Empirical Transition Matrix of Multi-State Models: The etm Package

Multi-State models provide a relevant framework for modelling complex event histories. Quantities of interest are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator. In this paper, we present the R package etm that computes and displays the transition probabilities. etm also features a Greenwood-type estimator of the covariance matrix. The use of the package is illustrated through a prominent example in bone marrow transplant for leukaemia patients.

Empirical Transition Matrix of Multi-State Models: The etm Package

Multi-State models provide a relevant framework for modelling complex event histories. Quantities of interest are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator. In this paper, we present the R package etm that computes and displays the transition probabilities. etm also features a Greenwood-type estimator of the covariance matrix. The use of the package is illustrated through a prominent example in bone marrow transplant for leukaemia patients

A competing risks approach for nonparametric estimation of transition probabilities in a non-Markov illness-death model

Competing risks model time to first event and type of first event. An example from hospital epidemiology is the incidence of hospital-acquired infection, which has to account for hospital discharge of non-infected patients as a competing risk. An illness-death model would allow to further study hospital outcomes of infected patients. Such a model typically relies on a Markov assumption. However, it is conceivable that the future course of an infected patient does not only depend on the time since hospital admission and current infection status but also on the time since infection. We demonstrate how a modified competing risks model can be used for nonparametric estimation of transition probabilities when the Markov assumption is violated

The number of primary events per variable affects estimation of the subdistribution hazard competing risks model

AbstractObjectivesTo examine the effect of the number of events per variable (EPV) on the accuracy of estimated regression coefficients, standard errors, empirical coverage rates of estimated confidence intervals, and empirical estimates of statistical power when using the Fine–Gray subdistribution hazard regression model to assess the effect of covariates on the incidence of events that occur over time in the presence of competing risks.Study Design and SettingMonte Carlo simulations were used. We considered two different definitions of the number of EPV. One included events of any type that occurred (both primary events and competing events), whereas the other included only the number of primary events that occurred.ResultsThe definition of EPV that included only the number of primary events was preferable to the alternative definition, as the number of competing events had minimal impact on estimation. In general, 40–50 EPV were necessary to ensure accurate estimation of regression coefficients and associated quantities. However, if all of the covariates are continuous or are binary with moderate prevalence, then 10 EPV are sufficient to ensure accurate estimation.ConclusionAnalysts must base the number of EPV on the number of primary events that occurred

Understanding competing risks: a simulation point of view

<p>Abstract</p> <p>Background</p> <p>Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature.</p> <p>Methods</p> <p>We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the latent failure time approach.</p> <p>Results</p> <p>The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning.</p> <p>Conclusions</p> <p>There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.</p

Dosing Characteristics of Recombinant Human Luteinizing Hormone or Human Menopausal Gonadotrophin-Derived LH Activity in Patients Undergoing Ovarian Stimulation: A German Fertility Database Study

Objectives: The aim of the study was to evaluate dosing of recombinant human luteinizing hormone (r-hLH) or human menopausal gonadotrophin (hMG)-derived medications with LH activity in ovarian stimulation (OS) cycles for in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI). Design: A non-interventional study was performed to analyse data from the German RecDate database (January 2007-December 2011). Participants/Materials, Setting, Methods: Starting/total r-hLH/hMG dose, OS duration/cycle number, r-hLH/hMG initiation day (first day of administration), and population/cycle characteristics were assessed in women (&amp; GE;18 years) undergoing OS for IVF/ICSI using r-hLH or hMG-derived medications (excluding corifollitropin alfa, clomiphene citrate, letrozole, mini/micro-dose human chorionic gonadotrophin, and urofollitropin alone). Data were summarized descriptively. Results: 67,858 identified cycles utilized medications containing r-hLH (10,749), hMG (56,432), or both (677). Mean (standard deviation) OS duration with r-hLH and hMG was 10.1 (4.43) and 9.8 (6.16) days, respectively. Median (25th-75th percentile) r-hLH starting dose (75.0 [75.0-150.0] IU) was consistent across patients regardless of age, infertility diagnosis, or gonadotrophin-releasing hormone (GnRH) protocol. Median (25th-75th percentile) hMG-derived LH activity starting dose was 225.0 (150.0-300.0) IU, regardless of GnRH protocol, but was lower in women aged &lt;35 years and those with ovulation disorders/polycystic ovary syndrome. Median (25th-75th percentile) total dose for r-hLH (750.0 [337.5-1,125.0] IU) and hMG-derived LH activity (1,575.0 [750.0-2,625.0] IU) varied according to patients' age, infertility diagnosis, cycle number, and r-hLH/hMG initiation day. GnRH antagonist use resulted in a numerically higher median total hMG-derived LH activity dose than GnRH agonist use. Limitations: The data used in this study were taken from electronic medical records relating to a specific timeframe (2007-2011) and therefore may not accurately reflect current clinical practice; however, it is likely that the differences between the two compounds would be maintained. Additionally, secondary data sources may suffer from uniformity and quality issues. Conclusions: The standard of care for OS cycles is described with respect to IVF/ICSI treatment including an LH component in Germany during the specified timeframe

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- comparison of adverse event risks in randomized controlled trials

Analyses of adverse events (AEs) are an important aspect of the evaluation of experimental therapies. The SAVVY (Survival analysis for AdVerse events with Varying follow-up times) project aims to improve the analyses of AE data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times, censoring, and competing events (CE). In an empirical study including seventeen randomized clinical trials the effect of varying follow-up times, censoring, and competing events on comparisons of two treatment arms with respect to AE risks is investigated. The comparisons of relative risks (RR) of standard probability-based estimators to the gold-standard Aalen-Johansen estimator or hazard-based estimators to an estimated hazard ratio (HR) from Cox regression are done descriptively, with graphical displays, and using a random effects meta-analysis on AE level. The influence of different factors on the size of the bias is investigated in a meta-regression. We find that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard RR. There is an urgent need to improve the guidelines of reporting AEs so that incidence proportions are finally replaced by the Aalen-Johansen estimator - rather than by Kaplan-Meier - with appropriate definition of CEs. For RRs based on hazards, the HR based on Cox regression has better properties than the ratio of incidence densities

Effect of carbapenem resistance on outcomes of bloodstream infection caused by Enterobacteriaceae in low-income and middle-income countries (PANORAMA): a multinational prospective cohort study

Background Low-income and middle-income countries (LMICs) are under-represented in reports on the burden of antimicrobial resistance. We aimed to quantify the clinical effect of carbapenem resistance on mortality and length of hospital stay among inpatients in LMICs with a bloodstream infection due to Enterobacteriaceae. Methods The PANORAMA study was a multinational prospective cohort study at tertiary hospitals in Bangladesh, Colombia, Egypt, Ghana, India, Lebanon, Nepal, Nigeria, Pakistan, and Vietnam, recruiting consecutively diagnosed patients with carbapenem-susceptible Enterobacteriaceae (CSE) and carbapenem-resistant Entero-bacteriaceae (CRE) bloodstream infections. We excluded patients who had previously been enrolled in the study and those not treated with curative intent at the time of bloodstream infection onset. There were no age restrictions. Central laboratories in India and the UK did confirmatory testing and molecular characterisation, including strain typing. We applied proportional subdistribution hazard models with inverse probability weighting to estimate the effect of carbapenem resistance on probability of discharge alive and in-hospital death, and multistate modelling for excess length of stay in hospital. All patients were included in the analysis. Findings Between Aug 1, 2014, and June 30, 2015, we recruited 297 patients from 16 sites in ten countries: 174 with CSE bloodstream infection and 123 with CRE bloodstream infection. Median age was 46 years (IQR 15–61). Crude mortality was 20% (35 of 174 patients) for patients with CSE bloodstream infection and 35% (43 of 123 patients) for patients with CRE bloodstream infection. Carbapenem resistance was associated with an increased length of hospital stay (3·7 days, 95% CI 0·3–6·9), increased probability of in-hospital mortality (adjusted subdistribution hazard ratio 1·75, 95% CI 1·04–2·94), and decreased probability of discharge alive (0·61, 0·45–0·83). Multilocus sequence typing showed various clades, with marginal overlap between strains in the CRE and CSE clades. Interpretation Carbapenem resistance is associated with increased length of hospital stay and mortality in patients with bloodstream infections in LMICs. These data will inform global estimates of the burden of antimicrobial resistance and reinforce the need for better strategies to prevent, diagnose, and treat CRE infections in LMICs