pinecone/core-2020-05-10-deduplication huggingface dataset:
Modelling semi-attributable toxicity in dual-agent phase I trials with non-concurrent drug administration

In oncology, combinations of drugs are often used to improve treatment efficacy and/or reduce harmful side effects. Dual-agent phase I clinical trials assess drug safety and aim to discover a maximum tolerated dose combination via dose-escalation; cohorts of patients are given set doses of both drugs and monitored to see if toxic reactions occur. Dose-escalation decisions for subsequent cohorts are based on the number and severity of observed toxic reactions, and an escalation rule. In a combination trial, drugs may be administered concurrently or non-concurrently over a treatment cycle. For two drugs given non-concurrently with overlapping toxicities, toxicities occurring after administration of the first drug yet before administration of the second may be attributed directly to the first drug, whereas toxicities occurring after both drugs have been given some present ambiguity; toxicities may be attributable to the first drug only, the second drug only or the synergistic combination of both. We call this mixture of attributable and non-attributable toxicity semi-attributable toxicity. Most published methods assume drugs are given concurrently, which may not be reflective of trials with non-concurrent drug administration. We incorporate semi-attributable toxicity into Bayesian modelling for dual-agent phase I trials with non-concurrent drug administration and compare the operating characteristics to an approach where this detail is not considered. Simulations based on a trial for non-concurrent administration of intravesical Cabazitaxel and Cisplatin in early-stage bladder cancer patients are presented for several scenarios and show that including semi-attributable toxicity data reduces the number of patients given overly toxic combinations.G.M. Wheeler and A.P. Mander are supported by the Medical Research Council (grant number G0800860). M.J. Sweeting is supported by a European Research Council Advanced Investigator Award: EPIC-Heart (grant number 268834), the UK Medical Research Council (grant number MR/L003120/1), the British Heart Foundation and the Cambridge National Institute for Health Research Biomedical Research Centre. S.M. Lee is supported by the American Cancer Society (grant number MRSG-13-146-01-CPHPS).This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/sim.691
Joint longitudinal hurdle and time-to-event models: an application related to viral load and duration of the first treatment regimen in patients with HIV initiating therapy.

Shared parameter joint models provide a framework under which a longitudinal response and a time to event can be modelled simultaneously. A common assumption in shared parameter joint models has been to assume that the longitudinal response is normally distributed. In this paper, we instead propose a joint model that incorporates a two-part 'hurdle' model for the longitudinal response, motivated in part by longitudinal response data that is subject to a detection limit. The first part of the hurdle model estimates the probability that the longitudinal response is observed above the detection limit, whilst the second part of the hurdle model estimates the mean of the response conditional on having exceeded the detection limit. The time-to-event outcome is modelled using a parametric proportional hazards model, assuming a Weibull baseline hazard. We propose a novel association structure whereby the current hazard of the event is assumed to be associated with the current combined (expected) outcome from the two parts of the hurdle model. We estimate our joint model under a Bayesian framework and provide code for fitting the model using the Bayesian software Stan. We use our model to estimate the association between HIV RNA viral load, which is subject to a lower detection limit, and the hazard of stopping or modifying treatment in patients with HIV initiating antiretroviral therapy.This paper presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. SLB was funded in part by an NIHR Methods Fellowship (MET 12–20). MJC was part funded by a NIHR Doctoral Research Fellowship (DRF-2012-05-409). MM was supported by the UK Medical Research Council (MRC) [grant number MR/J002380/1] and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. We would also like to thank the patients and clinical teams for their contribution.Peer-reviewedPost-prin
Joint modelling of longitudinal and multi-state processes: application
  to clinical progressions in prostate cancer

Joint modelling of longitudinal and survival data is increasingly used in
clinical trials on cancer. In prostate cancer for example, these models permit
to account for the link between longitudinal measures of prostate-specific
antigen (PSA) and the time of clinical recurrence when studying the risk of
relapse. In practice, multiple types of relapse may occur successively.
Distinguishing these transitions between health states would allow to evaluate,
for example, how PSA trajectory and classical covariates impact the risk of
dying after a distant recurrence post-radiotherapy, or to predict the risk of
one specific type of clinical recurrence post-radiotherapy, from the PSA
history. In this context, we present a joint model for a longitudinal process
and a multi-state process which is divided into two sub-models: a linear mixed
sub-model for longitudinal data, and a multi-state sub-model with proportional
hazards for transition times, both linked by shared random effects. Parameters
of this joint multi-state model are estimated within the maximum likelihood
framework using an EM algorithm coupled to a quasi-Newton algorithm in case of
slow convergence. It is implemented under R, by combining and extending the
mstate and JM packages. The estimation program is validated by simulations and
applied on pooled data from two cohorts of men with localized prostate cancer
and treated by radiotherapy. Thanks to the classical covariates available at
baseline and the PSA measurements collected repeatedly during the follow-up, we
are able to assess the biomarker's trajectory, define the risks of transitions
between health states, and quantify the impact of the PSA dynamics on each
transition intensity.Comment: Added appendi
Unbiased estimation in seamless phase II/III trials with unequal treatment effect variances and hypothesis-driven selection rules

Seamless phase II/III clinical trials offer an efficient way to select an experimental treatment and perform confirmatory analysis within a single trial. However, combining the data from both stages in the final analysis can induce bias into the estimates of treatment effects. Methods for bias adjustment developed thus far have made restrictive assumptions about the design and selection rules followed. In order to address these shortcomings, we apply recent methodological advances to derive the uniformly minimum variance conditionally unbiased estimator for two-stage seamless phase II/III trials. Our framework allows for the precision of the treatment arm estimates to take arbitrary values, can be utilised for all treatments that are taken forward to phase III and is applicable when the decision to select or drop treatment arms is driven by a multiplicity-adjusted hypothesis testing procedure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd
Estimating the Expected Value of Partial Perfect Information in Health
  Economic Evaluations using Integrated Nested Laplace Approximation

The Expected Value of Perfect Partial Information (EVPPI) is a
decision-theoretic measure of the "cost" of parametric uncertainty in decision
making used principally in health economic decision making. Despite this
decision-theoretic grounding, the uptake of EVPPI calculations in practice has
been slow. This is in part due to the prohibitive computational time required
to estimate the EVPPI via Monte Carlo simulations. However, recent developments
have demonstrated that the EVPPI can be estimated by non-parametric regression
methods, which have significantly decreased the computation time required to
approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian
Process regression is suggested, but this can still be prohibitively expensive.
Applying fast computation methods developed in spatial statistics using
Integrated Nested Laplace Approximations (INLA) and projecting from a
high-dimensional into a low-dimensional input space allows us to decrease the
computation time for fitting these high-dimensional Gaussian Processes, often
substantially. We demonstrate that the EVPPI calculated using our method for
Gaussian Process regression is in line with the standard Gaussian Process
regression method and that despite the apparent methodological complexity of
this new method, R functions are available in the package BCEA to implement it
simply and efficiently
Prior Event Rate Ratio Adjustment for Hidden Confounding in Observational Studies of Treatment Effectiveness: A Pairwise Cox Likelihood Approach

Observational studies provide a rich source of information for assessing effectiveness of treatment interventions in many situations where it is not ethical or practical to perform randomized controlled trials. However, such studies are prone to bias from hidden (unmeasured) confounding. A promising approach to identifying and reducing the impact of unmeasured confounding is Prior Event Rate Ratio (PERR) adjustment, a quasi-experimental analytic method proposed in the context of electronic medical record database studies. In this paper we present a statistical framework for using a pairwise approach to PERR adjustment that removes bias inherent in the original PERR method. A flexible pairwise Cox likelihood function is derived and used to demonstrate the consistency of the simple and convenient PERR-ALT estimator. We show how to estimate standard errors and confidence intervals for treatment effect estimates based on the observed information, and provide R code to illustrate how to implement the method. Assumptions required for the pairwise approach (as well as PERR) are clarified, and the consequences of model misspecification are explored. Our results confirm the need for researchers to consider carefully the suitability of the method in the context of each problem. We illustrate the application of the method using data from a longitudinal cohort study of enzyme replacement therapy for lysosomal storage disorders.This research was funded by the Medical Research Council [grant number G0902158]. William Henley received
additional support from the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health
Research and Care (CLAHRC) for the South West Peninsula. The views expressed in this publication are those of the
authors and not necessarily those of the NHS, the NIHR or the Department of Health
Bayesian joint ordinal and survival modeling for breast cancer risk assessment

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportional-hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects.\ud
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General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event-free probabilities and predict future longitudinal marker values.\ud
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The model is applied to the assessment of breast cancer risk in women attending a population-based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI-RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.Peer ReviewedPostprint (author's final draft
A flexible parametric approach to examining spatial variation in relative survival.

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts.PDB was supported by an Australian National Health and Medical\ud
Research Council Career Development Fellowship (#1005334). KLM acknowledges support\ud
from the Cooperative Research Centre for Spatial Information, whose activities are funded by\ud
the Australian Commonwealth's Cooperative Research Centres Programme. LMR and KLM\ud
acknowledge support from the ARC Centre of Excellence in Mathematical and Statistical\ud
Frontiers.Peer-reviewedPost-prin
A special case of reduced rank models for identification and modelling of time varying effects in survival analysis

Copyright © 2016 John Wiley & Sons, Ltd. Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd
One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information

Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information. However, one-stage IPD models are often wrongly specified, such that interactions are based on amalgamating within- and across-trial information. We compare, through simulations and an applied example, fixed-effect and random-effects models for a one-stage IPD meta-analysis of time-to-event data where the goal is to estimate a treatment-covariate interaction. We show that it is crucial to centre patient-level covariates by their mean value in each trial, in order to separate out within-trial and across-trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta-analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is -0.011 (95% CI: -0.019 to -0.003; p = 0.004), and thus highly significant, when amalgamating within-trial and across-trial information. However, when separating within-trial from across-trial information, the interaction is -0.007 (95% CI: -0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta-analysts should only use within-trial information to examine individual predictors of treatment effect and that one-stage IPD models should separate within-trial from across-trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.Peer-reviewedPublisher Versio
A studentized permutation test for three-arm trials in the 'gold
  standard' design

The 'gold standard' design for three-arm trials refers to trials with an
active control and a placebo control in addition to the experimental treatment
group. This trial design is recommended when being ethically justifiable and it
allows the simultaneous comparison of experimental treatment, active control,
and placebo. Parametric testing methods have been studied plentifully over the
past years. However, these methods often tend to be liberal or conservative
when distributional assumptions are not met particularly with small sample
sizes. In this article, we introduce a studentized permutation test for testing
non-inferiority and superiority of the experimental treatment compared to the
active control in three-arm trials in the `gold standard' design. The
performance of the studentized permutation test for finite sample sizes is
assessed in a Monte-Carlo simulation study under various parameter
constellations. Emphasis is put on whether the studentized permutation test
meets the target significance level. For comparison purposes, commonly used
Wald-type tests are included in the simulation study. The simulation study
shows that the presented studentized permutation test for assessing
non-inferiority in three-arm trials in the 'gold standard' design outperforms
its competitors for count data. The methods discussed in this paper are
implemented in the R package ThreeArmedTrials which is available on the
comprehensive R archive network (CRAN)
Longitudinal Beta-Binomial Modeling using GEE for Over-Dispersed Binomial Data

Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method. A hybrid algorithm of the Fisher scoring and the method of moments is implemented for computing the method. Extensive simulation studies are conducted to justify the validity of the proposed method. Finally, the proposed method is applied to analyze functional impairment in subjects who are at risk of Huntington disease from a multisite observational study of prodromal Huntington disease
Unbiased split variable selection for random survival forests using
  maximally selected rank statistics

The most popular approach for analyzing survival data is the Cox regression
model. The Cox model may, however, be misspecified, and its proportionality
assumption may not always be fulfilled. An alternative approach for survival
prediction is random forests for survival outcomes. The standard split
criterion for random survival forests is the log-rank test statistics, which
favors splitting variables with many possible split points. Conditional
inference forests avoid this split variable selection bias. However, linear
rank statistics are utilized by default in conditional inference forests to
select the optimal splitting variable, which cannot detect non-linear effects
in the independent variables. An alternative is to use maximally selected rank
statistics for the split point selection. As in conditional inference forests,
splitting variables are compared on the p-value scale. However, instead of the
conditional Monte-Carlo approach used in conditional inference forests, p-value
approximations are employed. We describe several p-value approximations and the
implementation of the proposed random forest approach. A simulation study
demonstrates that unbiased split variable selection is possible. However, there
is a trade-off between unbiased split variable selection and runtime. In
benchmark studies of prediction performance on simulated and real datasets the
new method performs better than random survival forests if informative
dichotomous variables are combined with uninformative variables with more
categories and better than conditional inference forests if non-linear
covariate effects are included. In a runtime comparison the method proves to be
computationally faster than both alternatives, if a simple p-value
approximation is used
A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.

Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthesising summary data estimates of genetic association gleaned from large and independent study populations. This is referred to as two-sample summary data MR. Unfortunately, due to the sheer number of variants that can be easily included into summary data MR analyses, it is increasingly likely that some do not meet the IV assumptions due to pleiotropy. There is a pressing need to develop methods that can both detect and correct for pleiotropy, in order to preserve the validity of the MR approach in this context. In this paper, we aim to clarify how established methods of meta-regression and random effects modelling from mainstream meta-analysis are being adapted to perform this task. Specifically, we focus on two contrastin g approaches: the Inverse Variance Weighted (IVW) method which assumes in its simplest form that all genetic variants are valid IVs, and the method of MR-Egger regression that allows all variants to violate the IV assumptions, albeit in a specific way. We investigate the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach, and propose statistics to quantify the relative goodness-of-fit of the IVW approach over MR-Egger regression.Peer-reviewedPublisher Versio