pinecone/core-2020-05-10-deduplication huggingface dataset:
State-Space Size Considerations for Disease-Progression Models

The article of record as published may be found at http://dx.doi.org/10.1002/sim.5808Markov models of disease progression are widely used to model transitions in patients’ health state over time.
Usually, patients’ health status may be classified according to a set of ordered health states. Modelers lump
together similar health states into a finite and usually small, number of health states that form the basis of
a Markov chain disease-progression model. This increases the number of observations used to estimate each
parameter in the transition probability matrix. However, lumping together observably distinct health states also
obscures distinctions among them and may reduce the predictive power of the model. Moreover, as we demonstrate,
precision in estimating the model parameters does not necessarily improve as the number of states in the
model declines. This paper explores the tradeoff between lumping error introduced by grouping distinct health
states and sampling error that arises when there are insufficient patient data to precisely estimate the transition
probability matrix
Simulating biologically plausible complex survival data

Simulation studies are conducted to assess the performance of current and novel statistical models in pre-defined scenarios. It is often desirable that chosen simulation scenarios accurately reflect a biologically plausible underlying distribution. This is particularly important in the framework of survival analysis, where simulated distributions are chosen for both the event time and the censoring time. This paper develops methods for using complex distributions when generating survival times to assess methods in practice. We describe a general algorithm involving numerical integration and root-finding techniques to generate survival times from a variety of complex parametric distributions, incorporating any combination of time-dependent effects, time-varying covariates, delayed entry, random effects and covariates measured with error. User-friendly Stata software is provided. Copyright © 2013 John Wiley & Sons, Ltd.Peer-reviewedPost-prin
Inverse Probability Weighted Estimating Equations for Randomized Trials in Transfusion Medicine

This is the peer reviewed version of the following article: Cook, R. J., Lee, K.-A., Cuerden, M. and Cotton, C. A. (2013), Inverse probability weighted estimating equations for randomized trials in transfusion medicine. Statist. Med., 32: 4380–4399. doi:10.1002/sim.5827, which has been published in final form at http://dx.doi.org/10.1002/sim.5827.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.Thrombocytopenia is a condition characterized by extremely low platelet counts, which puts
patients at elevated risk of morbidity and mortality because of bleeding. Trials in transfusion
medicine are routinely designed to assess the effect of experimental platelet products on patients
platelet counts. In such trials, patients may receive multiple platelet transfusions over a predefined
period of treatment, and a response is available from each such administration. The resulting data
comprised multiple responses per patient, and although it is natural to want to use this data in testing
for treatment effects, naive analyses of the multiple responses can yield biased estimates of the
probability of response and associated treatment effects. These biases arise because only subsets
of the patients randomized contribute response data on the second and subsequent administrations
of therapy and the balance between treatment groups with respect to potential confounding factors
is lost. We discuss the design and analysis issues involved in this setting and make recommendations
for the design of future platelet transfusion trials.Natural Sciences and Engineering Research Council of Canada (RJC RGPIN 155849, CAC RGPIN 402474); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626
Multivariate meta-analysis of mixed outcomes: a Bayesian approach

Multivariate random effects meta-analysis (MRMA) is an appropriate way for synthesizing data from studies reporting multiple correlated outcomes. In a Bayesian framework, it has great potential for integrating evidence from a variety of sources. In this paper, we propose a Bayesian model for MRMA of mixed outcomes, which extends previously developed bivariate models to the trivariate case and also allows for combination of multiple outcomes that are both continuous and binary. We have constructed informative prior distributions for the correlations by using external evidence. Prior distributions for the within-study correlations were constructed by employing external individual patent data and using a double bootstrap method to obtain the correlations between mixed outcomes. The between-study model of MRMA was parameterized in the form of a product of a series of univariate conditional normal distributions. This allowed us to place explicit prior distributions on the between-study correlations, which were constructed using external summary data. Traditionally, independent 'vague' prior distributions are placed on all parameters of the model. In contrast to this approach, we constructed prior distributions for the between-study model parameters in a way that takes into account the inter-relationship between them. This is a flexible method that can be extended to incorporate mixed outcomes other than continuous and binary and beyond the trivariate case. We have applied this model to a motivating example in rheumatoid arthritis with the aim of incorporating all available evidence in the synthesis and potentially reducing uncertainty around the estimate of interest.Peer-reviewedPublisher Versio
Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers

Predicting the probability of the occurrence of a binary outcome or condition is important in biomedical research. While assessing discrimination is an essential issue in developing and validating binary prediction models, less attention has been paid to methods for assessing model calibration. Calibration refers to the degree of agreement between observed and predicted probabilities and is often assessed by testing for lack-of-fit. The objective of our study was to examine the ability of graphical methods to assess the calibration of logistic regression models. We examined lack of internal calibration, which was related to misspecification of the logistic regression model, and external calibration, which was related to an overfit model or to shrinkage of the linear predictor. We conducted an extensive set of Monte Carlo simulations with a locally weighted least squares regression smoother (i.e., the loess algorithm) to examine the ability of graphical methods to assess model calibration. We found that loess-based methods were able to provide evidence of moderate departures from linearity and indicate omission of a moderately strong interaction. Misspecification of the link function was harder to detect. Visual patterns were clearer with higher sample sizes, higher incidence of the outcome, or higher discrimination. Loess-based methods were also able to identify the lack of calibration in external validation samples when an overfit regression model had been used. In conclusion, loess-based smoothing methods are adequate tools to graphically assess calibration and merit wider application
Quantifying cancer patient survival : extensions and applications of cure models and life expectancy estimation

Cancer patient survival is the single most important measure of cancer patient care. By 
quantifying cancer patient survival in different ways further insights can be gained in terms of 
temporal trends and differences in cancer patient survival between groups. The objective of this 
thesis is to develop and apply methods for estimating the cure proportion and loss in expectation of 
life for cancer patients.




In paper I, a cure model was used to study temporal trends in survival of patients with 
acute myeloid leukaemia in Sweden. Cancer patient survival was estimated in a relative survival 
setting and quantified as the proportion cured and the median survival time of uncured for different 
age groups and by calendar time of diagnosis. We found a dramatic increase in the cure proportion 
for the age group 19-40, although almost no improvement was seen for patients aged 70-79 at 
diagnosis.



 
In paper II, a flexible parametric cure model was developed to overcome some 
limitations with standard parametric cure models. This model is a special case of a non-mixture 
cure model, using splines instead of a parametric distribution for the modeling. The fit of the 
flexible parametric cure model was compared to the fit of a Weibull non-mixture cure model, and 
shown to be superior in cases when the standard non-mixture cure model did not give a good fit or 
did not converge. Software was developed to enable use of the method. 




In paper III, the possibility of using a flexible parametric relative survival model for 
estimating life expectancy and loss in expectation of life was evaluated. Extrapolation of the 
survival function is generally needed, and the flexible parametric relative survival model was 
shown to extrapolate the survival very well. The method was evaluated by comparing survival 
functions extrapolated from 10 years past diagnosis to observed survival by the use of data with 40 
years of follow-up. Software was developed to enable use of the method. 




In paper IV, the life expectancy and loss in expectation of life was estimated for colon 
cancer patients in Sweden. Even though relative survival was similar across age for colon cancer 
patients, the loss in expectation of life varied greatly by age, since young patients have more years 
to lose. We also found that the life expectancy of colon cancer patients improved over time. 
However, the improvement has to a large extent mimicked the improvement seen in the general 
population, and therefore there were no large changes in the loss in expectation of life. 




In conclusion, the methods presented in this thesis are additional tools for estimating 
and quantifying population-based cancer patient survival, that can lead to an improved 
understanding of different aspects of the prognosis of cancer patients
Data generation for the Cox proportional hazards model\ud
with time-dependent covariates: A method for medical\ud
researchers

The proliferation of longitudinal studies has increased the importance of statistical methods for time-to-event data that can incorporate time-dependent covariates. The Cox proportional hazards model is one such method that is widely used. As more extensions of the Cox model with time-dependent covariates are developed, simulations studies will grow in importance as well. An essential starting point for simulation studies of time-to-event models is the ability to produce simulated survival times from a known data generating process. This paper develops a method for the generation of survival times that follow a Cox proportional hazards model with time-dependent covariates. The method presented relies on a simple transformation of random variables generated according to a truncated piecewise exponential distribution, and allows practitioners great flexibility and control over both the number of time-dependent covariates and the number of time periods in the duration of follow-up measurement. Within this framework, an additional argument is suggested that allows researchers to generate time-to-event data in which covariates change at integer-valued steps of the time scale. The purpose of this approach is to produce data for simulation experiments that mimic the types of data structures applied researchers encounter when using longitudinal biomedical data. Validity is assessed in a set of simulation experiments and results indicate that the proposed procedure performs well in producing data that conform to the assumptions of the Cox proportional hazards model
Hippocampal shape analysis in Alzheimer’s disease using Functional Data Analysis

The hippocampus is one of the first affected regions in Alzheimer's disease. The left hippocampi of control subjects, patients with mild cognitive impairment and patients with Alzheimer's disease are represented by spherical harmonics. Functional data analysis is used in the hippocampal shape analysis. Functional principal component analysis and functional independent component analysis are defined for multivariate functions with two arguments. A functional linear discriminant function is also defined. Comparisons with other approaches are carried out. Our functional approach gives promising results, especially in shape classification. Copyright © 2013 John Wiley & Sons, Ltd
Inverse probability weighting for covariate adjustment in randomized studies

Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented
Incorporating adverse event relatedness into dose-finding clinical trial designs

Dose-finding designs estimate the dose level of a drug based on observed adverse events. Relatedness of the adverse event to the drug has been generally ignored in all proposed design methodologies. These designs assume that the adverse events observed during a trial are definitely related to the drug, which can lead to flawed dose-level estimation. We incorporate adverse event relatedness into the so-called continual reassessment method. Adverse events that have ‘doubtful’ or ‘possible’ relationships to the drug are modelled using a two-parameter logistic model with an additive probability mass. Adverse events ‘probably’ or ‘definitely’ related to the drug are modelled using a cumulative logistic model. To search for the maximum tolerated dose, we use the maximum estimated toxicity probability of these two adverse event relatedness categories. We conduct a simulation study that illustrates the characteristics of the design under various scenarios. This article demonstrates that adverse event relatedness is important for improved dose estimation. It opens up further research pathways into continual reassessment design methodologies
Identifiability of Subgroup Causal Effects in Randomized Experiments
  with Nonignorable Missing Covariates

Although randomized experiments are widely regarded as the gold standard for
estimating causal effects, missing data of the pretreatment covariates makes it
challenging to estimate the subgroup causal effects. When the missing data
mechanism of the covariates is nonignorable, the parameters of interest are
generally not pointly identifiable, and we can only get bounds for the
parameters of interest, which may be too wide for practical use. In some real
cases, we have prior knowledge that some restrictions may be plausible. We show
the identifiability of the causal effects and joint distributions for four
interpretable missing data mechanisms, and evaluate the performance of the
statistical inference via simulation studies. One application of our methods to
a real data set from a randomized clinical trial shows that one of the
nonignorable missing data mechanisms fits better than the ignorable missing
data mechanism, and the results conform to the study's original expert
opinions. We also illustrate the potential applications of our methods to
observational studies using a data set from a job-training program.Comment: Statistics in Medicine (2014
Inference for the median residual life function in sequential multiple assignment randomized trials

In survival analysis, median residual lifetime is often used as a summary measure to assess treatment effectiveness; it is not clear, however, how such a quantity could be estimated for a given dynamic treatment regimen using data from sequential randomized clinical trials. We propose a method to estimate a dynamic treatment regimen‐specific median residual life (MERL) function from sequential multiple assignment randomized trials. We present the MERL estimator, which is based on inverse probability weighting, as well as, two variance estimates for the MERL estimator. One variance estimate follows from Lunceford, Davidian and Tsiatis' 2002 survival function‐based variance estimate and the other uses the sandwich estimator. The MERL estimator is evaluated, and its two variance estimates are compared through simulation studies, showing that the estimator and both variance estimates produce approximately unbiased results in large samples. To demonstrate our methods, the estimator has been applied to data from a sequentially randomized leukemia clinical trial. Copyright © 2013 John Wiley & Sons, Ltd
Incomplete contingency tables with censored cells with application to estimating the number of people who inject drugs in Scotland

Estimating the size of hidden or difficult to reach populations is often of interest for economic, sociological or public health reasons. In order to estimate such populations, administrative data lists are often collated to form multi-list cross-counts and displayed in the form of an incomplete contingency table. Log-linear models are typically fitted to such data to obtain an estimate of the total population size by estimating the number of individuals not observed by any of the data-sources. This approach has been taken to estimate the current number of people who inject drugs (PWID) in Scotland, with the Hepatitis C virus (HCV) diagnosis database used as one of the data-sources to identify PWID. However, the HCV diagnosis data-source does not distinguish between current and former PWID, which, if ignored, will lead to over-estimation of the total population size of current PWID. We extend the standard model-fitting approach to allow for a data-source which contains a mixture of target and non-target individuals (i.e. in this case; current and former PWID). We apply the proposed approach to data for PWID in Scotland in 2003, 2006 and 2009 and compare to the results from standard log-linear models.PostprintPublisher PDFPeer reviewe
Model-based dose finding under model uncertainty using general
  parametric models

Statistical methodology for the design and analysis of clinical Phase II dose
response studies, with related software implementation, are well developed for
the case of a normally distributed, homoscedastic response considered for a
single timepoint in parallel group study designs. In practice, however, binary,
count, or time-to-event endpoints are often used, typically measured repeatedly
over time and sometimes in more complex settings like crossover study designs.
In this paper we develop an overarching methodology to perform efficient
multiple comparisons and modeling for dose finding, under uncertainty about the
dose-response shape, using general parametric models. The framework described
here is quite general and covers dose finding using generalized non-linear
models, linear and non-linear mixed effects models, Cox proportional hazards
(PH) models, etc. In addition to the core framework, we also develop a general
purpose methodology to fit dose response data in a computationally and
statistically efficient way. Several examples, using a variety of different
statistical models, illustrate the breadth of applicability of the results. For
the analyses we developed the R add-on package DoseFinding, which provides a
convenient interface to the general approach adopted here.Comment: Now references were correctly compile
Multi‐state models for colon cancer recurrence and death with a cured fraction

In cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi‐state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials. Copyright © 2013 John Wiley & Sons, Ltd
Choosing profile double-sampling designs for survival estimation with application to PEPFAR evaluation

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample. In limited-resource settings, however, generating accurate estimates using a minimum of resources is important. We propose using double-sampling designs that oversample certain profiles of dropouts as more efficient alternatives to random designs. First, we develop a framework to estimate the survival function under these profile double-sampling designs. We then derive the precision of these designs as a function of the rule for selecting different profiles, in order to identify more efficient designs. We illustrate using data from the United States President's Emergency Plan for AIDS Relief-funded HIV care and treatment program in western Kenya. Our results show why and how more efficient designs should oversample patients with shorter dropout times. Further, our work suggests generalizable practice for more efficient double-sampling designs, which can help maximize efficiency in resource-limited settings
Quantifying unrecognised replication present in reports of HIV diagnoses

New diagnoses of HIV infection were reported confidentially to the Public Health Laboratory Service (PHLS) AIDS Centre under a national voluntary surveillance scheme. Two sets of data drawn from the national datasets were made available to us for analysis, the first in 1991, the second in 1994, by which time the replication of reports had been reduced. The data used in the analyses consisted of the numbers of replications of the reported full date of birth in the individual records (one, two, three and so on), for each year of birth. This paper uses a non-parametric maximum likelihood estimation method for quantifying the amount of replication in the data. The estimated amount of replication was 3.37% (95% confidence interval (0.98%,11.83%)) in the 1991 and 0.58% (95% confidence interval (0%,2.64%)) in the 1994 dataset