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This paper presents methods of analysis for ordinal repeated measures. We use a generalization of the continuation ratio model with a random effect. We handle frailty by using a normal distribution and also a non-parametric distribution. The methodology is readily implemented in existing software and flexible enough to treat large data sets with uncommon time measurements as well as different numbers of repeated measures for each individual. The models are used to investigate how a group of explanatory variables influences the overall condition of patients treated for breast cancer.
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In anticancer drug development, the combined use of two drugs is an important strategy to achieve greater therapeutic success. Often combination studies are performed in animal (mostly mice) models before clinical trials are conducted. These experiments on mice are costly, especially with combination studies. However, experimental designs and sample size derivations for the joint action of drugs are not currently available except for a few cases where strong model assumptions are made. For example, Abdelbasit and Plackett proposed an optimal design assuming that the dose-response relationship follows some specified linear models. Tallarida et al. derived a design by fixing the mixture ratio and used a t-test to detect the simple similar action. The issue is that in reality we usually do not have enough information on the joint action of the two compounds before experiment and to understand their joint action is exactly our study goal. In this paper, we first propose a novel non-parametric model that does not impose such strong assumptions on the joint action. We then propose an experimental design for the joint action using uniform measure in this non-parametric model. This design is optimal in the sense that it reduces the variability in modelling synergy while allocating the doses to minimize the number of experimental units and to extract maximum information on the joint action of the compounds. Based on this design, we propose a robust F-test to detect departures from the simple similar action of two compounds and a method to determine sample sizes that are economically feasible. We illustrate the method with a study of the joint action of two new anticancer agents: temozolomide and irinotecan.
For event time data involving multiple mutually exclusive competing causes of failure, classic competing risks results show that marginal survival distributions are not identifiable. In a related instance, one or more failure modes may be observed provided that the failure events occur in a specific order. In such situations, sometimes referred to as semi-competing risks problems, the observations may under realistic assumptions lend information about parameters of interest that would be nonidentifiable in the strict competing risks case. Here, we present an approach that makes use of partially observable multiple modes of failures to obtain an estimate of the marginal distribution of one event type that may occur prior to the occurrence of another event type or be precluded by it. We apply the proposed method to the problem of estimating the distribution of time to tumour recurrence at specific sites among breast cancer patients participating in randomized clinical trials.
Surrogate endpoints have been defined by Prentice as response variables that can substitute for a 'true' endpoint for the purpose of comparing specific interventions or treatments in a clinical trial. The applicability of this definition, and of related surrogate endpoint criteria, is discussed, with emphasis on cancer and AIDS research settings. Auxiliary endpoints are defined as response variables, or covariates, that can strengthen true endpoint analyses. Specifically, such response variables provide some additional information on true endpoint occurrence times for study subjects having censored values for such times. Auxiliary variables will very frequently be available, and they may be able to be used without making additional strong assumptions. Approaches to the use of auxiliary variables using ideas based on augmented score and augmented likelihood methods are described.
OBJECTIVES: Preoperative knowledge of the depth of myometrial infiltration is important in patients with endometrial carcinoma. This study aimed at assessing the value of histopathological parameters obtained from an endometrial biopsy (Pipelle de Cornier; results available preoperatively) and ultrasound measurements obtained after transvaginal sonography with color Doppler imaging in the preoperative prediction of the depth of myometrial invasion, as determined by the final histopathological examination of the hysterectomy specimen (the gold standard). METHODS: We first collected ultrasound and histopathological data from 97 consecutive women with endometrial carcinoma and divided them into two groups according to surgical stage (Stages Ia and Ib vs. Stages Ic and higher). The areas (AUC) under the receiver-operating characteristics curves of the subjective assessment of depth of invasion by an experienced gynecologist and of the individual ultrasound parameters were calculated. Subsequently, we used these variables to train a logistic regression model and least squares support vector machines (LS-SVM) with linear and RBF (radial basis function) kernels. Finally, these models were validated prospectively on data from 76 new patients in order to make a preoperative prediction of the depth of invasion. RESULTS: Of all ultrasound parameters, the ratio of the endometrial and uterine volumes had the largest AUC (78%), while that of the subjective assessment was 79%. The AUCs of the blood flow indices were low (range, 51-64%). Stepwise logistic regression selected the degree of differentiation, the number of fibroids, the endometrial thickness and the volume of the tumor. Compared with the AUC of the subjective assessment (72%), prospective evaluation of the mathematical models resulted in a higher AUC for the LS-SVM model with an RBF kernel (77%), but this difference was not significant. CONCLUSIONS: Single morphological parameters do not improve the predictive power when compared with the subjective assessment of depth of myometrial invasion of endometrial cancer, and blood flow indices do not contribute to the prediction of stage. In this study an LS-SVM model with an RBF kernel gave the best prediction; while this might be more reliable than subjective assessment, confirmation by larger prospective studies is required.
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The contribution of both non-inherited (stochastic, random, environmental, and other non-inherited influences) and inherited factors (genetic and inherited epigenetic factors) to the variability of spontaneous lung metastasis formation in over 100 metastatic lines from each of three murine tumors was measured. The contribution of inherited and genetic sources of variability to metastasis formation was significantly greater than 0 in all cases, but only in the lines of sarcoma SANH was it the major influence on metastatic variability. In the sarcoma SA4020 and hepatocarcinoma HCA-1 lines, non-inherited factors accounted for the majority of the variation in spontaneous lung metastasis formation. A similar situation was also observed in the variability of the tumors with respect to the diameter doubling time. In conclusion, both non-inherited and genetic/inherited factors significantly influenced the formation of spontaneous metastases in the tumors examined. The significance of this finding for the cloning of metastatic genes is discussed.
Quantitative bioluminescence and single-photon imaging have been applied for mapping concentration distributions of metabolites, such as adenosine triphosphate (ATP), glucose and lactate, in biopsies of cervical cancers in patients. Biopsies were taken before a conventional radiation treatment, and a number of clinically relevant data, such as local tumor control, patient survival, metastatic spread and so forth, were documented. There was no correlation between staging or grading and any of the metabolic parameters measured. Local correlations between ATP, glucose and lactate on a pixel-to-pixel basis were generally positive, with respective Spearman's correlation coefficients less in patients without clinically documented metastasis compared with those with metastatic spread. Lactate concentrations were significantly higher and scattered over a wider range in tumors with metastatic spread in comparison to malignancies in patients without metastasis. Thus, high local lactate levels of > or = 20 mumole/g appear to be associated with a high risk of metastasis, at least in human cervical tumors.
With the proliferation of related microarray studies by independent groups, a natural step in the analysis of these gene expression data is to combine the results across these studies. However, this raises a variety of issues in the analysis of such data. In this article, we discuss the statistical issues of combining data from multiple gene expression studies. This leads to more complications than those in standard meta-analyses, including different experimental platforms, duplicate spots and complex data structures. We illustrate these ideas using data from four prostate cancer profiling studies. In addition, we develop a simple approach for assessing differential expression using the LASSO method. A combination of the results and the pathway databases are then used to generate candidate biological pathways for cancer.
The neural cell adhesion molecule (NCAM) is involved in the intercellular junctions of neurons and glial cells. We investigated its relevance as a biomarker in gliomas which main characteristic is their high invasiveness. We studied by Western blot the pattern of serum NCAM bands in patients with gliomas (n = 34), with brain metastasis of different primary cancers (n = 27) and with benign brain tumors (n = 22)] compared with healthy controls (n = 69). For densitometric analysis NCAM bands > or = 130 kDa (HMW) and <130 kDa (LMW) were clustered. We observed that glioma patients presented higher NCAM HMW and lower NCAM LMW levels than control subjects (P < 0.01). A similar pattern was found in patients with brain metastasis or brain benign tumors, suggesting that the pattern of serum NCAM bands would be useful to detect brain tumor pathology. On the other hand, serum NCAM expression was not associated with the main clinicopathological features of gliomas, including overall survival. Interestingly, we found that 9/12 patients with glioma showed a significant decrease in NCAM HMW/LMW ratio between 1-3 months after successful tumor removal. Thus, serum NCAM could be a useful marker for monitoring treatment.NCAM expression was also analyzed at tissular level in 59 glioma sections from paraffined tumors. We observed that NCAM immunostaining was inversely correlated with the histological grade of malignancy, remaining this association in a multivariate analysis. Besides, loss of NCAM staining was significantly associated with bad prognosis in an univariate analysis.
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A stage-shift cancer screening model is developed in the context of a randomized controlled trial (RCT) of cancer screening. In the model, detection by screening causes the time of diagnosis of the cancer to be advanced so that either the stage at diagnosis is shifted from one stage to the next lower one or the stage of diagnosis is unchanged but the cancer is diagnosed earlier in the stage. These are called external and internal stage shifts, respectively. At each stage the extent of the external and internal shifts and any associated mortality benefits are estimated. Further, the model allows the interrelationships of these benefits within and between stages to be delineated. This then allows us to better understand the results of the RCT. Data from a completed breast cancer screening RCT are used to illustrate the application of the model and its value in improving our understanding of the trial's results.
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