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Statistics in Medicine

Chaeryon Kang, Holly Janes, Parvin Tajik, Henk Groen, Ben Mol, Corine Koopmans, Kim Broekhuijsen, Eva Zwertbroek, Maria van Pampus, Maureen Franssen
Biomarkers that predict treatment effects may be used to guide treatment decisions, thus improving patient outcomes. A meta-analysis of individual participant data (IPD) is potentially more powerful than a single-study data analysis in evaluating markers for treatment selection. Our study was motivated by the IPD that were collected from 2 randomized controlled trials of hypertension and preeclampsia among pregnant women to evaluate the effect of labor induction over expectant management of the pregnancy in preventing progression to severe maternal disease...
February 14, 2018: Statistics in Medicine
Philip Pallmann, Christian Ritz, Ludwig A Hothorn
Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to "marginalise" the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step...
February 14, 2018: Statistics in Medicine
Yan Yuan, Qian M Zhou, Bingying Li, Hengrui Cai, Eric J Chow, Gregory T Armstrong
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 prespecified future time t 0 . 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...
February 8, 2018: Statistics in Medicine
Qiaolin Chen, Catherine A Sugar, Robert E Weiss
Researchers collected multiple measurements on patients with schizophrenia and their relatives, as well as control subjects and their relatives, to study vulnerability factors for schizophrenics and their near relatives. Observations across individuals from the same family are correlated, and also the multiple outcome measures on the same individuals are correlated. Traditional data analyses model outcomes separately and thus do not provide information about the interrelationships among outcomes. We propose a novel Bayesian family factor model (BFFM), which extends the classical confirmatory factor analysis model to explain the correlations among observed variables using a combination of family-member and outcome factors...
February 5, 2018: Statistics in Medicine
Bo Lu, Dingjiao Cai, Xingwei Tong
Time-to-event data are very common in observational studies. Unlike randomized experiments, observational studies suffer from both observed and unobserved confounding biases. To adjust for observed confounding in survival analysis, the commonly used methods are the Cox proportional hazards (PH) model, the weighted logrank test, and the inverse probability of treatment weighted Cox PH model. These methods do not rely on fully parametric models, but their practical performances are highly influenced by the validity of the PH assumption...
February 5, 2018: Statistics in Medicine
Marie Lilleborge, Solveig Hofvind, Sofie Sebuødegård, Ragnar Hauge
This study proposes a method to optimize the performance of BreastScreen Norway through a stratified recommendation of tests including independent double or single reading of the screening mammograms and additional imaging with or without core needle biopsy. This is carefully evaluated by a value of information analysis. An estimated graphical probabilistic model describing the relationship between a set of risk factors and the corresponding risk of breast cancer is used for this analysis, together with a Bayesian network modeling screening test results conditional on the true (but unknown) breast cancer status of a woman...
February 1, 2018: Statistics in Medicine
Marius Thomas, Björn Bornkamp, Heidi Seibold
An important task in early-phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of randomized clinical trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article, we propose new strategies to perform subgroup analyses in dose-finding trials and discuss the challenges, which arise in this new setting...
February 1, 2018: Statistics in Medicine
Dipesh Mistry, Nigel Stallard, Martin Underwood
BACKGROUND: Motivated by the setting of clinical trials in low back pain, this work investigated statistical methods to identify patient subgroups for which there is a large treatment effect (treatment by subgroup interaction). Statistical tests for interaction are often underpowered. Individual patient data (IPD) meta-analyses provide a framework with improved statistical power to investigate subgroups. However, conventional approaches to subgroup analyses applied in both a single trial setting and an IPD setting have a number of issues, one of them being that factors used to define subgroups are investigated one at a time...
January 31, 2018: Statistics in Medicine
Robson J M Machado, Ardo van den Hout
Continuous-time multistate survival models can be used to describe health-related processes over time. In the presence of interval-censored times for transitions between the living states, the likelihood is constructed using transition probabilities. Models can be specified using parametric or semiparametric shapes for the hazards. Semiparametric hazards can be fitted using P-splines and penalised maximum likelihood estimation. This paper presents a method to estimate flexible multistate models that allow for parametric and semiparametric hazard specifications...
January 31, 2018: Statistics in Medicine
J M Madden, L D Browne, X Li, P M Kearney, A P Fitzgerald
Blood pressure (BP) fluctuates throughout the day. The pattern it follows represents one of the most important circadian rhythms in the human body. For example, morning BP surge has been suggested as a potential risk factor for cardiovascular events occurring in the morning, but the accurate quantification of this phenomenon remains a challenge. Here, we outline a novel method to quantify morning surge. We demonstrate how the most commonly used method to model 24-hour BP, the single cosinor approach, can be extended to a multiple-component cosinor random-effects model...
January 29, 2018: Statistics in Medicine
Wenting Cheng, Jeremy M G Taylor, Pantel S Vokonas, Sung Kyun Park, Bhramar Mukherjee
We consider a situation where there is rich historical data available for the coefficients and their standard errors in a linear regression model describing the association between a continuous outcome variable Y and a set of predicting factors X, from a large study. We would like to use this summary information for improving inference in an expanded model of interest, Y given X,B. The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We formulate the problem in an inferential framework where the historical information is translated in terms of nonlinear constraints on the parameter space and propose both frequentist and Bayes solutions to this problem...
January 24, 2018: Statistics in Medicine
Tim P Morris, David J Fisher, Michael G Kenward, James R Carpenter
Quantitative evidence synthesis through meta-analysis is central to evidence-based medicine. For well-documented reasons, the meta-analysis of individual patient data is held in higher regard than aggregate data. With access to individual patient data, the analysis is not restricted to a "two-stage" approach (combining estimates and standard errors) but can estimate parameters of interest by fitting a single model to all of the data, a so-called "one-stage" analysis. There has been debate about the merits of one- and two-stage analysis...
January 18, 2018: Statistics in Medicine
Tianlei Chen, Pang Du
Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model...
January 17, 2018: Statistics in Medicine
Yongqiang Tang
The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints...
January 15, 2018: Statistics in Medicine
Benedict H W Wong, Sarah B Peskoe, Donna Spiegelman
The partial population attributable risk (pPAR) is used to quantify the population-level impact of preventive interventions in a multifactorial disease setting. In this paper, we consider the effect of nondifferential risk factor misclassification on the direction and magnitude of bias of pPAR estimands and related quantities. We found that the bias in the uncorrected pPAR depends nonlinearly and nonmonotonically on the sensitivities, specificities, relative risks, and joint prevalence of the exposure of interest and background risk factors, as well as the associations between these factors...
January 15, 2018: Statistics in Medicine
Günter Heimann, Rossella Belleli, Jouni Kerman, Roland Fisch, Joseph Kahn, Sigrid Behr, Conny Berlin
Signal detection is routinely applied to spontaneous report safety databases in the pharmaceutical industry and by regulators. As an example, methods that search for increases in the frequencies of known adverse drug reactions for a given drug are routinely applied, and the results are reported to the health authorities on a regular basis. Such methods need to be sensitive to detect true signals even when some of the adverse drug reactions are rare. The methods need to be specific and account for multiplicity to avoid false positive signals when the list of known adverse drug reactions is long...
January 10, 2018: Statistics in Medicine
Edward Bein, Jonah Deutsch, Guanglei Hong, Kristin E Porter, Xu Qin, Cheng Yang
This study investigates appropriate estimation of estimator variability in the context of causal mediation analysis that employs propensity score-based weighting. Such an analysis decomposes the total effect of a treatment on the outcome into an indirect effect transmitted through a focal mediator and a direct effect bypassing the mediator. Ratio-of-mediator-probability weighting estimates these causal effects by adjusting for the confounding impact of a large number of pretreatment covariates through propensity score-based weighting...
January 10, 2018: Statistics in Medicine
Paul W Bernhardt
Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has been given to validation of the response model and influence diagnostics. Previous research has mainly focused on estimating residuals for observations with missing covariates using expected values, after which specialized techniques are needed to conduct proper inference...
January 9, 2018: Statistics in Medicine
Dan Jackson, Martin Law, Theo Stijnen, Wolfgang Viechtbauer, Ian R White
Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference...
January 8, 2018: Statistics in Medicine
Karla Hemming, Monica Taljaard, Andrew Forbes
Cluster randomized trials are frequently used in health service evaluation. It is common practice to use an analysis model with a random effect to allow for clustering at the analysis stage. In designs where clusters are exposed to both control and treatment conditions, it may be of interest to examine treatment effect heterogeneity across clusters. In designs where clusters are not exposed to both control and treatment conditions, it can also be of interest to allow heterogeneity in the degree of clustering between arms...
January 8, 2018: Statistics in Medicine
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