Analysis of times to repeated events in two-arm randomized trials with noncompliance and dependent censoring

This article develops randomization-based methods for times to repeated events in two-arm randomized trials with noncompliance and dependent censoring. Structural accelerated failure time models are assumed to capture causal effects on repeated event times and dependent censoring time, but the dependence structure among repeated event times and dependent censoring time is unspecified. Artificial censoring techniques to accommodate nonrandom noncompliance and dependent censoring are proposed. Estimation of the acceleration parameters are based on rank-based estimating functions. A simulation study is conducted to evaluate the performance of the developed methods. An illustration of the methods using data from an acute myeloid leukemia trial is provided.     

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.     

Breast cancer prevention--clinical trials strategies involving aromatase inhibitors

Estrogens and their metabolites have been implicated in both the initiation and the prevention of breast cancer. The reduction in breast cancer incidence seen in the tamoxifen arms of the four prospective trials to date has established the proof of principle that antagonizing estrogen is a potential means of reducing breast cancer risk. However, the areas to improve on these results include: (a) enhanced efficacy, (b) reduction in the incidence of receptor-negative tumors, (c) improved overall and endocrinological side effects, and (d) improved function on end-organs other than the breast. The aromatase inhibitors offer the potential to achieve these goals in part in the following ways: (a) greater reduction in risk of disease as evidenced by superior efficacy in advanced breast cancer and by inhibition of both initiation and promotion of breast cancer, (b) reduction in receptor-negative tumors by synergy with COX-2 inhibitors resulting in growth factor inhibition, anti-angiogenesis and inhibition of tumor-associated aromatase expression, (c) fewer vasomotor and urogenital abnormalities, and (d) reduced thromboembolism and cardiovascular complications and satisfactory effects on bone metabolism. Important differences may exist between non-steroidal reversible inhibitors and steroidal irreversible inactivators in particular related to the androgenic/anabolic effects of the steroidal inactivators. Pilot studies of aromatase inhibitors described elsewhere in this session have begun in healthy women with dense mammography, or a high-risk genetic and/or histocytopathologic profile, to determine potential efficacy, as well as effects on end-organ function. A number of phase three trials with aromatase inhibitors are also underway or in planning. Among these are the BRCA 1 and 2 study of exemestane versus placebo in unaffected postmenopausal carriers, the International Breast Intervention Study 2 (IBIS 2) of anastrozole versus placebo in women with a high-risk profile, and the National Cancer Institute of Canada’s Clinical Trial Group (NCIC CTG) study of exemestane with or without celecoxib versus placebo in women at risk of the disease. For premenopausal women, combination strategies of gonadotrophin agonists and aromatase inhibitors are being investigated. The potential of using low doses of aromatase inhibitors to lower “in breast” estrogen levels without unduly perturbing plasma concentrations is also being explored. The potential of the aromatase gene functioning as an oncogene within the breast may be tied to breast density which in turn may represent both a selection tool for elevated risk and an intermediate marker of prevention. The strong link between postmenopausal estrogen levels and breast cancer risk suggests the possibility that plasma estrogen levels may be a useful intermediate marker of prevention. The aromatase inhibitors offer us the first ever tool to render women virtually free of estrogen and are potentially an exciting tool for the prevention of breast cancer.     

Seamlessly expanding a randomized phase II trial to phase III

A sequential Bayesian phase II/III design is proposed for comparative clinical trials. The design is based on both survival time and discrete early events that may be related to survival and assumes a parametric mixture model. Phase II involves a small number of centers. Patients are randomized between treatments throughout, and sequential decisions are based on predictive probabilities of concluding superiority of the experimental treatment. Whether to stop early, continue, or shift into phase III is assessed repeatedly in phase II. Phase III begins when additional institutions are incorporated into the ongoing phase II trial. Simulation studies in the context of a non-small-cell lung cancer trial indicate that the proposed method maintains overall size and power while usually requiring substantially smaller sample size and shorter trial duration when compared with conventional group-sequential phase III designs.     

Self-designing two-stage trials to minimize expected costs

In the design of clinical trials, the sample size for the trial is traditionally calculated from estimates of parameters of interest, such as the mean treatment effect, which can often be inaccurate. However, recalculation of the sample size based on an estimate of the parameter of interest that uses accumulating data from the trial can lead to inflation of the overall Type I error rate of the trial. The self-designing method of Fisher, also known as the variance-spending method, allows the use of all accumulating data in a sequential trial (including the estimated treatment effect) in determining the sample size for the next stage of the trial without inflating the Type I error rate. We propose a self-designing group sequential procedure to minimize the expected total cost of a trial. Cost is an important parameter to consider in the statistical design of clinical trials due to limited financial resources. Using Bayesian decision theory on the accumulating data, the design specifies sequentially the optimal sample size and proportion of the test statistic's variance needed for each stage of a trial to minimize the expected cost of the trial. The optimality is with respect to a prior distribution on the parameter of interest. Results are presented for a simple two-stage trial. This method can extend to nonmonetary costs, such as ethical costs or quality-adjusted life years.     

Retrospective analysis of the relationships among the test environments of the Southern Queensland sugarcane breeding programme

Repeatability of aspects of genotype by environment (GxE) interactions is an important factor to be assessed in designing more efficient selection programmes. Sugar yield data from multi environment trials (METs) which were part of the sugarcane breeding programme in southern Queensland were analysed. Data were obtained from 71 environments consisting of trials planted from 1986 to 1989. Retrospective analysis on these data was conducted to assess the repeatability of the clone by environment (CxE) interactions over locations and years. This analysis focussed on identifying similarities among test environments in the way they discriminated among clones for sugar yield. Analyses of variance and pattern analyses on environments over years based on standardised data were conducted. The pattern analyses were done sequentially according to the accumulated data sets over years. Squared Euclidean distances among environments were averaged over data sets and years before pattern analyses across the data sets were conducted. A graphical methodology was developed to present the results of the cumulative historical analysis. CxE interactions of a magnitude which affected selection decisions were present in each data set studied. Pattern analyses on cumulative data sets identified environmental groupings that were based on geographical positions. Each location generated a different pattern of discrimination among the clones. These results emphasised the importance of clone by location (CxL) interactions in southern Queensland and the need to concentrate more on testing across locations than on ratooning ability within a location. The classifications identified similarities among ratoon crops within a location, differences among locations and differences between ratoon crops and their plant crop (PC). This suggested that some aspects of CxL and clone by crop-year (CxY) interactions were repeatable across years. The potential applications of these results to increase efficiency of the sugarcane breeding programme, such as the possibility of applying indirect selection among environments generating similar discrimination among clones, are discussed.Abbreviations GxE Genotype-by-environment interactions - METs multi-environment trials - CxE clone-by-en-vironment interactions - CxL clone-by-location interactions - PC plant crop - CxY clone-by-crop-year interactions - CxLxF clone-by-location-by-crop-year interactions - SYT substation yield trials - BSES Bureau of SugarExperiment Stations     

Weighted finite population sampling to maximize entropy

Attention is drawn to a method of sampling a finite populationof N units with unequal probabilities and without replacement.The method was originally proposed by Stern & Cover (1989)as a model for lotteries. The method can be characterized asmaximizing entropy given coverage probabilities _i, or equivalentlyas having the probability of a selected sample proportionalto the product of a set of ‘weights’ w_i. We showthe essential uniqueness of the w_i given the _i. We present twomethods for stepwise selection of sampling units, and correspondingschemes for removal of units that can be used in connectionwith sample rotation. Inclusion probabilities of any order canbe written explicitly in closed form. Second-order inclusionprobabilities _ij satisfy the condition O < _ij < _ij, whichguarantees Yates & Grundy‘s variance estimator tobe unbiased, definable for all samples and always nonnegativefor any sample size.     

烟青虫(Helicoverpa assulta Guenée)信息素的结构鉴定及田间试验(英语)

北京地区的烟青虫(Helicover Pa asslta Cucn(?)e)的性信息素腺体提取物经毛细管柱气相色谱分析及GC MS分析,鉴定了6种组分.这6种组分为:十六醛(16:Ald)、顺 9—十六烯醛(Z9—16:Ald)、顺11—十六烯醛(Zll 16:Ald)、顺9—十六烯醇(Z9-16:OH)、顺11—十六烯醇(Zll-16:OH)、顺9—十六烯基乙酸酯(Z9 16:OAc),比例为10.9:58.7:3.9:14.7:1.1:10.7.田间试验表明,只有16:Ald、Z9 16:Ald和Zll 16:Ald(比例为15.3:79.2:5.5)组成的三组分诱芯和Z9—16:Ald和Zll—16:Ald(比例为93.4:6.6)组成的两组分诱芯对于雄蛾有强烈的引诱活性.在3种醛为组分的诱芯中加入Z9 16:OH明显地降低引诱活性.     

Comparison of ITS RFLP patterns of Gracilaria (Rhodophyceae, Gracilariales) populations from Chile and New Zealand and an examination of interfertility of Chilean morphotypes

Restriction fragment length polymorphism (RFLP) patterns of the internal transcribed spacer (ITS) of the nuclear ribosomal cistron and crossability trials were used to characterize four morphotypes of Gracilaria from Lenga, Isla Santa María and Maullín, Chile, and two morphotypes from sites in New Zealand. PCR products from all Chilean morphotypes resulted in a major single band of ca. 1198 bp. ITS-RFLP profiles generated with the restriction enzymes Cla I, Hae III, Pst I, Hha I, Rsa I and Taq I, were identical in all cases. All crosses within, as well as between, morphotypes resulted in cystocarp differentiation, with the production of viable carpospores. Based upon these data, it is concluded that the four morphotypes from Chile correspond to a single species, G. chilensis, and that the ITS-RFLP pattern is a useful marker to predict genetic relatedness at the specific level in Gracilaria. A comparison of the ITS-RFLP patterns of the Chilean morphotypes with the patterns of two samples of G. chilensis from New Zealand revealed that the sample from Scorching Bay, Wellington, fits the Chilean ITS-RFLP patterns. The population from Blockhouse Bay, Auckland, appears to correspond to another species. This revised version was published online in July 2006 with corrections to the Cover Date.     

Views on artificial intelligence (AI) assisted clinical trials

It is of interest to document the views of medical professionals on the application of artificial intelligence (using known data for the prediction of unknown events) in clinical trials using a web survery with a structured questionnaire from 377 subjects. The questionnaire contained 17 statements which were categorised into awareness (1,2 statements), perception (3-10 statements) and opinion (11-17 statements). The data obtained was compared between the subjects using two tailed Fisher''s exact test with p-value <0.05 for data significance analysis. Data shows that majority of professionals have possitive views on the application of artificial intelligence in clinical trials. This will accelarrate the drug evaluation process. However, the use of emerging tools such as AI will not replace human subjects in this context.