age, mothers’ education, fathers’ occupation, wealth index, birth order number, place of residence, mother’s antenatal care taken during and after pregnancy, partner’s occupation were significant factors for child malnutrition. Prevention of malnutrition in Bangladesh should, therefore, be seen as a significant public health issue and given top priority.

Background: Complete Vaccination has made a significant contribution to the prevention of infectious diseases among children. However, not all children were completely vaccinated. This study aimed to determine the vaccination coverage, timeliness, and maternal-related factors associated with incomplete vaccination of children in Miagao, Iloilo, Philippines.Methods: A cross-sectional study of children aged 12-59 months at the time the study was conducted between April 15 and 26, 2019 in Miagao, Iloilo. A total of 515 children were selected using a two-stage random sampling design. The data were collected from the Target Client Lists records, encoded, and analyzed using SPSS v21. Binary logistic regression was used to determine the factors associated with incomplete vaccination. Crude and adjusted odds ratio (AOR) with their confidence interval were reported.Results: The complete vaccination coverage among children was low at 28.7%. Among those completely vaccinated, 2.7% to 88.5% had untimely Vaccination. Significant factors associated with incomplete Vaccination were children born at home (AOR=2.1; 95% CI: 1.32-3.36) and mothers who had two or less postnatal visits (AOR=3.4; 95% CI: 1.39-8.44). Age and number of antenatal visits of the mothers did not influence the outcome.Conclusions: Maternal related factors significantly associated with incomplete vaccination were the place of child delivery and the number of postnatal visits of mothers. The study recommends educating mothers on the importance of postnatal visits and enhancing maternal health services, especially the promotion of facility-based delivery.

Seroprevalence and Determinants of Helicobacter pylori Infection in the Hispanic Community Health Study/Study of Latinos