estimateR: an R package to estimate and monitor the effective reproductive number.

Background: Accurate estimation of the effective reproductive number ([Formula: see text]) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2.

The effective reproductive number is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods.

Comprehensive Statistical Exploration of Prognostic (Bio-)Markers for Responses to Immune Checkpoint Inhibitor in Patients with Non-Small Cell Lung Cancer.

Metastatic non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) may suffer from heavy side effects and not all patients benefit from the treatment. We conducted a comprehensive statistical analysis to identify promising (bio-)markers for treatment response. We analyzed retrospective data from NSCLC patients treated with ICIs in first- or further-line therapy settings at the University Hospital Zurich.

The importance of timely contact tracing - A simulation study.

Background: While the role of contact tracing in the containment of the COVID-19 epidemic remains important until vaccines are widely available, literature on objectively measurable indicators for the effectiveness of contact tracing is scarce. We suggest the diagnostic serial interval, the time between the diagnosis of the infector and infectee, as a new indicator for the effectiveness of contact tracing.

Methods: Using an agent-based simulation model, we demonstrate how the diagnostic serial interval correlates with the course of the epidemic.

Loss to follow-up correction increased mortality estimates in HIV-positive people on antiretroviral therapy in Mozambique.

Objectives: People living with human immunodeficiency virus (HIV) on antiretroviral therapy (ART) may be lost to follow-up (LTFU), which hampers the assessment of outcomes. We estimated mortality for patients starting ART in a rural region in sub-Saharan Africa and examined risk factors for death, correcting for LTFU.

Study Design And Setting: We analyzed data from Ancuabe, Mozambique, where patients LTFU are traced by phone and home visits.

Diagnostic serial interval as a novel indicator for contact tracing effectiveness exemplified with the SARS-CoV-2/COVID-19 outbreak in South Korea.

Background: The clinical onset serial interval is often used as a proxy for the transmission interval of an infectious disease. For SARS-CoV-2/COVID-19, data on clinical onset serial intervals is limited, since symptom onset dates are not routinely recorded and do not exist in asymptomatic carriers.

Methods: We define the diagnostic serial interval as the time between the diagnosis dates of the infector and infectee.

From hype to reality: data science enabling personalized medicine.

Background: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media).

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example.

Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large interindividual differences. Typically, the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multidisciplinary team with extensive experience. While the application of Machine Learning classification methods (ML classifiers) to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are currently opaque and not accessible to researchers and clinicians outside the field.

Causal stability ranking.

Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data.

The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring.

Objectives: We examined the effect of switching to second-line antiretroviral therapy (ART) on mortality in patients who experienced immunological failure in ART programmes without access to routine viral load monitoring in sub-Saharan Africa.

Design And Setting: Collaborative analysis of two ART programmes in Lusaka, Zambia and Lilongwe, Malawi.

Methods: We included all adult patients experiencing immunological failure based on WHO criteria.

Understanding human functioning using graphical models.

Background: Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disability and Health (ICF) on the one hand and recent developments in graphical modeling on the other hand might be combined and open the door to a more comprehensive understanding of human functioning.

Inconsistency of the MLE for the Joint Distribution of Interval-Censored Survival Times and Continuous Marks.

This paper considers the non-parametric maximum likelihood estimator (MLE) for the joint distribution function of an interval-censored survival time and a continuous mark variable. We provide a new explicit formula for the MLE in this problem. We use this formula and the mark-specific cumulative hazard function of Huang & Louis (1998) to obtain the almost sure limit of the MLE.

CURRENT STATUS DATA WITH COMPETING RISKS: LIMITING DISTRIBUTION OF THE MLE.

We study nonparametric estimation for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider a simpler 'naive estimator'. Groeneboom, Maathuis and Wellner [8] proved that both types of estimators converge globally and locally at rate n(1/3).

Nonparametric estimation of the joint distribution of a survival time subject to interval censoring and a continuous mark variable.

This article considers three nonparametric estimators of the joint distribution function for a survival time and a continuous mark variable when the survival time is interval censored and the mark variable may be missing for interval-censored observations. Finite and large sample properties are described for the nonparametric maximum likelihood estimator (NPMLE) as well as estimators based on midpoint imputation (MIDMLE) and coarsening the mark variable (CMLE). The estimators are compared using data from a simulation study and a recent phase III HIV vaccine efficacy trial where the survival time is the time from enrollment to infection and the mark variable is the genetic distance from the infecting HIV sequence to the HIV sequence in the vaccine.