pinecone/core-2020-05-10-deduplication huggingface dataset:
Hierarchical network meta-analysis models to address sparsity of events and differing treatment classifications with regard to adverse outcomes

This is the accepted version of the article, which has been published in final form at DOI: 10.1002/sim.6131.Meta-analysis for adverse events resulting from medical interventions has many challenges, in part due to small numbers of such events within primary studies. Furthermore, variability in drug dose, potential differences between drugs within the same pharmaceutical class and multiple indications for a specific treatment can all add to the complexity of the evidence base. This paper explores the use of synthesis methods, incorporating mixed treatment comparisons, to estimate the risk of adverse events for a medical intervention, while acknowledging and modelling the complexity of the structure of the evidence base. The motivating example was the effect on malignancy of three anti-tumour necrosis factor (anti-TNF) drugs (etanercept, adalimumab and infliximab) indicated to treat rheumatoid arthritis. Using data derived from 13 primary studies, a series of meta-analysis models of increasing complexity were applied. Models ranged from a straightforward comparison of anti-TNF against non-anti-TNF controls, to more complex models in which a treatment was defined by individual drug and its dose. Hierarchical models to allow 'borrowing strength' across treatment classes and dose levels, and models involving constraints on the impact of dose level, are described. These models provide a flexible approach to estimating sparse, often adverse, outcomes associated with interventions. Each model makes its own set of assumptions, and approaches to assessing goodness of fit of the various models will usually be extremely limited in their effectiveness, due to the sparse nature of the data. Both methodological and clinical considerations are required to fit realistically complex models in this area and to evaluate their appropriateness.Partially supported by a National Institute for Health Research Senior Investigator Awar
Joint longitudinal and survival-cure models in tumour xenograft experiments

In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also, cure data are observed because of a portion of individuals who are completely cured in the experiments. When modelling these data, certain constraints have to be imposed on the parameters in the models to account for the intrinsic growth of the tumour in the absence of treatment. Also, the likely inherent association of longitudinal and survival-cure data has to be taken into account in order to obtain unbiased estimators of parameters. In this paper, we propose such models for the joint modelling of longitudinal and survival-cure data arising in xenograft experiments. Estimators of parameters in the joint models are obtained using a Markov chain Monte Carlo approach. Real data analysis of a xenograft experiment is carried out, and simulation studies are also conducted, showing that the proposed joint modelling approach outperforms the separate modelling methods in the sense of mean squared errors. © 2014 John Wiley & Sons, Ltd
Identification and estimation of survivor average causal effects

In longitudinal studies, outcomes ascertained at follow-up are typically undefined for individuals who die prior to the follow-up visit. In such settings, outcomes are said to be truncated by death and inference about the effects of a point treatment or exposure, restricted to individuals alive at the follow-up visit, could be biased even if as in experimental studies, treatment assignment were randomized. To account for truncation by death, the survivor average causal effect (SACE) defines the effect of treatment on the outcome for the subset of individuals who would have survived regardless of exposure status. In this paper, the author nonparametrically identifies SACE by leveraging post-exposure longitudinal correlates of survival and outcome that may also mediate the exposure effects on survival and outcome. Nonparametric identification is achieved by supposing that the longitudinal data arise from a certain nonparametric structural equations model and by making the monotonicity assumption that the effect of exposure on survival agrees in its direction across individuals. A novel weighted analysis involving a consistent estimate of the survival process is shown to produce consistent estimates of SACE. A data illustration is given, and the methods are extended to the context of time-varying exposures. We discuss a sensitivity analysis framework that relaxes assumptions about independent errors in the nonparametric structural equations model and may be used to assess the extent to which inference may be altered by a violation of key identifying assumptions. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd
Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation.

The benefits and challenges of incorporating biomarkers into the development of anticancer agents have been increasingly discussed. In many cases, a sensitive subpopulation of patients is determined based on preclinical data and/or by retrospectively analyzing clinical trial data. Prospective exploration of sensitive subpopulations of patients may enable us to efficiently develop definitively effective treatments, resulting in accelerated drug development and a reduction in development costs. We consider the development of a new molecular-targeted treatment in cancer patients. Given preliminary but promising efficacy data observed in a phase I study, it may be worth designing a phase II clinical trial that aims to identify a sensitive subpopulation. In order to achieve this goal, we propose a Bayesian randomized phase II clinical trial design incorporating a biomarker that is measured on a graded scale. We compare two Bayesian methods, one based on subgroup analysis and the other on a regression model, to analyze a time-to-event endpoint such as progression-free survival (PFS) time. The two methods basically estimate Bayesian posterior probabilities of PFS hazard ratios in biomarker subgroups. Extensive simulation studies evaluate these methods' operating characteristics, including the correct identification probabilities of the desired subpopulation under a wide range of clinical scenarios. We also examine the impact of subgroup population proportions on the methods' operating characteristics. Although both methods' performance depends on the distribution of treatment effect and the population proportions across patient subgroups, the regression-based method shows more favorable operating characteristics
A multiple imputation strategy for sequential multiple assignment randomized trials

Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient‐specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well‐known SMARTs to date. Copyright © 2014 John Wiley & Sons, Ltd
A latent variable transformation model approach for exploring dysphagia

Multiple outcomes are often collected in applications where the quantity of interest cannot be measured directly or is difficult or expensive to measure. In a head and neck cancer study conducted at Dana‐Farber Cancer Institute, the investigators wanted to determine the effect of clinical and treatment factors on unobservable dysphagia through collected multiple outcomes of mixed types. Latent variable models are commonly adopted in this setting. These models stipulate that multiple collected outcomes are conditionally independent given the latent factor. Mixed types of outcomes (e.g., continuous vs. ordinal) and censored outcomes present statistical challenges, however, as a natural analog of the multivariate normal distribution does not exist for mixed data. Recently, Lin  et al . proposed a semiparametric latent variable transformation model for mixed outcome data; however, it may not readily accommodate event time outcomes where censoring is present. In this paper, we extend the work of Lin  et al . by proposing both semiparametric and parametric latent variable models that allow for the estimation of the latent factor in the presence of measurable outcomes of mixed types, including censored outcomes. Both approaches allow for a direct estimate of the treatment (or other covariate) effect on the unobserved latent variable, greatly enhancing the interpretability of the models. The semiparametric approach has the added advantage of allowing the relationship between the measurable outcomes and latent variables to be unspecified, rendering more robust inference. The parametric and semiparametric models can also be used together, providing a comprehensive modeling strategy for complicated latent variable problems. Copyright © 2014 John Wiley & Sons, Ltd
Estimating cancer incidence using a Bayesian back-calculation approach

We propose a Bayesian hierarchical model for the calculation of incidence counts from mortality data by a convolution equation that expresses mortality through its relationship with incidence and the survival probability density. The basic idea is to use mortality data together with an estimate of the survival distribution from cancer incidence to cancer mortality to reconstruct the numbers of individuals who constitute previously incident cases that give rise to the observed pattern of cancer mortality. This model is novel because it takes into account the uncertainty from the survival distribution; thus, a Bayesian-mixture cure model for survival is introduced. Furthermore, projections are obtained starting from a Bayesian age-period-cohort model. The main advantage of the proposed approach is its consideration of the three components of the model: the convolution equation, the survival mixture cure model and the age-period-cohort projection within a directed acyclic graph model. Furthermore, the estimation are obtained through the Gibbs sampler. We applied the model to cases of women with stomach cancer using six age classes [15–45], [45–55], [55–65], [65–75], [75–85] and [85–95] and validated it by using data from the Tuscany Cancer Registry. The model proposed and the program implemented are convenient because they allow different cancer disease to be analysed because the survival time is modelled by flexible distributions that are able to describe different trends
Disease Mapping via Negative Binomial Regression M-quantiles

We introduce a semi-parametric approach to ecological regression for disease
mapping, based on modelling the regression M-quantiles of a Negative Binomial
variable. The proposed method is robust to outliers in the model covariates,
including those due to measurement error, and can account for both spatial
heterogeneity and spatial clustering. A simulation experiment based on the
well-known Scottish lip cancer data set is used to compare the M-quantile
modelling approach and a random effects modelling approach for disease mapping.
This suggests that the M-quantile approach leads to predicted relative risks
with smaller root mean square error than standard disease mapping methods. The
paper concludes with an illustrative application of the M-quantile approach,
mapping low birth weight incidence data for English Local Authority Districts
for the years 2005-2010.Comment: 23 pages, 7 figure
An extreme value theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella

International audienceWe propose a new method that could be part of a warning system for the early detection of time clusters applied to public health surveillance data. This method is based on the extreme value theory (EVT). To any new count of a particular infection reported to a surveillance system, we associate a return period that corresponds to the time that we expect to be able to see again such a level. If such a level is reached, an alarm is generated. Although standard EVT is only defined in the context of continuous observations, our approach allows to handle the case of discrete observations occurring in the public health surveillance framework. Moreover, it applies without any assumption on the underlying unknown distribution function. The performance of our method is assessed on an extensive simulation study and is illustrated on real data from Salmonella surveillance in France
A general framework for parametric survival analysis

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software.Peer-reviewedPost-prin
Optimising the Data Combination Rule for Seamless Phase II/III Clinical Trials

We consider seamless Phase II/III clinical trials which compare K treatments with a common control in Phase II, then test the most promising treatment against control in Phase III. The final hypothesis test for the selected treatment can use data from both Phases, subject to controlling the familywise type I error rate. We show that the choice of method for conducting the final hypothesis test has a substantial impact on the power to demonstrate that an effective treatment is superior to control. To understand these differences in power we derive decision rules which maximise power for particular configurations of treatment effects. A rule with such an optimal frequentist property is found as the solution of a multivariate Bayes decision problem. The optimal rules that we derive depend on the assumed configuration of treatment means. However, we are able to identify two decision rules with robust efficiency across a wide variety of scenarios: a rule using a weighted average of the Phase II and Phase III data on the selected treatment and control, and a closed testing procedure using an inverse normal combination rule and a Dunnett test for intersection hypotheses. For the first of these rules, we find the optimal division of a given total sample size between Phase II and Phase III.We also assess the value of using Phase II data in the final analysis and find that for many plausible scenarios, between 50% and 70% of the Phase II numbers on the selected treatment and control would need to be added to the Phase III sample size in order to achieve the same increase in power
The many weak instruments problem and Mendelian randomization

Instrumental variable estimates of causal effects can be biased when using many instruments that are only weakly associated with the exposure. We describe several techniques to reduce this bias and estimate corrected standard errors. We present our findings using a simulation study and an empirical application. For the latter, we estimate the effect of height on lung function, using genetic variants as instruments for height.
Our simulation study demonstrates that, using many weak individual variants, two-stage least squares (2SLS) is biased, whereas the limited information maximum likelihood (LIML) and the continuously updating estimator (CUE) are unbiased and have accurate rejection frequencies when standard errors are corrected for the presences of many weak instruments. Our illustrative empirical example uses data on 3,631 children from England. We used 180 genetic variants as instruments, and compare conventional ordinary least squares (OLS) estimates to results for the 2SLS, LIML and CUE instrumental variable estimates using an unweighted or weighted allele score as single instruments.
In conclusion, the allele scores and CUE gave consistent estimates of the causal effect. In our empirical example, estimates using the allele score were more efficient. CUE with corrected standard errors, however, provides a useful additional statistical tool in applications with many weak instruments. The CUE may be preferred over an allele score if the population weights for the allele score are unknown, or when the causal effects of multiple risk factors are estimated jointly.This is the final version. It was first published by Wiley at onlinelibrary.wiley.com/doi/10.1002/sim.6358/abstrac
Sample size and robust marginal methods for cluster-randomized trials with censored event times

This is the peer reviewed version of the following article: Zhong Yujie, and Cook Richard J. (2015), Sample size and robust marginal methods for cluster-randomized trials with censored event times, Statist. Med., 34, pages 901–923. doi: 10.1002/sim.6395, which has been published in final form at http://dx.doi.org/10.1002/sim.6395.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.In cluster-randomized trials, intervention effects are often formulated by specifying marginal
models, fitting them under a working independence assumption, and using robust variance estimates
to address the association in the responses within clusters. We develop sample size criteria
within this framework, with analyses based on semiparametric Cox regression models fitted with
event times subject to right censoring. At the design stage, copula models are specified to enable
derivation of the asymptotic variance of estimators from a marginal Cox regression model
and to compute the number of clusters necessary to satisfy power requirements. Simulation studies
demonstrate the validity of the sample size formula in finite samples for a range of cluster
sizes, censoring rates and degrees of within-cluster association among event times. The power
and relative efficiency implications of copula misspecification is studied, as well as the effect of
within-cluster dependence in the censoring times. Sample size criteria and other design issues are
also addressed for the setting where the event status is only ascertained at periodic assessments
and times are interval censored.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626
Evaluating hospital performance based on excess cause-specific incidence

Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry
A power series beta Weibull regression model for predicting breast carcinoma

The postmastectomy survival rates are often based on previous outcomes of large numbers of women who had adisease, but they do not accurately predict what will happen in any particular patient’s case. Pathologic explana-tory variables such as disease multifocality, tumor size, tumor grade, lymphovascular invasion, and enhancedlymph node staining are prognostically signicant to predict these survival rates. We propose a new cure ratesurvival regression model for predicting breast carcinoma survival in women who underwent mastectomy. Weassume that the unknown number of competing causes that can inuence the survival time is given by a powerseries distribution and that the time of the tumor cells left active after the mastectomy for metastasizing follows thebeta Weibull distribution. The new compounding regression model includes as special cases several well-knowncure rate models discussed in the literature. The model parameters are estimated by maximum likelihood. Fur-ther, for different parameter settings, sample sizes, and censoring percentages, some simulations are performed.We derive the appropriate matrices for assessing local inuences on the parameter estimates under different per-turbation schemes and present some ways to assess local inuences. The potentiality of the new regression modelto predict accurately breast carcinoma mortality is illustrated by means of real data. Copyright © 2015 JohnWiley & Sons, Ltd
A Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials

Dual-agent trials are now increasingly common in oncology research and many proposed dose-escalation designs
are available in the statistical literature. Despite this, the translation from statistical design to practical application
is slow, as has been highlighted in single-agent Phase I trials, where a 3+3 rule-based design is often still used.
To expedite this process, new dose-escalation designs need to be not only scientifically beneficial, but also easy to
understand and implement by clinicians. In this paper we propose a curve-free (non-parametric) design for a dualagent
trial in which the model parameters are the probabilities of toxicity at each of the dose combinations. We show
that it is relatively trivial for a clinician’s prior beliefs or historical information to be incorporated in the model
and updating is fast and computationally simple through the use of conjugate Bayesian inference. Monotonicity is
ensured by considering only a set of monotonic contours for the distribution of the maximum tolerated contour,
which defines the dose-escalation decision process. Varied experimentation around the contour is achievable and
multiple dose combinations can be recommended to take forward to Phase II. Code for R, Stata and Excel are
available for implementation.We would like to acknowledge funding from the UK Medical Research Council (grant code U1052.00.014) for this work.
We would also like to thank the reviewers for providing some excellent suggestions to help improve the manuscript.This is the final published version. It first appeared at http://onlinelibrary.wiley.com/doi/10.1002/sim.6434/abstract