Optimal conditional error functions for the control of conditional power

Ethical considerations and the competitive environment of clinical trials usually require that any given trial have sufficient power to detect a treatment advance. If at an interim analysis the available data are used to decide whether the trial is promising enough to be continued, investigators and sponsors often wish to have a high conditional power, which is the probability to reject the null hypothesis given the interim data and the alternative of interest. Under this requirement a design with interim sample size recalculation, which keeps the overall and conditional power at a prespecified value and preserves the overall type I error rate, is a reasonable alternative to a classical group sequential design, in which the conditional power is often too small. In this article two-stage designs with control of overall and conditional power are constructed that minimize the expected sample size, either for a simple point alternative or for a random mixture of alternatives given by a prior density for the efficacy parameter. The presented optimality result applies to trials with and without an interim hypothesis test; in addition, one can account for constraints such as a minimal sample size for the second stage. The optimal designs will be illustrated with an example, and will be compared to the frequently considered method of using the conditional type I error level of a group sequential design.     

On the control between cell-mediated, IgM and IgG immunity

An hypothesis is proposed here describing some of the conditions that determine the type of response an antigen will induce, and explaining how the induction of one type of immunity affects the induction of other types of immunity. In more detail, the hypothesis attempts to account for the following observations: Some antigens induce only cell-mediated immunity, whereas others can, under different conditions, induce either cell-mediated or humoral immunity. The humoral response to most antigens consists of an initial period of IgM antibody synthesis, followed by a period of IgG synthesis. Some polymeric antigens induce the synthesis of only IgM antibody. There is a tendency for the immune response to an antigen, at a particular time, to be exclusively of the cell-mediated, IgM or IgG type.The hypothesis may also be relevant to some observations that, I believe, have been incorrectly interpreted to mean that “tolerance” to some antigens requires the presence of T (thymus-derived) cells specific for these antigens. The hypothesis suggests teleological reasons for the existence of the different types of immunity. It also suggests ways of controlling the type of response an antigen induces.     

The consequences of spatial structure for the design and analysis of ecological field surveys

In ecological field surveys, observations are gathered at different spatial locations. The purpose may be to relate biological response variables (e.g., species abundances) to explanatory environmental variables (e.g., soil characteristics). In the absence of prior knowledge, ecologists have been taught to rely on systematic or random sampling designs. If there is prior knowledge about the spatial patterning of the explanatory variables, obtained from either previous surveys or a pilot study, can we use this information to optimize the sampling design in order to maximize our ability to detect the relationships between the response and explanatory variables?
The specific questions addressed in this paper are: a) What is the effect (type I error) of spatial autocorrelation on the statistical tests commonly used by ecologists to analyse field survey data? b) Can we eliminate, or at least minimize, the effect of spatial autocorrelation by the design of the survey? Are there designs that provide greater power for surveys, at least under certain circumstances? c) Can we eliminate or control for the effect of spatial autocorrelation during the analysis? To answer the last question, we compared regular regression analysis to a modified t‐test developed by Dutilleul for correlation coefficients in the presence of spatial autocorrelation.
Replicated surfaces (typically, 1000 of them) were simulated using different spatial parameters, and these surfaces were subjected to different sampling designs and methods of statistical analysis. The simulated surfaces may represent, for example, vegetation response to underlying environmental variation. This allowed us 1) to measure the frequency of type I error (the failure to reject the null hypothesis when in fact there is no effect of the environment on the response variable) and 2) to estimate the power of the different combinations of sampling designs and methods of statistical analysis (power is measured by the rate of rejection of the null hypothesis when an effect of the environment on the response variable has been created).
Our results indicate that: 1) Spatial autocorrelation in both the response and environmental variables affects the classical tests of significance of correlation or regression coefficients. Spatial autocorrelation in only one of the two variables does not affect the test of significance. 2) A broad‐scale spatial structure present in data has the same effect on the tests as spatial autocorrelation. When such a structure is present in one of the variables and autocorrelation is found in the other, or in both, the tests of significance have inflated rates of type I error. 3) Dutilleul's modified t‐test for the correlation coefficient, corrected for spatial autocorrelation, effectively corrects for spatial autocorrelation in the data. It also effectively corrects for the presence of deterministic structures, with or without spatial autocorrelation.
The presence of a broad‐scale deterministic structure may, in some cases, reduce the power of the modified t‐test.     

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient‐specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient‐specific combined therapy should be designed. Large‐scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.     

Why is there persistent disease despite biologic therapy? Importance of early intervention

This short article hypothesizes that the major reason for persistent disease despite biologic therapy is the inappropriately late timing of therapy with biologic agents. There is clear evidence to support this hypothesis. This short review will indicate that patients treated at an earlier phase of disease can achieve a clinical remission rate of 70% and a response rate of above 95%.The majority of studies of rheumatoid arthritis (RA) have shown that when patients with RA are treated with biologics, they achieve a remission rate of only 30% or less. There are many potential reasons why disease activity is incompletely suppressed by biologics. The most commonly used biologics are anti-cytokines, in particular those that block TNF alpha. Therefore, a logical reason for persistent disease activity is either incomplete blockade of the individual cytokine or the fact that multiple cytokines/alternative mechanisms are implicated in the pathogenesis. A pragmatic second reason is that the outcome measures used to measure response include elements other than disease activity/inflammation. Even when inflammation is completely switched off, these outcome measures may not normalize. An example of this is the reduced impact of inflammation-suppressive therapy on outcome measures in later disease, at a time when there is already extensive damage.This short article, representing a personal view, will examine the hypothesis that a major reason there is persistent disease activity is that anti-cytokine biologics are used inappropriately late in the disease. In patients with late disease, the disease activity score (DAS) reflects damage as well as inflammation and therapy has a large irreversible component. More controversially, the pathogenesis of the disease by this time may have evolved, so that the disease process is less reversible with the blockade of a single cytokine. For both of these reasons, TNF blockade will produce full benefits only when given early in the course of disease.     

Optimal adaptive two‐stage designs for phase II cancer clinical trials

In oncology, single‐arm two‐stage designs with binary endpoint are widely applied in phase II for the development of cytotoxic cancer therapies. Simon's optimal design with prefixed sample sizes in both stages minimizes the expected sample size under the null hypothesis and is one of the most popular designs. The search algorithms that are currently used to identify phase II designs showing prespecified characteristics are computationally intensive. For this reason, most authors impose restrictions on their search procedure. However, it remains unclear to what extent this approach influences the optimality of the resulting designs. This article describes an extension to fixed sample size phase II designs by allowing the sample size of stage two to depend on the number of responses observed in the first stage. Furthermore, we present a more efficient numerical algorithm that allows for an exhaustive search of designs. Comparisons between designs presented in the literature and the proposed optimal adaptive designs show that while the improvements are generally moderate, notable reductions in the average sample size can be achieved for specific parameter constellations when applying the new method and search strategy.     

SELEX技术在胰腺癌分子诊断及靶向治疗中应用的研究进展

胰腺癌由于起病隐匿,早期诊断率较低,临床治疗效果差,是目前预后最差的恶性肿瘤之一。目前,临床上尚缺乏有效的非创伤早期筛查胰腺癌的手段,因而胰腺癌的早期诊断和治疗显得尤为重要。近年来,指数富集配基的系统进化(SELEX)技术以其在其他疾病中所表现的应用价值为疾病的诊治提供了一个新的途径。对于缺乏有效确诊手段,发病隐匿且病死率高的胰腺癌而言,SELEX技术基于胰腺癌发病的分子机制,可以筛选出特异结合于胰腺癌分子靶标的适配体,对筛选所得适配体进一步化学修饰,可以实现分子水平成像及靶向治疗,进而达到胰腺癌早期诊治的目的,具有重要的临床意义。本文就SELEX技术在胰腺癌分子诊断及靶向治疗中的应用研究进展进行了综述。     

Why do cancer cells metastasize into particular organs?

Metastatic spread of tumor cells is one of the most common causes of death in cancer patients. Therefore, elucidation of the molecular mechanisms that underlie the formation of metastatic colonies has been one of the major objectives of cancer research during the last two decades. In this review we will mainly discuss the mechanisms that cause a malignant cell to grow at a given site rather than at other possible sites, taking into account experimental and clinical evidence published on the subject. As a whole this evidence tends to confirm the hypothesis that organ-specific colonization by malignant cells often follows very specific and close interactions between the cancer cell and the target organ, either in terms of specific cellular adhesion or growth promotion. In this paper we would like to underscore the fact that cellular adhesion, either specific or unspecific, is a necessary but, by itself, insufficient condition for the development of metastases. It is the ability of the tumor cells to grow at the site where they arrested that ultimately determines whether a metastatic colony develops or fails to develop at that site.     

Blinded sample size recalculation in multiple composite population designs with normal data and baseline adjustments

The increasing interest in subpopulation analysis has led to the development of various new trial designs and analysis methods in the fields of personalized medicine and targeted therapies. In this paper, subpopulations are defined in terms of an accumulation of disjoint population subsets and will therefore be called composite populations. The proposed trial design is applicable to any set of composite populations, considering normally distributed endpoints and random baseline covariates. Treatment effects for composite populations are tested by combining p-values, calculated on the subset levels, using the inverse normal combination function to generate test statistics for those composite populations while the closed testing procedure accounts for multiple testing. Critical boundaries for intersection hypothesis tests are derived using multivariate normal distributions, reflecting the joint distribution of composite population test statistics given no treatment effect exists. For sample size calculation and sample size, recalculation multivariate normal distributions are derived which describe the joint distribution of composite population test statistics under an assumed alternative hypothesis. Simulations demonstrate the absence of any practical relevant inflation of the type I error rate. The target power after sample size recalculation is typically met or close to being met.     

Statistical analysis of noninferiority trials with a rate ratio in small-sample matched-pair designs

Testing of noninferiority has become increasingly important in modern medicine as a means of comparing a new test procedure to a currently available test procedure. Asymptotic methods have recently been developed for analyzing noninferiority trials using rate ratios under the matched-pair design. In small samples, however, the performance of these asymptotic methods may not be reliable, and they are not recommended. In this article, we investigate alternative methods that are desirable for assessing noninferiority trials, using the rate ratio measure under small-sample matched-pair designs. In particular, we propose an exact and an approximate exact unconditional test, along with the corresponding confidence intervals based on the score statistic. The exact unconditional method guarantees the type I error rate will not exceed the nominal level. It is recommended for when strict control of type I error (protection against any inflated risk of accepting inferior treatments) is required. However, the exact method tends to be overly conservative (thus, less powerful) and computationally demanding. Via empirical studies, we demonstrate that the approximate exact score method, which is computationally simple to implement, controls the type I error rate reasonably well and has high power for hypothesis testing. On balance, the approximate exact method offers a very good alternative for analyzing correlated binary data from matched-pair designs with small sample sizes. We illustrate these methods using two real examples taken from a crossover study of soft lenses and a Pneumocystis carinii pneumonia study. We contrast the methods with a hypothetical example.     

c-erbB-2 in serum of patients receiving fractionated paclitaxel chemotherapy.

Humanized anti-c-erbB-2 antibodies (Herceptin) in a weekly schedule are a new therapeutic option for the treatment of c-erbB-2-positive, advanced breast cancer (ABC). Addition of Herceptin to first-line chemotherapy for c-erbB-2 overexpressing ABC increased anticancer activity in a randomized phase III trial. However, except from standard UICC response criteria, there are hitherto no recommendations as to how to monitor Herceptin therapy. In a therapy optimizing study with weekly dose-intensified paclitaxel monotherapy (schedule: 90 mg/m2 weekly x 6, q9w), we correlated the clinical course of stage IV breast cancer in UICC criteria with the course of the shed c-erbB-2 protein fragment and the CA 27.29 serum level. Serum samples were taken weekly from 35 patients to measure the serum c-erbB-2 and CA 27.29 protein levels over time. Up to now, 10 patients (28.5%) are c-erbB-2 positive (> 15 U/mL), with a median baseline protein expression of 65 U/mL. While the overall response rate in the study is 36%, the response rate among c-erbB-2-positive patients is 62%, indicating a high sensitivity of c-erbB-2 positive patients to dose-intense paclitaxel treatment. In all responders the c-erbB-2 serum level decreased below the detection limit either before the clinical diagnosis of response or by the end of the next cycle. However, the normalization of the c-erbB-2 serum level was not specific for responders as patients with stable or progressive disease presented normalized levels or a > 50% decrease of the baseline level, too. The courses of the c-erbB-2 protein levels correlated closely with the courses of CA 27.29. The decrease in the serum c-erbB-2 oncoprotein level might indicate a regression of c-erbB-2 positive tumor load. This may even happen in progressive disease according to UICC criteria when the c-erbB-2-negative tumor fraction progresses while the c-erbB-2-positive fraction is controlled. Another explanation would be that the mechanisms of c-erbB-2 shedding change under chemotherapy, with less of the c-erbB-2 protein fragment being released to the serum, which would make the c-erbB-2 positive tumor cells a better target for anti-c-erbB-2 antibody treatment.     

Drug discovery strategies to outer membrane targets in Gram-negative pathogens

This review will cover selected recent examples of drug discovery strategies which target the outer membrane (OM) of Gram-negative bacteria either by disruption of outer membrane function or by inhibition of essential gene products necessary for outer membrane assembly. Significant advances in pathway elucidation, structural biology and molecular inhibitor designs have created new opportunities for drug discovery within this target-class space.     

A cautionary note on exact unconditional inference for a difference between two independent binomial proportions

Fisher's exact test for comparing response proportions in a randomized experiment can be overly conservative when the group sizes are small or when the response proportions are close to zero or one. This is primarily because the null distribution of the test statistic becomes too discrete, a partial consequence of the inference being conditional on the total number of responders. Accordingly, exact unconditional procedures have gained in popularity, on the premise that power will increase because the null distribution of the test statistic will presumably be less discrete. However, we caution researchers that a poor choice of test statistic for exact unconditional inference can actually result in a substantially less powerful analysis than Fisher's conditional test. To illustrate, we study a real example and provide exact test size and power results for several competing tests, for both balanced and unbalanced designs. Our results reveal that Fisher's test generally outperforms exact unconditional tests based on using as the test statistic either the observed difference in proportions, or the observed difference divided by its estimated standard error under the alternative hypothesis, the latter for unbalanced designs only. On the other hand, the exact unconditional test based on the observed difference divided by its estimated standard error under the null hypothesis (score statistic) outperforms Fisher's test, and is recommended. Boschloo's test, in which the p-value from Fisher's test is used as the test statistic in an exact unconditional test, is uniformly more powerful than Fisher's test, and is also recommended.     

Tissue‐specific inactivation by cytosine deaminase/uracil phosphoribosyl transferase as a tool to study plant biology

Recent advances in the study of plant developmental and physiological responses have benefited from tissue‐specific approaches, revealing the role of some cell types in these processes. Such approaches have relied on the inactivation of target cells using either toxic compounds or deleterious genes; however, both tissue‐specific and truly inducible tools are lacking in order to precisely target a developmental window or specific growth response. We engineered the yeast fluorocytosine deaminase (FCY1) gene by creating a fusion with the bacterial uracil phosphoribosyl transferase (UPP) gene. The recombinant protein converts the precursor 5‐fluorocytosine (5‐FC) into 5‐fluorouracyl, a drug used in the treatment of a range of cancers, which triggers DNA and RNA damage. We expressed the FCY‐UPP gene construct in specific cell types using enhancer trap lines and promoters, demonstrating that this marker acts in a cell‐autonomous manner. We also showed that it can inactivate slow developmental processes like lateral root formation by targeting pericycle cells. It also revealed a role for the lateral root cap and the epidermis in controlling root growth, a faster response. The 5‐FC precursor acts systemically, as demonstrated by its ability to inhibit stomatal movements when supplied to the roots in combination with a guard cell‐specific promoter. Finally, we demonstrate that the tissular inactivation is reversible, and can therefore be used to synchronize plant responses or to determine cell type‐specific functions during different developmental stages. This tool will greatly enhance our capacity to understand the respective role of each cell type in plant physiology and development.     

An evaluation of silver-stage American Eel conspecific chemical cueing during outmigration

American Eel Anguilla rostrata abundance has declined in recent decades, in part because sexually maturing, silver-stage adults, outmigrating from freshwater to oceanic spawning grounds, encounter migratory blockades or perish when passing through active hydroelectric turbines. To help improve downstream passage effectiveness and increase survival rates, the role of silver-stage American Eel conspecific chemical cueing during outmigration was investigated using a new type of bioassay. Inside a laboratory flume, downstream-swimming eels were exposed to both live (putative attractant) and dead (putative repellent) conspecific washings to determine whether their trajectory of downstream movement, level of activity, or time spent inside targeted areas of the arena changed after exposure. Silver eels were not attracted to or repulsed by either odor, as none of five scoring metrics indicated a behavioral response. Results did not support the hypothesis that conspecific chemical cueing is a mechanism for downstream migration coordination or danger avoidance; however responses may not have been readily apparent in this type of assay. Fisheries managers may opt to focus future research on more feasible restoration efforts using alternate experimental designs to remedy this ecological issue.     

Coagulation and inflammation in cancer: Limitations and prospects for treatment

The development of so-called immune checkpoint inhibitors (ICIs), which target specific molecular processes of tumour growth, has had a transformative effect on cancer treatment. Widespread use of antibody-based medicines to inhibit tumour cell immune evasion by modulating T cell responses is becoming more common. Despite this, response rates are still low, and secondary resistance is an issue that arises often. In addition, a wide range of serious adverse effects is triggered by enhancing the immunological response. As a result of an increased mortality rate, a higher prevalence of thrombotic complications is connected with an increased incidence of immunological reactions, complement activation, and skin toxicity. This suggests that the tumour microenvironment's interaction between coagulation and inflammation is important at every stage of the tumour's life cycle. The coagulation system's function in tumour formation is the topic of this review. By better understanding the molecular mechanisms in which tumour cells circulate, plasmatic coagulation and immune system cells are engaged, new therapy options for cancer sufferers may be discovered.