Phase-type models for competing risks, with emphasis on identifiability issues.

We first review some main results for phase-type distributions, including a discussion of Coxian distributions and their canonical representations. We then consider the extension of phase-type modeling to cover competing risks. This extension involves the consideration of finite state Markov chains with more than one absorbing state, letting each absorbing state correspond to a particular risk. The non-uniqueness of Markov chain representations of phase-type distributions is well known. In the paper we study corresponding issues for the competing risks case with the aim of obtaining identifiable parameterizations. Statistical inference for the Coxian competing risks model is briefly discussed and some real data are analyzed for illustration.

Assessing short-term risk of ischemic stroke in relation to all prescribed medications.

We examined the short-term risk of stroke associated with drugs prescribed in Norway or Sweden in a comprehensive, hypothesis-free manner using comprehensive nation-wide data. We identified 27,680 and 92,561 cases with a first ischemic stroke via the patient- and the cause-of-death registers in Norway (2004-2014) and Sweden (2005-2014), respectively, and linked these data to prescription databases. A case-crossover design was used that compares the drugs dispensed within 1 to 14 days before the date of ischemic stroke occurrence with those dispensed 29 to 42 days before the index event. A Bolasso approach, a version of the Lasso regression algorithm, was used to select drugs that acutely either increase or decrease the apparent risk of ischemic stroke. Application of the Bolasso regression algorithm selected 19 drugs which were associated with increased risk for ischemic stroke and 11 drugs with decreased risk in both countries. Morphine in combination with antispasmodics was associated with a particularly high risk of stroke (odds ratio 7.09, 95% confidence intervals 4.81-10.47). Several potentially intriguing associations, both within and across pharmacological classes, merit further investigation in focused, follow-up studies.

Author Correction: Systematic assessment of prescribed medications and short-term risk of myocardial infarction - a pharmacopeia-wide association study from Norway and Sweden.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

MACPET: model-based analysis for ChIA-PET.

We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional interactions than those discovered by MACS. MACPET is freely available on Bioconductor. ChIA-PET; MACPET; Model-based clustering; Paired-end tags; Peak-calling algorithm.

Systematic assessment of prescribed medications and short-term risk of myocardial infarction - a pharmacopeia-wide association study from Norway and Sweden.

Wholesale, unbiased assessment of Scandinavian electronic health-care databases offer a unique opportunity to reveal potentially important undiscovered drug side effects. We examined the short-term risk of acute myocardial infarction (AMI) associated with drugs prescribed in Norway or Sweden. We identified 24,584 and 97,068 AMI patients via the patient- and the cause-of-death registers and linked to prescription databases in Norway (2004-2014) and Sweden (2005-2014), respectively. A case-crossover design was used to compare the drugs dispensed 1-7 days before the date of AMI diagnosis with 15-21 days' time -window for all the drug individually while controlling the receipt of other drugs. A BOLASSO approach was used to select drugs that acutely either increase or decrease the apparent risk of AMI. We found 48 drugs to be associated with AMI in both countries. Some antithrombotics, antibiotics, opioid analgesics, adrenergics, proton-pump inhibitors, nitroglycerin, diazepam, metoclopramide, acetylcysteine were associated with higher risk for AMI; whereas angiotensin-II-antagonists, calcium-channel blockers, angiotensin-converting-enzyme inhibitors, serotonin-specific reuptake inhibitors, allopurinol, mometasone, metformin, simvastatin, levothyroxine were inversely associated. The results were generally robust in different sensitivity analyses. This study confirms previous findings for certain drugs. Based on the known effects or indications, some other associations could be anticipated. However, inverse associations of hydroxocobalamin, levothyroxine and mometasone were unexpected and needs further investigation. This pharmacopeia-wide association study demonstrates the feasibility of a systematic, unbiased approach to pharmacological triggers of AMI and other diseases with acute, identifiable onsets.

Modeling of semi-competing risks by means of first passage times of a stochastic process.

In semi-competing risks one considers a terminal event, such as death of a person, and a non-terminal event, such as disease recurrence. We present a model where the time to the terminal event is the first passage time to a fixed level c in a stochastic process, while the time to the non-terminal event is represented by the first passage time of the same process to a stochastic threshold S, assumed to be independent of the stochastic process. In order to be explicit, we let the stochastic process be a gamma process, but other processes with independent increments may alternatively be used. For semi-competing risks this appears to be a new modeling approach, being an alternative to traditional approaches based on illness-death models and copula models. In this paper we consider a fully parametric approach. The likelihood function is derived and statistical inference in the model is illustrated on both simulated and real data.

Uncertainty of decibel levels.

The mean sound exposure level from a source is routinely estimated by the mean of the observed sound exposures from repeated measurements. A formula for the standard uncertainty based on the Guide to the expression of Uncertainty in Measurement (GUM) is derived. An alternative formula is derived for the case where the GUM method fails. The formulas are applied on several examples, and compared with a Monte Carlo calculation of the standard uncertainty. The recommended formula can be seen simply as a convenient translation of the uncertainty on an energy scale into the decibel level scale, but with a theoretical foundation.

Residual plots to reveal the functional form for covariates in parametric accelerated failure time models.

We study residual plots for parametric accelerated failure time (AFT) models, using both standardized residuals and Cox-Snell residuals. Two different approaches are discussed in the case of censored data; adjusting censored residuals by adding a residual time, and using nonparametric exponential regression of non-adjusted censored Cox-Snell residuals. The main object of the paper is to show how residuals can be used to infer the correct functional form for possibly misspecified covariates. We demonstrate the use of the methods by analysis of two reliability data sets and by a simulation study using Weibull-distributed data. We also consider briefly a corresponding approach for parametric proportional hazards models.

Bayesian estimation of HIV-1 dynamics in vivo.

Statistical analysis of viral dynamics in HIV-1 infected patients undergoing structured treatment interruptions were performed using a novel model that accounts for treatment efficiency as well as total CD8+ T cell counts. A brief review of parameter estimates obtained in other studies is given, pointing to a considerable variation in the estimated values. A Bayesian approach to parameter estimation was used with longitudinal measurements of CD4+ and CD8+ T cell counts and HIV RNA. We describe an estimation procedure which uses spline approximations of CD8+ T cells dynamics. This approach reduces the number of parameters that must be estimated and is especially helpful when the CD8+ T cells growth function has a delayed dependence on the past. Seven important parameters related to HIV-1 in-host dynamics were estimated, most of them treated as global parameters across the group of patients. The estimated values were mainly in keeping with the estimates obtained in other reports, but our paper also introduces the estimates of some new parameters which supplement the current knowledge. The method was also tested on a simulated data set.