Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials

Some clinical trials follow a design where patients are randomized to a primary therapy at entry followed by another randomization to maintenance therapy contingent upon disease remission. Ideally, analysis would allow different treatment policies, i.e., combinations of primary and maintenance therapy if specified up-front, to be compared. Standard practice is to conduct separate analyses for the primary and follow-up treatments, which does not address this issue directly. We propose consistent estimators for the survival distribution and mean restricted survival time for each treatment policy in such two-stage studies and derive large-sample properties. The methods are demonstrated on a leukemia clinical trial data set and through simulation.     

The Chain Ratio Estimator and Regression Estimator with Linear Combination of Two Auxiliary Variables

In sample surveys, it is usual to make use of auxiliary information to increase the precision of the estimators. We propose a new chain ratio estimator and regression estimator of a finite population mean using linear combination of two auxiliary variables and obtain the mean squared error (MSE) equations for the proposed estimators. We find theoretical conditions that make proposed estimators more efficient than the traditional multivariate ratio estimator and the regression estimator using information of two auxiliary variables.     

Ratio estimation with measurement error in the auxiliary variate

Summary .  With auxiliary information that is well correlated with the primary variable of interest, ratio estimation of the finite population total may be much more efficient than alternative estimators that do not make use of the auxiliary variate. The well-known properties of ratio estimators are perturbed when the auxiliary variate is measured with error. In this contribution we examine the effect of measurement error in the auxiliary variate on the design-based statistical properties of three common ratio estimators. We examine the case of systematic measurement error as well as measurement error that varies according to a fixed distribution. Aside from presenting expressions for the bias and variance of these estimators when they are contaminated with measurement error we provide numerical results based on a specific population. Under systematic measurement error, the biasing effect is asymmetric around zero, and precision may be improved or degraded depending on the magnitude of the error. Under variable measurement error, bias of the conventional ratio-of-means estimator increased slightly with increasing error dispersion, but far less than the increased bias of the conventional mean-of-ratios estimator. In similar fashion, the variance of the mean-of-ratios estimator incurs a greater loss of precision with increasing error dispersion compared with the other estimators we examine. Overall, the ratio-of-means estimator appears to be remarkably resistant to the effects of measurement error in the auxiliary variate.     

Correcting for noncompliance and dependent censoring in an AIDS Clinical Trial with inverse probability of censoring weighted (IPCW) log-rank tests

AIDS Clinical Trial Group (ACTG) randomized trial 021 compared the effect of bactrim versus aerosolized pentamidine (AP) as prophylaxis therapy for pneumocystis pneumonia (PCP) in AIDS patients. Although patients randomized to the bactrim arm experienced a significant delay in time to PCP, the survival experience in the two arms was not significantly different (p = .32). In this paper, we present evidence that bactrim therapy improves survival but that the standard intent-to-treat comparison failed to detect this survival advantage because a large fraction of the subjects either crossed over to the other therapy or stopped therapy altogether. We obtain our evidence of a beneficial bactrim effect on survival by artificially regarding the subjects as dependently censored at the first time the subject either stops or switches therapy; we then analyze the data with the inverse probability of censoring weighted Kaplan-Meier and Cox partial likelihood estimators of Robins (1993, Proceedings of the Biopharmaceutical Section, American Statistical Association, pp. 24-33) that adjust for dependent censoring by utilizing data collected on time-dependent prognostic factors.     

Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

Summary .  In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach.     

A New Exponential Ratio-Type Estimator with Linear Combination of Two Auxiliary Variables

In sample surveys, it is usual to make use of auxiliary information to increase the precision of estimators. We propose a new exponential ratio-type estimator of a finite population mean using linear combination of two auxiliary variables and obtain mean square error (MSE) equation for proposed estimator. We find theoretical conditions that make proposed estimator more efficient than traditional multivariate ratio estimator using information of two auxiliary variables, the estimator of Bahl and Tuteja and the estimator proposed by Abu-Dayeh et al. In addition, we support these theoretical results with the aid of two numerical examples.     

Improving efficiency of inferences in randomized clinical trials using auxiliary covariates

Summary .   The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two-arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds ratios or log odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods.     

Noniterative adjustment to regression estimators with population-based auxiliary information for semiparametric models

Disease registries, surveillance data, and other datasets with extremely large sample sizes become increasingly available in providing population-based information on disease incidence, survival probability, or other important public health characteristics. Such information can be leveraged in studies that collect detailed measurements but with smaller sample sizes. In contrast to recent proposals that formulate additional information as constraints in optimization problems, we develop a general framework to construct simple estimators that update the usual regression estimators with some functionals of data that incorporate the additional information. We consider general settings that incorporate nuisance parameters in the auxiliary information, non-i.i.d. data such as those from case-control studies, and semiparametric models with infinite-dimensional parameters common in survival analysis. Details of several important data and sampling settings are provided with numerical examples.     

Cox regression methods for two-stage randomization designs

Two-stage randomization designs (TSRD) are becoming increasingly common in oncology and AIDS clinical trials as they make more efficient use of study participants to examine therapeutic regimens. In these designs patients are initially randomized to an induction treatment, followed by randomization to a maintenance treatment conditional on their induction response and consent to further study treatment. Broader acceptance of TSRDs in drug development may hinge on the ability to make appropriate intent-to-treat type inference within this design framework as to whether an experimental induction regimen is better than a standard induction regimen when maintenance treatment is fixed. Recently Lunceford, Davidian, and Tsiatis (2002, Biometrics 58, 48-57) introduced an inverse probability weighting based analytical framework for estimating survival distributions and mean restricted survival times, as well as for comparing treatment policies at landmarks in the TSRD setting. In practice Cox regression is widely used and in this article we extend the analytical framework of Lunceford et al. (2002) to derive a consistent estimator for the log hazard in the Cox model and a robust score test to compare treatment policies. Large sample properties of these methods are derived, illustrated via a simulation study, and applied to a TSRD clinical trial.     

Judgement post-stratification for designed experiments

Summary .   In many scientific studies, information that is not easily translated into covariates is ignored in the analysis. However, this type of information may significantly improve inference. In this research, we apply the idea of judgment post-stratification to utilize such information. Specifically, we consider experiments that are conducted under a completely randomized design. Sets of experimental units are formed, and the units in a set are ranked. Estimation is performed conditional on the sets and ranks. We propose a new estimator for a treatment contrast. We improve the new estimator by Rao–Blackwellization. Asymptotic distribution theory and corresponding inferential procedures for both estimators are developed. Simulation studies quantify the superiority of the new estimators and show their desirable properties for small and moderate sample sizes. The impact of the new techniques is illustrated with data from a clinical trial.     

Censored linear regression in the presence or absence of auxiliary survival information

There has been a rising interest in better exploiting auxiliary summary information from large databases in the analysis of smaller-scale studies that collect more comprehensive patient-level information. The purpose of this paper is twofold: first, we propose a novel approach to synthesize information from both the aggregate summary statistics and the individual-level data in censored linear regression. We show that the auxiliary information amounts to a system of nonsmooth estimating equations and thus can be combined with the conventional weighted log-rank estimating equations by using the generalized method of moments (GMM) approach. The proposed methodology can be further extended to account for the potential inconsistency in information from different sources. Second, in the absence of auxiliary information, we propose to improve estimation efficiency by combining the overidentified weighted log-rank estimating equations with different weight functions via the GMM framework. To deal with the nonsmooth GMM-type objective functions, we develop an asymptotics-guided algorithm for parameter and variance estimation. We establish the asymptotic normality of the proposed GMM-type estimators. Simulation studies show that the proposed estimators can yield substantial efficiency gain over the conventional weighted log-rank estimators. The proposed methods are applied to a pancreatic cancer study for illustration.     

A Product-Type Estimator in Double Sampling

The use of ratio and product estimators, using auxiliary information, for estimating the mean of a finite population is well known. The efficiency of ratio estimator or product estimator is high depending on whether the auxiliary character is highly positively or negatively coorelated with the main character of interest. This paper proposes a product-type estimator which is more efficient than the usual ratio and product estimators in practical situations. We consider the case of double sampling from which the single sampling results may easily be derived.     

Combination of microtubule targeting agents with other antineoplastics for cancer treatment

Microtubule targeting agents (MTAs) have attracted extensive attention for cancer treatment. However, their clinical efficacies are limited by intolerable toxicities, inadequate efficacy and acquired multidrug resistance. The combination of MTAs with other antineoplastics has become an efficient strategy to lower the toxicities, overcome resistance and improve the efficacies for cancer treatment. In this article, we review the combinations of MTAs with some other anticancer drugs, such as cytotoxic agents, kinases inhibitors, histone deacetylase inhibitors, immune checkpoints inhibitors, to overcome these obstacles. We strongly believe that this review will provide helpful information for combination therapy based on MTAs.     

An Improved Class of Estimators for Finite Population Mean in Sample Surveys

This paper proposes a class of estimators for estimating the finite population mean -Y of a study variate y using information on two auxiliary variates, one of which is positively and the other negatively correlated with the study variate y. An “asymptotically optimum estimator” (AOE) in the class is identified with its bias and mean square error formulae. It is observed that the proposed AOE is more efficient than Srivastava (1965), Srivastava (1974), Prasad (1989) and Gandge , Varghese , and Prabhu-Ajgaonkar (1993) estimators.     

Improved Generalized Ratio-Cum-Product Estimation

In this paper, a generalized ratio-cum-product estimator for estimating the ratio (product) of two population means using auxiliary information on two other variables is given of which the estimators by SINGH (1969) and SHAH and SHAH (1978) are particular cases. The estimator is regeneralized when the covariance between two auxiliary variables is known.     

Apelin and stem cells: the role played in the cardiovascular system and energy metabolism

Apelin, a member of the adipokine family, is widely distributed in the body and exerts cytoprotective effects on many organs. Apelin isoforms are involved in different physiological processes, including regulation of the cardiovascular system, cardiac contractility, angiogenesis, and energy metabolism. Several investigations have been performed to study the effect of apelin on stem cell therapy. This review aims to summarize the literature representing the effects of apelin on stem cell properties. Furthermore, this review discusses the therapeutic potential of apelin‐treated stem cells for cardiovascular diseases and demonstrates the effect of stem cells overexpressing apelin on energy metabolism. Stem cells with their unique characteristics play a crucial role in the maintenance of tissue integrity. These cells participate in tissue regeneration via multiple mechanisms. Although preclinical and clinical studies have demonstrated the therapeutic potential of stem cells in various diseases, their application in regenerative medicine has not been efficient. A number of strategies such as genetic modification or treatment of stem cells with different factors have been used to improve the efficacy of cell therapy and to increase their survival after transplantation. This article reviews the effect of apelin treatment on the efficacy of cell therapy.     

Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals

We consider methods for causal inference in randomized trials nested within cohorts of trial‐eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite‐sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial‐eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.     

Importance of the stem cell microenvironment for ophthalmological cell-based therapy

Cell therapy is a promising treatment for diseases that are caused by cell degeneration or death. The cells for clinical transplantation are usually obtained by culturing healthy allogeneic or exogenous tissue invitro. However, for diseases of the eye, obtaining the adequate number of cells for clinical transplantation is difficult due to the small size of tissue donors and the frequent needs of long-term amplification of cells in vitro, which results in low cell viability after transplantation. In addition, the transplanted cells often develop fibrosis or degrade and have very low survival. Embryonic stem cells(ESCs) and induced pluripotent stem cells(i PS) are also promising candidates for cell therapy. Unfortunately, the differentiation of ESCs can bring immune rejection, tumorigenicity and undesired differentiated cells, limiting its clinical application. Although i PS cells can avoid the risk of immune rejection caused by ES cell differentiation post-transplantation, the low conversion rate, the risk of tumor formation and the potentially unpredictable biological changes that could occur through genetic manipulation hinder its clinical application. Thus, the desired clinical effect of cell therapy is impaired by these factors. Recent research findings recognize that the reason for low survival of the implanted cells not only depends on the seeded cells, but also on the cell microenvironment, which determines the cell survival, proliferation and even reverse differentiation. When used for cell therapy, the transplanted cells need a specific three-dimensional structure to anchor and specific extra cellular matrix components in addition to relevant cytokine signaling to transfer the required information to support their growth. These structures present in the matrix in which the stem cells reside are known as the stem cell microenvironment. The microenvironment interaction with the stem cells provides the necessary homeostasis for cell maintenance and growth. A large number of studies suggest that to explore how to reconstruct the stem cell microenvironment and strengthen its combination with the transplanted cells are key steps to successful cell therapy. In this review, we will describe the interactions of the stem cell microenvironment with the stem cells, discuss the importance of the stem cell microenvironment for cell-based therapy in ocular diseases, and introduce the progress of stem cell-basedtherapy for ocular diseases.     

Results in children of acute lymphoid leukaemia protocol ICIG-ALL 9 consisting of chemotherapy for only nine months followed by active immunotherapy

Summary Protocol ICIG-ALL 9 with only nine months' remission chemotherapy followed by active immunotherapy has given a proportion of about 50% of the patients on the plateau of the first remission curve, while 60% of the children are on the plateau of survival curve.These results do not differ from those of another protocol (ICIG-ALL 10) conducted on an identical population of patients and comprising a 25 month remission chemotherapy before immunotherapy.This observation, confirmed by a randomized trial of the EORTC Haemopathy Working Party, suggests that between the 9th and the 25th month, active immunotherapy is as efficient as maintenance chemotherapy.The overall results of this protocol with short chemotherapy followed by active immunotherapy have been compared with those of another prolonged maintenance chemotherapy before immunotherapy protocol (ICIG-ALL 11), and with published protocols comprising only long maintenance chemotherapy: protocol 9 is, as far as the first remission plateau and the survival plateau are concerned, superior to most of these protocols (if not all their branches).Lethal toxicity of active immunotherapy is nil, in contrast to the proportion of deaths (4–28%) occurring during remission in the patients submitted to maintenance chemotherapy.However, not all patients with so-called acute lymphoid leukaemias should be treated identically: our early prognosis parameters (WHO cytological types and volume of the tumour, in this study) allow us to distinguish a good prognosis group in which protocol 9 gave an 80% cure expectancy.The patients with a poor prognosis should be the object of further research for a more efficient therapy. Even if this should be more intensive, the risk is justified in this group, while it is not so for the good prognosis group.     

Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation

In this work, we fit pattern-mixture models to data sets with responses that are potentially missing not at random (MNAR, Little and Rubin, 1987). In estimating the regression parameters that are identifiable, we use the pseudo maximum likelihood method based on exponential families. This procedure provides consistent estimators when the mean structure is correctly specified for each pattern, with further information on the variance structure giving an efficient estimator. The proposed method can be used to handle a variety of continuous and discrete outcomes. A test built on this approach is also developed for model simplification in order to improve efficiency. Simulations are carried out to compare the proposed estimation procedure with other methods. In combination with sensitivity analysis, our approach can be used to fit parsimonious semi-parametric pattern-mixture models to outcomes that are potentially MNAR. We apply the proposed method to an epidemiologic cohort study to examine cognition decline among elderly.