pinecone/core-2020-05-10-deduplication huggingface dataset:
Beta-binomial model for meta-analysis of odds ratios

In meta-analysis of odds ratios (${\OR}s$), heterogeneity between the studies is usually modelled via the additive random effects model (REM). An alternative, multiplicative random effects model for ${\OR}s$ uses overdispersion. The multiplicative factor in this overdispersion model (ODM) can be interpreted as an intra-class correlation (ICC) parameter. This model naturally arises when the probabilities of an event in one or both arms of a comparative study are themselves beta-distributed, resulting in beta-binomial distributions. We propose two new estimators of the ICC for meta-analysis in this setting. One is based on the inverted Breslow-Day test, and the other on the improved gamma approximation by Kulinskaya and Dollinger (2015, p. 26) to the distribution of Cochran's $Q$. The performance of these and several other estimators of ICC on bias and coverage is studied by simulation. Additionally, the Mantel-Haenszel approach to estimation of odds ratios is extended to the beta-binomial model, and we study performance of various ICC estimators when used in the Mantel-Haenszel or the inverse-variance method to combine odds ratios in meta-analysis. The results of the simulations show that the improved gamma-based estimator of ICC is superior for small sample sizes, and the Breslow-Day-based estimator is the best for $n\geq100$. The Mantel-Haenszel-based estimator of ${\OR}$ is very biased and is not recommended. The inverse-variance approach is also somewhat biased for ${\OR}s\neq1$, but this bias is not very large in practical settings. Developed methods and R programs, provided in the Web Appendix, make the beta-binomial model a feasible alternative to the standard REM for meta-analysis of odds ratios
Bayesian hierarchical modeling of longitudinal glaucomatous visual fields using a two-stage approach

The Bayesian approach has become increasingly popular because it allows to fit quite complex models to data via Markov chain Monte Carlo sampling. However, it is also recognized nowadays that Markov chain Monte Carlo sampling can become computationally prohibitive when applied to a large data set. We encountered serious computational difficulties when fitting an hierarchical model to longitudinal glaucoma data of patients who participate in an ongoing Dutch study. To overcome this problem, we applied and extended a recently proposed two-stage approach to model these data. Glaucoma is one of the leading causes of blindness in the world. In order to detect deterioration at an early stage, a model for predicting visual fields (VFs) in time is needed. Hence, the true underlying VF progression can be determined, and treatment strategies can then be optimized to prevent further VF loss. Because we were unable to fit these data with the classical one-stage approach upon which the current popular Bayesian software is based, we made use of the two-stage Bayesian approach. The considered hierarchical longitudinal model involves estimating a large number of random effects and deals with censoring and high measurement variability. In addition, we extended the approach with tools for model evaluation. Copyrigh
Estimating cohort health expectancies from cross-sectional surveys of disability

A life history can be regarded as a random process that evolves with age through various states of health before terminating with absorption into the state of death. Health expectancies are the occupation times of the non-absorbing states and their estimation is of interest. A continuing major problem has been the lack of satisfactory longitudinal data on which to base estimates and as a result standard inferential techniques may not be relevant. Supposing only cross-sectional data available, we propose a method that is generally applicable and first estimates a logistic parametrization of the probabilities of the various states. A large sample approximation is obtained for the distribution of age specific log (odds). Parameters are estimated by weighted least squares, and this in turn leads to estimates of cohort health expectancies. A result of Liang and Zeger is used to find standard errors. The method is illustrated by application to Australian data from the health surveys of 1981, 1988 and 1993
A two-phase model for chronic disease processes under intermittent inspection

This is the peer reviewed version of the following article: Wu, Y., and Cook, R. J. (2017) A two-phase model for chronic disease processes under intermittent inspection. Statist. Med., 36: 2016-2031. doi: 10.1002/sim.7269, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.7269/full. This article may be used for noncommercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.A model is developed for chronic diseases with an indolent phase that is followed by a phase with more active disease resulting in progression and damage. The time scales for the intensity functions for the active phase are more naturally based on the time since the start of the active phase, corresponding to a semi-Markov formulation. This two-phase model enables one to fit a separate regression model for the duration of the indolent phase and intensity-based models for the more active second phase. In cohort studies for which the disease status is only known at a series of clinical assessment times, transition times are interval-censored, which means the time origin for phase II is interval-censored. Weakly parametric models with piecewise constant baseline hazard and rate functions are specified, and an expectation-maximization algorithm is described for model fitting. Simulation studies examining the performance of the proposed model show good performance under maximum likelihood and two-stage estimation. An application to data from the motivating study of disease progression in psoriatic arthritis illustrates the procedure and identifies new human leukocyte antigens associated with the duration of the indolent phase.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849) 
Canadian Institutes for Health Research (FRN 13887
Closed-form REML estimators and sample size determination for mixed
  effects models for repeated measures under monotone missingness

We derive the closed-form restricted maximum likelihood (REML) estimator and
Kenward-Roger's variance estimator for fixed effects in the mixed effects model
for repeated measures (MMRM) when the missing data pattern is monotone. As an
important application of the analytic result, we present the formula for
calculating the power of treatment comparison using the Wald t test with the
Kenward-Roger adjusted variance estimate in MMRM. It allows adjustment for
baseline covariates without the need to specify the covariate distribution in
randomized trials. A simple two-step procedure is proposed to determine the
sample size needed to achieve the targeted power. The proposed method performs
well for both normal and moderately nonnormal data even in small samples (n =
20) in simulations. An anti-depressant trial is analyzed for illustrative
purposes.Comment: 14 pages, 3 table
Approximating Cross-validatory Predictive P-values with Integrated IS
  for Disease Mapping Models

An important statistical task in disease mapping problems is to identify out-
lier/divergent regions with unusually high or low residual risk of disease.
Leave-one-out cross-validatory (LOOCV) model assessment is a gold standard for
computing predictive p-value that can flag such outliers. However, actual LOOCV
is time-consuming because one needs to re-simulate a Markov chain for each
posterior distribution in which an observation is held out as a test case. This
paper introduces a new method, called iIS, for approximating LOOCV with only
Markov chain samples simulated from a posterior based on a full data set. iIS
is based on importance sampling (IS). iIS integrates the p-value and the
likelihood of the test observation with respect to the distribution of the
latent variable without reference to the actual observation. The predictive
p-values computed with iIS can be proved to be equivalent to the LOOCV
predictive p-values, following the general theory for IS. We com- pare iIS and
other three existing methods in the literature with a lip cancer dataset
collected in Scotland. Our empirical results show that iIS provides predictive
p-values that are al- most identical to the actual LOOCV predictive p-values
and outperforms the existing three methods, including the recently proposed
ghosting method by Marshall and Spiegelhalter (2007).Comment: 21 page
Predictive inference for best linear combination of biomarkers subject to limits of detection.

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method
The forecasting of menstruation based on a state-space modeling of basal
  body temperature time series

Women's basal body temperature (BBT) follows a periodic pattern that is
associated with the events in their menstrual cycle. Although daily BBT time
series contain potentially useful information for estimating the underlying
menstrual phase and for predicting the length of current menstrual cycle, few
models have been constructed for BBT time series. Here, we propose a
state-space model that includes menstrual phase as a latent state variable to
explain fluctuations in BBT and menstrual cycle length. Conditional
distributions for the menstrual phase were obtained by using sequential
Bayesian filtering techniques. A predictive distribution for the upcoming onset
of menstruation was then derived based on the conditional distributions and the
model, leading to a novel statistical framework that provided a sequentially
updated prediction of the day of onset of menstruation. We applied this
framework to a real dataset comprising women's self-reported BBT and days of
menstruation, comparing the prediction accuracy of our proposed method with
that of conventional calendar calculation. We found that our proposed method
provided a better prediction of the day of onset of menstruation. Potential
extensions of this framework may provide the basis of modeling and predicting
other events that are associated with the menstrual cycle.Comment: 16 pages, 4 figure
Blinded sample size re-estimation in three-arm trials with 'gold
  standard' design

The sample size of a clinical trial relies on information about nuisance
parameters such as the outcome variance. When no or only limited information is
available, it has been proposed to include an internal pilot study in the
design of the trial. Based on the results of the internal pilot study, the
initially planned sample size can be adjusted. In this paper, we study blinded
sample size re-estimation in the 'gold standard' design for normally
distributed outcomes. The 'gold standard' design is a three-arm clinical trial
design which includes an active and a placebo control in addition to an
experimental treatment. We compare several sample size re-estimation procedures
in a simulation study assessing operating characteristics including power and
type I error. We find that sample size re-estimation based on the popular
one-sample variance estimator results in overpowered trials. Moreover, sample
size re-estimation based on unbiased variance estimators such as the Xing-Ganju
variance estimator results in underpowered trials, as it is expected since an
overestimation of the variance and thus the sample size is in general required
for the re-estimation procedure to eventually meet the target power. Moreover,
we propose an inflation factor for the sample size re-estimation with the
Xing-Ganju variance estimator and show that this approach results in adequately
powered trials. Due to favorable features of Xing-Ganju variance estimator such
as unbiasedness and a distribution independent of the group means, the
inflation factor does not depend on the nuisance parameter and, therefore, can
be calculated prior to a trial
Bayesian epidemic models for spatially aggregated count data

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so we utilise a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time varying covariates through an Ornstein Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece
Optimum design and sequential treatment allocation in an experiment in deep brain stimulation with sets of treatment combinations

In an experiment including patients who underwent surgery for deep brain stimulation electrode placement, each patient responds to a set of 9 treatment combinations. There are 16 such sets, and the design problem is to choose which sets should be administered and in what proportions. Extensions to the methods of nonsequential optimum experimental design lead to identification of an unequally weighted optimum design involving 4 sets of treatment combinations. In the actual experiment, patients arrive sequentially and present with sets of prognostic factors. The idea of loss due to Burman is extended and used to assess designs with varying randomization structures. It is found that a simple sequential design using only 2 sets of treatments has surprisingly good properties for trials with the proposed number of patients
A Threshold-free Prospective Prediction Accuracy Measure for Censored
  Time to Event Data

Prediction performance of a risk scoring system needs to be carefully
assessed before its adoption in clinical practice. Clinical preventive care
often uses risk scores to screen asymptomatic population. The primary clinical
interest is to predict the risk of having an event by a pre-specified future
time $t_0$. Prospective accuracy measures such as positive predictive values
have been recommended for evaluating the predictive performance. However, for
commonly used continuous or ordinal risk score systems, these measures require
a subjective cutoff threshold value that dichotomizes the risk scores. The need
for a cut-off value created barriers for practitioners and researchers. In this
paper, we propose a threshold-free summary index of positive predictive values
that accommodates time-dependent event status. We develop a nonparametric
estimator and provide an inference procedure for comparing this summary measure
between competing risk scores for censored time to event data. We conduct a
simulation study to examine the finite-sample performance of the proposed
estimation and inference procedures. Lastly, we illustrate the use of this
measure on a real data example, comparing two risk score systems for predicting
heart failure in childhood cancer survivors.Comment: 17 pages, 2 figures, 3 table
Sensitivity analysis for unobserved confounding of direct and indirect
  effects using uncertainty intervals

To estimate direct and indirect effects of an exposure on an outcome from
observed data strong assumptions about unconfoundedness are required. Since
these assumptions cannot be tested using the observed data, a mediation
analysis should always be accompanied by a sensitivity analysis of the
resulting estimates. In this article we propose a sensitivity analysis method
for parametric estimation of direct and indirect effects when the exposure,
mediator and outcome are all binary. The sensitivity parameters consist of the
correlation between the error terms of the mediator and outcome models, the
correlation between the error terms of the mediator model and the model for the
exposure assignment mechanism, and the correlation between the error terms of
the exposure assignment and outcome models. These correlations are incorporated
into the estimation of the model parameters and identification sets are then
obtained for the direct and indirect effects for a range of plausible
correlation values. We take the sampling variability into account through the
construction of uncertainty intervals. The proposed method is able to assess
sensitivity to both mediator-outcome confounding and confounding involving the
exposure. To illustrate the method we apply it to a mediation study based on
data from the Swedish Stroke Register (Riksstroke).Comment: 22 pages, 5 figure
Bootstrap Inference when Using Multiple Imputation

Many modern estimators require bootstrapping to calculate confidence
intervals because either no analytic standard error is available or the
distribution of the parameter of interest is non-symmetric. It remains however
unclear how to obtain valid bootstrap inference when dealing with multiple
imputation to address missing data. We present four methods which are
intuitively appealing, easy to implement, and combine bootstrap estimation with
multiple imputation. We show that three of the four approaches yield valid
inference, but that the performance of the methods varies with respect to the
number of imputed data sets and the extent of missingness. Simulation studies
reveal the behavior of our approaches in finite samples. A topical analysis
from HIV treatment research, which determines the optimal timing of
antiretroviral treatment initiation in young children, demonstrates the
practical implications of the four methods in a sophisticated and realistic
setting. This analysis suffers from missing data and uses the $g$-formula for
inference, a method for which no standard errors are available
Assessment of Bayesian Expected Power via Bayesian Bootstrap

The Bayesian expected power (BEP) has become increasingly popular in sample
size determination and assessment of the probability of success (POS) for a
future trial. The BEP takes into consideration the uncertainty around the
parameters assumed by a power analysis and is thus more robust compared to the
traditional power that assumes a single set of parameters. Current methods for
assessing BEP are often based in a parametric framework by imposing a model on
the pilot data to derive and sample from the posterior distributions of the
parameters. Implementation of the model-based approaches can be analytically
challenging and computationally costly especially for multivariate data sets;
it also runs the risk of generating misleading BEP if the model is
mis-specified. We propose an approach based on the Bayesian bootstrap technique
(BBS) to simulate future trials in the presence of individual-level pilot data,
based on which the empirical BEP can be calculated. The BBS approach is
model-free with no assumptions about the distribution of the prior data and
circumvents the analytical and computational complexity associated with
obtaining the posterior distribution of the parameters. Information from
multiple pilot studies is also straightforward to combine. We also propose the
double bootstrap (BS2), a frequentist counterpart to the BBS, that shares
similar properties and achieves the same goal as the BBS for BEP assessment.
Simulation studies and case studies are presented to demonstrate the
implementation of the BBS and BS2 techniques and to compare the BEP results
with model-based approaches
Assessing the robustness of sisVIVE in a Mendelian randomization study to estimate the causal effect of body mass index on income using multiple SNPs from understanding society

The “some invalid, some valid instrumental variable estimator” (sisVIVE) is a lasso‐based method for instrumental variables (IVs) regression of outcome on an exposure. In principle, sisVIVE is robust to some of the IVs in the analysis being invalid, in the sense of being related to the outcome variable through pathways not mediated by the exposure. In this paper, we consider the application of sisVIVE to a Mendelian randomization study in which multiple genetic variants are used as IVs to estimate the causal effect of body mass index on personal income in the presence of unobserved confounding. In addition to analyzing data from the large‐scale longitudinal household survey Understanding Society, we conduct a simulation study to (a) assess the performance of sisVIVE in relation to that of competing robust methods like “MR‐Egger” and “MR‐Median” and (b) identify scenarios under which its absolute performance is poor. We find that sisVIVE outperforms alternative robust methods, in terms of mean‐square error, across a wide range of scenarios, but that its performance is poor in absolute terms when the presence of indirect pleiotropy leads to failure of the “InSIDE” condition, which is not explicitly required for identification. We argue that this is because the consistency criterion for sisVIVE does not identify the true causal effect when InSIDE fails
Using simulation studies to evaluate statistical methods

Simulation studies are computer experiments that involve creating data by
pseudorandom sampling. The key strength of simulation studies is the ability to
understand the behaviour of statistical methods because some 'truth' (usually
some parameter/s of interest) is known from the process of generating the data.
This allows us to consider properties of methods, such as bias. While widely
used, simulation studies are often poorly designed, analysed and reported. This
tutorial outlines the rationale for using simulation studies and offers
guidance for design, execution, analysis, reporting and presentation. In
particular, this tutorial provides: a structured approach for planning and
reporting simulation studies, which involves defining aims, data-generating
mechanisms, estimands, methods and performance measures ('ADEMP'); coherent
terminology for simulation studies; guidance on coding simulation studies; a
critical discussion of key performance measures and their estimation; guidance
on structuring tabular and graphical presentation of results; and new graphical
presentations. With a view to describing recent practice, we review 100
articles taken from Volume 34 of Statistics in Medicine that included at least
one simulation study and identify areas for improvement.Comment: 31 pages, 9 figures (2 in appendix), 8 tables (1 in appendix