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Proportional odds models are commonly used to model ordinal responses, but the proportional odds assumption may not hold in practice, leading to biased inference. Tests such as score, Wald and likelihood ratio (LR) have been proposed to evaluate the proportional odds assumption based on models without the assumption. Brant has proposed an independent binary model-based Wald-type test, and Wolfe and Gould have extended the idea to propose an LR-type test. This paper provides a brief review of the Brant and Wolfe-Gould tests for evaluating the proportional odds assumption and evaluates their performance through simulation studies and a real data example. Sample programs are provided in SAS, SPSS and Stata to facilitate the implementation of these tests using standard statistical software packages. This study highlights the importance of evaluating the proportional odds assumption when using proportional odds models for ordinal responses. The sample programs provided in this paper make it easy for researchers to apply these tests in their own analyses using standard statistical software packages. [ABSTRACT FROM AUTHOR]

Although logistic regression is the most popular for modelling regression relationships with binary responses, many find relative risk (RR), or risk ratio, easier to interpret and prefer to use this measure of risk in regression analysis. Indeed, since Zou published his modified Poisson regression approach for modelling RR for cross-sectional data, his paper has been cited over 7 000 times, demonstrating the popularity of this alternative measure of risk in regression analysis involving binary responses. As longitudinal studies have become increasingly popular in clinical trials and observational studies, it is imperative to extend Zou's approach for longitudinal data. The two most popular approaches for longitudinal data analysis are the generalised linear mixed-effects model (GLMM) and generalised estimating equations (GEE). However, the parametric GLMM cannot be used for the extension within the current context, because Zou's approach treats the binary response as a Poisson variable, which is at odds with the Bernoulli distribution for the binary response. On the other hand, as it imposes no mathematical model on data distributions, the semiparametric GEE is coherent with Zou's modified Poisson regression. In this paper, we develop a GEE-based longitudinal model for binary responses to provide inference about RR. [ABSTRACT FROM AUTHOR]

Incentivised by breakthroughs and data generated by the high-throughput sequencing technology, this paper proposes a distance-based framework to fulfil the emerging needs in elucidating insights from the high-dimensional microbiome data in psychiatric studies. By shifting focus from traditional methods that focus on the observations from each subject to the between-subject attributes that aggregate two or more subjects' entire feature vectors, the described approach revolutionises the conventional prescription for high-dimensional observations via microbiome diversity. To this end, we enrich the classical generalised linear models to articulate the multivariable regression relationship between distance-based variables. We also discuss a robust and computationally feasible semiparametric inference technique. Benefitting from the latest advances in the semiparametric efficiency theory for such attributes, the proposed estimators enjoy robustness and good asymptotic properties that guarantee sensitivity in detecting signals between clinical outcomes and microbiome diversity. It offers a readily implementable and easily interpretable solution for deciphering the gut-brain axis in mental health research. [ABSTRACT FROM AUTHOR]

In this White Paper, we discuss the current state of microbial cancer therapy. This paper resulted from a meeting ('Microbial Based Cancer Therapy') at the US National Cancer Institute in the summer of 2017. Here, we define 'Microbial Therapy' to include both oncolytic viral therapy and bacterial anticancer therapy. Both of these fields exploit tumor-specific infectious microbes to treat cancer, have similar mechanisms of action, and are facing similar challenges to commercialization. We designed this paper to nucleate this growing field of microbial therapeutics and increase interactions between researchers in it and related fields. The authors of this paper include many primary researchers in this field. In this paper, we discuss the potential, status and opportunities for microbial therapy as well as strategies attempted to date and important questions that need to be addressed. The main areas that we think will have the greatest impact are immune stimulation, control of efficacy, control of delivery, and safety. There is much excitement about the potential of this field to treat currently intractable cancer. Much of the potential exists because these therapies utilize unique mechanisms of action, difficult to achieve with other biological or small molecule drugs. By better understanding and controlling these mechanisms, we will create new therapies that will become integral components of cancer care. [ABSTRACT FROM AUTHOR]

Nicotine use is more prevalent in patients with psychiatric disorders, especially those diagnosed with psychotic illnesses. Previously, this higher prevalence has been partially attributed to the potential ameliorative effects of nicotine on symptom severity and cognitive impairment. Some healthcare professionals and patients perceive there is a beneficial effect of nicotine on mental health. Emerging data show that the harm associated with nicotine in the population of patients with mental health conditions outweighs any potential benefit. This paper will review the evidence surrounding the nicotinic system and schizophrenia, with a focus on any causality between nicotine and psychosis. [ABSTRACT FROM AUTHOR]

The rapid development of immuno-oncology (I-O) therapies for multiple types of cancer has transformed the cancer treatment landscape and brightened the long-term outlook for many patients with advanced cancer. Responding to ongoing efforts to generate value assessments for novel therapies, multiple stakeholders have been considering the question of "What makes I-O transformative?" Evaluating the distinct features and attributes of these therapies, and better characterizing how patients experience them, will inform such assessments. This paper defines ways in which treatment with I-O is different from other therapies. It also proposes key aspects and attributes of I-O therapies that should be considered in any assessment of their value and seeks to address evidence gaps in existing value frameworks given the unique properties of patient outcomes with I-O therapy. The paper concludes with a "data needs catalogue" (DNC) predicated on the belief that multiple key, unique elements that are necessary to fully characterize the value of I-O therapies are not routinely or robustly measured in current clinical practice or reimbursement databases and are infrequently captured in existing research studies. A better characterization of the benefit of I-O treatment will allow a more thorough assessment of its benefits and provide a template for the design and prioritization of future clinical trials and a roadmap for healthcare insurers to optimize coverage for patients with cancers eligible for I-O therapy. [ABSTRACT FROM AUTHOR]

*: BackgroundGenetic instability is known to relate with carcinogenesis by providing tumors with a mechanism for fast adaptation. However, mounting evidence also indicates causal relation between genetic instability and improved cancer prognosis resulting from efficient immune response. Highly unstable tumors seem to accumulate mutational burdens that result in dynamical landscapes of neoantigen production, eventually inducing acute immune recognition. How are tumor instability and enhanced immune response related? An important step towards future developments involving combined therapies would benefit from unraveling this connection. *: MethodsIn this paper we present a minimal mathematical model to describe the ecological interactions that couple tumor adaptation and immune recognition while making use of available experimental estimates of relevant parameters. The possible evolutionary trade-offs associated to both cancer replication and T cell response are analysed, and the roles of mutational load and immune activation in governing prognosis are studied. *: ResultsModeling and available data indicate that cancer-clearance states become attainable when both mutational load and immune migration are enhanced. Furthermore, the model predicts the presence of well-defined transitions towards tumor control and eradication after increases in genetic instability numerically consistent with recent experiments of tumor control after Mismatch Repair knockout in mice. *: ConclusionsThese two main results indicate a potential role of genetic instability as a driver of transitions towards immune control of tumors, as well as the effectiveness of increasing mutational loads prior to adoptive cell therapies. This mathematical framework is therefore a quantitative step towards predicting the outcomes of combined therapies where genetic instability might play a key role. [ABSTRACT FROM AUTHOR]

Tumor immunotherapy using gene-modified T cells has already met with considerable success in the treatment of metastatic melanoma and B cell lymphoma. With improving patient prognoses, new questions arise. In particular, the long-term consequences of treatment among individuals of childbearing age could now be considered. Former patients can carry a cohort of transgenic memory T cells long after treatment has ceased and the effector T cell population has contracted. When patients become parents well after treatment is completed, expectant mothers may still pass transgenic T cells to their unborn children. Consequences should be more measurable if the mother also breastfeeds the baby. Maternal T cells may shape immune responses in the child, can tolerize the child to maternal antigens, and might cause either beneficial or adverse effects in the offspring. The hypothesis put forth is that transgenic T cells transferred from mother to child during and after pregnancy might have consequences that have not been adequately considered to date. Depending on the targeted antigen and the MHC eventually required to present it, such transfer may be beneficial, uneventful or even damaging. Such potential consequences are addressed in this paper. The transgenic T cells might form a pocket of memory T cells in secondary lymphoid organs of the child, expand upon antigen stimulation, and react. However, simple measures might be devised to avoid any reason for concern. These considerations provide ample incentive to probe transgenerational transfer of transgenic T cells. [ABSTRACT FROM AUTHOR]

The therapeutic landscape in metastatic melanoma has changed dramatically in the last decade, with the success of immune checkpoint inhibitors resulting in durable responses for a large number of patients. For patients with BRAF mutations, combinations of BRAF and MEK inhibitors demonstrated response rates and benefit comparable to those from immune checkpoint inhibitors, providing the rationale for sequential treatment with targeted and immunotherapies and raising the question of optimal treatment sequencing. Biomarkers for the selection of anti-PD-1 therapy in BRAF wild type (BRAF WT) and in BRAF mutated (BRAF MUT) patients help development of alternative treatments for patients unlikely to benefit, and might lead to better understanding of the interaction of checkpoint inhibition and targeted therapy. In this paper we evaluate the performance of a previously developed serum proteomic test, BDX008, in metastatic melanoma patients treated with anti-PD-1 agents and investigate the role of BRAF mutation status. BDX008, a pre-treatment proteomic test associated with acute phase reactants, wound healing and complement activation, stratifies patients into two groups, BDX008+ and BDX008-, with better and worse outcomes on immunotherapy. Serum samples were available from 71 patients treated with anti-PD1 inhibitors; 25 patients had BRAF mutations, 39 were wild type. Overall, BDX008+ patients had significantly better overall survival (OS) (HR = 0.50, P = 0.016) and a trend for better progression-free survival (PFS) (HR = 0.61, P = 0.060) than BDX008- patients. BDX008 classification was statistically significant in the analyses adjusted for mutation status, LDH, and line of treatment (P = 0.009 for OS and 0.031 for PFS). BRAF WT BDX008+ patients had markedly long median OS of 32.5 months and 53% landmark 2 years survival, with statistically significantly superior OS as compared to BDX008- patients (HR = 0.41, P = 0.032). The difference between BDX008+ and BDX008- in PFS in BRAF WT patients and in OS and PFS in BRAF MUT patients did not reach statistical significance, though numerically was consistent with overall results. The test demonstrated significant interaction with neutrophil-to-lymphocyte ratio (NLR) (PFS P = 0.041, OS P = 0.004). BDX008 as a biomarker selecting for benefit from immune checkpoint blockade, especially in patients with wild type BRAF and in subgroups with low NLR, warrants further evaluation. [ABSTRACT FROM AUTHOR]

Background: In oncology clinical development, objective response rate, disease control rate and early tumor size changes are commonly used as efficacy metrics for early decision-making. However, for immunotherapy trials, it is unclear whether these early efficacy metrics are still predictive of long-term clinical benefit such as overall survival. The goal of this paper is to identify appropriate early efficacy metrics predictive of overall survival for immunotherapy trials. Methods: Based on several checkpoint blockade based immunotherapy studies in metastatic melanoma, we evaluated the predictive value of early tumor size changes and RECIST-based efficacy metrics at various time points on overall survival. The cut-off values for tumor size changes to predict survival were explored via tree based recursive partitioning and validated by external data. Sensitivity analyses were performed for the cut-offs. Results: The continuous tumor size change metric and RECIST-based trichotomized response metric at different landmark time points were found to be statistically significantly associated with overall survival. The predictive values were higher at Week 12 and 18 than those at Week 24. The percentage of tumor size changes appeared to have comparable or lower predictive values than the RECIST-based trichotomized metric, and a cut-off of approximately 10% tumor reduction appeared to be reasonable for predicting survival. Conclusions: An approximate 10% tumor reduction may be a reasonable cut-off for early decision-making while the RECIST-based efficacy metric remains the primary tool. Early landmark analysis is especially useful for decision making when accrual is fast. Composite response rate (utilizing different weights for PR/CR and SD) may be worth further investigation. [ABSTRACT FROM AUTHOR]

SAS and other popular statistical packages provide support for survey data with sampling weights. For example, PROC MEANS and PROC LOGISTIC in SAS have their counterparts PROC SURVEYMEANS and PROC SURVEYLOGISTIC to facilitate analysis of data from complex survey studies. On the other hand, PROC MEANS and many other classic SAS procedures also provide an option for including weights and yield identical point estimates, but different standard errors (SEs), as their corresponding survey procedures. This paper takes an in-depth look at different types of weights and provides guidance on use of different SAS procedures. [ABSTRACT FROM AUTHOR]

Background Therapeutic drug monitoring guides clinical individualised medication by measuring plasma concentration, which could improve the curative effect, avoid drug overdose and reduce the incidence of adverse reactions. At present, there are few reports on the clinical detection of venlafaxine and its active metabolite O-desmethylvenlafaxine. In this paper, the detection method of venlafaxine and O-desmethylvenlafaxine in blood plasma was established, which provides an effective and convenient means for guiding clinical application of medication. Aim To establish a method for determination of venlafaxine and its active metabolite O-desmethylvenlafaxine in human plasma by highperformance liquid chromatography with fluorescence detection. Methods Chromatographic separation was achieved on an Agilent Eclipse XDB-C18 Column (4.6×150 mm, 5 µm) with water containing sodium dihydrogen phosphate (0.05 mol/L) and acetonitrile (72:28) as the mobile phases. The following parameters were employed: flow rate 0.5 mL/min, column temperature 30°C, fluorescence excitation wavelength 276 nm and emission wavelength 598 nm. Results The method showed good linearity in the concentration range 10-1000 ng/mL. The regression equation for venlafaxine was R=0.0054C+0.0264, r2=0.99991. The regression equation for O-desmethylvenlafaxine was R=0.0034C+0.0272, r2=0.99969. The intraday and interday precisions (relative SD) were less than 10%, and the quantitative limit was 10 ng/mL. Conclusion We established a sensitive, specific and simple method for the detection of venlafaxine and O-desmethylvenlafaxine. This method fully meets the needs of clinical trials of venlafaxine and the requirements of relevant guidelines. It provided a reference for the clinical detection of venlafaxine and O-desmethylvenlafaxine plasma concentrations and pharmacokinetic study. [ABSTRACT FROM AUTHOR]

The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery. [ABSTRACT FROM AUTHOR]

The use of immunomodulation to treat malignancies has seen a recent explosion in interest. The therapeutic appeal of these treatments is far reaching, and many new applications continue to evolve. In particular, immune modulating drugs have the potential to enhance the systemic anticancer immune effects induced by locoregional thermal ablation. The immune responses induced by ablation monotherapy are well documented, but independently they tend to be incapable of evoking a robust antitumor response. By adding immunomodulators to traditional ablative techniques, several researchers have sought to amplify the induced immune response and trigger systemic antitumor activity. This paper summarizes the work done in animal models to investigate the immune effects induced by the combination of ablative therapy and immunomodulation. Combination therapy with radiofrequency ablation, cryoablation, and microwave ablation are all reviewed, and special attention has been paid to the addition of checkpoint blockades. [ABSTRACT FROM AUTHOR]

Prostate cancer is the most commonly diagnosed malignancy and second leading cause of cancer death among men in the United States. In recent years, several new agents, including cancer immunotherapies, have been approved or are currently being investigated in late-stage clinical trials for the management of advanced prostate cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) convened a multidisciplinary panel, including physicians, nurses, and patient advocates, to develop consensus recommendations for the clinical application of immunotherapy for prostate cancer patients. To do so, a systematic literature search was performed to identify high-impact papers from 2006 until 2014 and was further supplemented with literature provided by the panel. Results from the consensus panel voting and discussion as well as the literature review were used to rate supporting evidence and generate recommendations for the use of immunotherapy in prostate cancer patients. Sipuleucel-T, an autologous dendritic cell vaccine, is the first and currently only immunotherapeutic agent approved for the clinical management of metastatic castrate resistant prostate cancer (mCRPC). The consensus panel utilized this model to discuss immunotherapy in the treatment of prostate cancer, issues related to patient selection, monitoring of patients during and post treatment, and sequence/combination with other anti-cancer treatments. Potential immunotherapies emerging from late-stage clinical trials are also discussed. As immunotherapy evolves as a therapeutic option for the treatment of prostate cancer, these recommendations will be updated accordingly. [ABSTRACT FROM AUTHOR]

Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. [ABSTRACT FROM AUTHOR]

been suggested as a safe and promising treatment for auditory hallucinations, however, no systematic review has been conducted to evaluate the effects of tDCS on auditory hallucinations (AH). Objective: To investigate the efficacy and safety of tDCS for auditory hallucinations among patients with schizophrenia. Methods: We searched relevant randomized controlled trials (RCTs) from PubMed, EMBASE, the Cochrane Library, Chinese National Knowledge Infrastructure, Chongqing VIP database for Chinese Technical Periodicals, WANFANG DATA, Chinese Biological Medical Literature Database, and Taiwan Electronic Periodical Services (TEPS) before February 13, 2016. Studies were selected based on pre-defined inclusion and exclusion criteria. The quality of each included study was assessed by the risk of bias table. The levels of evidence of primary outcomes were evaluated using GRADE criteria. Data synthesis was conducted using RevMan 5.3. Results: 304 papers were screened. Finally, three studies with a combined sample size of 87 patients were included in the meta-analysis. Two studies were classified as having 'low risk of bias', one study was classified as having 'unclear'. Inconsistent results and the overall level of evidence of primary outcome was graded as 'low'. Conclusions: The sample sizes of the published studies were small and the results were inconsistent. We could not draw any strong conclusions from these trials. Further high quality RCTs with large sample sizes are needed to assess the efficacy of tDCS for auditory hallucinations in patients with schizophrenia. [ABSTRACT FROM AUTHOR]

The relative risk, risk difference, and odds ratio are the three most commonly used measures for comparing the risk of disease between different groups. Although widely popular in biomedical and psychosocial research, the relationship among the three measures has not been clarified in the literature. Many researchers incorrectly assume a monotonic relationship, such that higher (or lower) values in one measure are associated with higher (or lower) values in the other measures. In this paper we discuss three theorems and provide examples demonstrating that this is not the case; there is no logical relationship between any of these measures. Researchers must be very cautious when implying a relationship between the different measures or when combining results of studies that use different measures of risk. [ABSTRACT FROM AUTHOR]

In this paper, we review recent advances in immuno-oncology with particular focus on liver, kidney, and bladder cancers as discussed at the 2nd meeting of the Campania Society of Oncology Immunotherapy (SCITO). [ABSTRACT FROM AUTHOR]

The p-value is the most widely used statistical concept in biomedical research. Recently, there are controversies over its utility and over the possible relationship between p-value misuse and the relatively high proportion of published medical research that cannot be replicated. In this paper, we introduce the p-value in layman's terms and explain its randomness and limitations. However, we also point out that the available alternatives to p-value suffer similar limitations. We conclude that using p values is a valid way to test the null and alternative hypotheses in clinical trials. However, using the p-value from a single statistical test to judge the scientific merit of a research project is a misuse of the p-value; the results of inference tests using p-values need to be integrated with secondary results and other data to arrive at clinically valid conclusions. Understanding the variability and limitations of the p-value is important for the interpretation of statistical results in research studies. p 值是生物医学研究中使用最广泛的统计学概 念。最近，学界关于p 值的效用以及p 值的滥用与已 发表的医学研究无法重复性较差之间可能存在的关联 性有一些争论。在本文中，我们以通俗易懂的方法介 绍p 值，并且解释它的随机性和局限性。然而，目前 提出其它能替代p 值的概念也有同样的局限。我们得 出了如下的结论：对于检验临床试验的中的零假设 (null hypothesis) 和替代假设 (alternative hypothesis) 来说， 使用 p 值是一种有效的方法。然而，仅仅利用从某单 一统计检验所得出的p 值来判断研究项目的科学价值 则是一种对p 值的滥用；为得到可信的临床研究结果， 我们需要将利用P 值得到的推断检验的结果与次要结 果以及其它数据进行整合。对于在研究中阐释统计结 果而言，了解p 值的多样性和局限性是至关重要的. [ABSTRACT FROM AUTHOR]

Partly due to the overlap of symptom groupings in DSM, psychiatric comorbidity is extremely common. One of the most common and difficult to manage comorbid conditions is the co-occurrence of bipolar disorder (BD) and obsessive compulsive disorder (OCD). However, the key nosological question about this condition – whether they are two distinct disorders or a subtype of one of the disorders – remains unresolved. In order to help address this unanswered question, we updated our recent systematic review, searching the electronic databases MEDLINE, Embase, and PsycINFO to specifically investigate the heredity in BD-OCD patients. We identified a total of 8 relevant papers, the majority of which found that, compared to non-BD-OCD patients, BD-OCD patients were more likely to have a family history for mood disorders and less likely to have a family history for OCD. These results support the view that the majority of cases of comorbid BD-OCD are, in fact, BD cases. If confirmed in larger, more focused studies, this conclusion would have important nosological and clinical implications. [ABSTRACT FROM AUTHOR]

The onset, course, and management of mental health problems typically occur over relatively long periods of time, so a substantial proportion of psychiatric research - particularly the research that can provide clear answers about the complex interaction of biological, psychological, and social factors - requires multiple assessments of individuals and the environments in which they live over time. However, many psychiatric researchers use incorrect statistical methods to analyze this type of longitudinal data, a problem that can result in unrecognized bias in analytic results and, thus, incorrect conclusions. This paper provides an introduction to the topic of longitudinal data analysis. It discusses the different dataset structures used in the analysis of longitudinal data, the classification and management of missing data, and methods of adjusting for intra-individual correlation when developing multivariate regression models using longitudinal data. [ABSTRACT FROM AUTHOR]

This paper provides a brief overview of meta-analysis (MA) with emphasis on classical fixedeffects and random-effects MA models. It illustrates the application of MA models with the open-source software R using publicly available data from five studies on lamotrigine to treat bipolar depression and finds that meta-analysis identifies a statistically significant advantage of lamotrigine over placebo that was not evident in the individual studies. [ABSTRACT FROM AUTHOR]

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure. [ABSTRACT FROM AUTHOR]

This paper reviews a revolution in our understanding of psychosis over the last 20 years. To a major extent, this has resulted from a process of cross-fertilization between psychosocial epidemiology and cognitive behavior therapy for psychosis (CBT-p). This encouraged complementary strategies for the acquisition and analysis of data. These include the use of a range of dependent variables related to psychosis, and the exploitation of data from cross-sectional and longitudinal epidemiological surveys, virtual reality experiments, experience sampling methodology, and treatment trials. The key element is to investigate social and psychological measures in relation to each other. This research has confirmed the role of the external social world in the development and persistence of psychotic disorder. In addition, several psychological drivers of psychotic experiences have been identified. There is now persuasive evidence that the influence of social factors in psychosis is significantly mediated by non-psychotic symptoms, particularly mood symptoms and other attributes of affect such as insomnia. Psychotic symptoms are also driven by reasoning biases such as jumping to conclusions and belief inflexibility, though little is known about social influences on such biases. It is now clear that there are many routes to psychosis and that it takes many forms. Treatment of all kinds should take account of this: the dependence of CBT-p on a detailed initial formulation in terms of psychological processes and social influences is an example of the required flexibility. Individual mediators are now being targeted in specific forms of CBT-p, with good effect. This in turn corroborates the hypothesized role of non-psychotic symptoms in mediation, and attests to the power of the approaches described. [ABSTRACT FROM AUTHOR]

In mental health and psychosocial studies it is often necessary to report on the between-rater agreement of measures used in the study. This paper discusses the concept of agreement, highlighting its fundamental difference from correlation. Several examples demonstrate how to compute the kappa coefficient - a popular statistic for measuring agreement - both by hand and by using statistical software packages such as SAS and SPSS. Real study data are used to illustrate how to use and interpret this coefficient in clinical research and practice. The article concludes with a discussion of the limitations of the coefficient. [ABSTRACT FROM AUTHOR]

Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient's time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual's response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data. [ABSTRACT FROM AUTHOR]

Summary: Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But semi-parametric models are quite sensitive to outlying observations, so the generated estimates are unreliable when study data includes outliers. In this situation, some researchers trim the extreme values prior to conducting the analysis, but the ad-hoc rules used for data trimming are based on subjective criteria so different methods of adjustment can yield different results. Rank regression provides a more objective approach to dealing with non-normal data that includes outliers. This paper uses simulated and real data to illustrate this useful regression approach for dealing with outliers and compares it to the results generated using classical regression models and semi-parametric regression models. [ABSTRACT FROM AUTHOR]

The rapid development of immuno-oncology (I-O) therapies for multiple types of cancer has transformed the cancer treatment landscape and brightened the long-term outlook for many patients with advanced cancer. Responding to ongoing efforts to generate value assessments for novel therapies, multiple stakeholders have been considering the question of "What makes I-O transformative?" Evaluating the distinct features and attributes of these therapies, and better characterizing how patients experience them, will inform such assessments. This paper defines ways in which treatment with I-O is different from other therapies. It also proposes key aspects and attributes of I-O therapies that should be considered in any assessment of their value and seeks to address evidence gaps in existing value frameworks given the unique properties of patient outcomes with I-O therapy. The paper concludes with a "data needs catalogue" (DNC) predicated on the belief that multiple key, unique elements that are necessary to fully characterize the value of I-O therapies are not routinely or robustly measured in current clinical practice or reimbursement databases and are infrequently captured in existing research studies. A better characterization of the benefit of I-O treatment will allow a more thorough assessment of its benefits and provide a template for the design and prioritization of future clinical trials and a roadmap for healthcare insurers to optimize coverage for patients with cancers eligible for I-O therapy. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various papers within the issue on topics including molecular genetic studies of attention deficit hyperactivity disorder (ADHD), review of several neuroimaging studies, and biostatistics in psychiatry.

Immune checkpoint blockers (ICB) have revolutionized cancer therapy. However, complete response is observed in a minority of patients and most patients develop immune-related adverse events (irAEs). These include colitis, which can be treated with anti-tumor necrosis factor (TNF) antibodies such as Infliximab. In a recent issue of the Journal for ImmunoTherapy of Cancer, Badran et al. reported that co-administering Infliximab together with ICB to five cancer patients prevents colitis recurrence, with four of them exhibiting overall disease stability. The basis for this treatment strategy stemmed from our pre-clinical demonstration that TNF contributes to resistance to anti-PD-1 therapy. In agreement with this concept, we have shown that TNF blockers improve the anti-tumor therapeutic activity of ICB in mice and based on these findings we are currently evaluating the combination in melanoma patients enrolled in the TICIMEL clinical trial. Herein, (i) we discuss the scientific rationale for combining anti-TNF and ICB in cancer patients, (ii) comment on the paper published by Badran et al. and (iii) provide the TICIMEL clinical trial design. [ABSTRACT FROM AUTHOR]

Tumor immunotherapy using gene-modified T cells has already met with considerable success in the treatment of metastatic melanoma and B cell lymphoma. With improving patient prognoses, new questions arise. In particular, the long-term consequences of treatment among individuals of childbearing age could now be considered. Former patients can carry a cohort of transgenic memory T cells long after treatment has ceased and the effector T cell population has contracted. When patients become parents well after treatment is completed, expectant mothers may still pass transgenic T cells to their unborn children. Consequences should be more measurable if the mother also breastfeeds the baby. Maternal T cells may shape immune responses in the child, can tolerize the child to maternal antigens, and might cause either beneficial or adverse effects in the offspring. The hypothesis put forth is that transgenic T cells transferred from mother to child during and after pregnancy might have consequences that have not been adequately considered to date. Depending on the targeted antigen and the MHC eventually required to present it, such transfer may be beneficial, uneventful or even damaging. Such potential consequences are addressed in this paper. The transgenic T cells might form a pocket of memory T cells in secondary lymphoid organs of the child, expand upon antigen stimulation, and react. However, simple measures might be devised to avoid any reason for concern. These considerations provide ample incentive to probe transgenerational transfer of transgenic T cells. [ABSTRACT FROM AUTHOR]

Background: In oncology clinical development, objective response rate, disease control rate and early tumor size changes are commonly used as efficacy metrics for early decision-making. However, for immunotherapy trials, it is unclear whether these early efficacy metrics are still predictive of long-term clinical benefit such as overall survival. The goal of this paper is to identify appropriate early efficacy metrics predictive of overall survival for immunotherapy trials. Methods: Based on several checkpoint blockade based immunotherapy studies in metastatic melanoma, we evaluated the predictive value of early tumor size changes and RECIST-based efficacy metrics at various time points on overall survival. The cut-off values for tumor size changes to predict survival were explored via tree based recursive partitioning and validated by external data. Sensitivity analyses were performed for the cut-offs. Results: The continuous tumor size change metric and RECIST-based trichotomized response metric at different landmark time points were found to be statistically significantly associated with overall survival. The predictive values were higher at Week 12 and 18 than those at Week 24. The percentage of tumor size changes appeared to have comparable or lower predictive values than the RECIST-based trichotomized metric, and a cut-off of approximately 10% tumor reduction appeared to be reasonable for predicting survival. Conclusions: An approximate 10% tumor reduction may be a reasonable cut-off for early decision-making while the RECIST-based efficacy metric remains the primary tool. Early landmark analysis is especially useful for decision making when accrual is fast. Composite response rate (utilizing different weights for PR/CR and SD) may be worth further investigation. Trial registration: Clinical trials gov, NCT01295827, Registered February 15, 2011; NCT01704287, Registered October 11, 2012; NCT01866319, Registered May 31, 2013. [ABSTRACT FROM AUTHOR]

Background: With numerous and fast approvals of different agents including immune checkpoint inhibitors, monoclonal antibodies, or chimeric antigen receptor (CAR) T-cell therapy, immunotherapy is now an established form of cancer treatment. These agents have demonstrated impressive clinical activity across many tumor types, but also revealed different toxicity profiles and mechanisms of action. The classic assumptions imposed by cytotoxic agents may no longer be applicable, requiring new strategies for dose selection and trial design. Description: This main goal of this article is to summarize and highlight main challenges of early-phase study design of immunotherapies from a statistical perspective. We compared the underlying toxicity and efficacy assumptions of cytotoxic versus immune-oncology agents, proposed novel endpoints to be included in the dose-selection process, and reviewed design considerations to be considered for early-phase trials. When available, references to software and/or web-based applications were also provided to ease the implementation. Throughout the paper, concrete examples from completed (pembrolizumab, nivolumab) or ongoing trials were used to motivate the main ideas including recommendation of alternative designs. Conclusion: Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining the optimal dose to be carried forward in later phases, incorporating additional endpoints in the dose selection process (PK, PD, immune-based biomarkers), developing personalized biomarker profiles, or testing drug combination therapies to improve efficacy and reduce toxicity. [ABSTRACT FROM AUTHOR]