mstate: An R Package for the Analysis of Competing Risks and Multi-State Models

Multi-state models are a very useful tool to answer a wide range of questions in survival analysis that cannot, or only in a more complicated way, be answered by classical models. They are suitable for both biomedical and other applications in which time-to-event variables are analyzed. However, they are still not frequently applied. So far, an important reason for this has been the lack of available software. To overcome this problem, we have developed the mstate package in R for the analysis of multi-state models. The package covers all steps of the analysis of multi-state models, from model building and data preparation to estimation and graphical representation of the results. It can be applied to non- and semi-parametric (Cox) models. The package is also suitable for competing risks models, as they are a special category of multi-state models. This article offers guidelines for the actual use of the software by means of an elaborate multi-state analysis of data describing post-transplant events of patients with blood cancer. The data have been provided by the EBMT (the European Group for Blood and Marrow Transplantation). Special attention will be paid to the modeling of different covariate effects (the same for all transitions or transition-specific) and different baseline hazard assumptions (different for all transitions or equal for some).

mstate: An R Package for the Analysis of Competing Risks and Multi-State Models

Analysis and Stochastic

Handling missing covariate data in clinical studies in haematology

Missing data are frequently encountered across studies in clinical haematology. Failure to handle these missing values in an appropriate manner can complicate the interpretation of a study's findings, as estimates presented may be biased and/or imprecise. In the present work, we first provide an overview of current methods for handling missing covariate data, along with their advantages and disadvantages. Furthermore, a systematic review is presented, exploring both contemporary reporting of missing values in major haematological journals, and the methods used for handling them. A principal finding was that the method of handling missing data was explicitly specified in a minority of articles (in 76 out of 195 articles reporting missing values, 39%). Among these, complete case analysis and the missing indicator method were the most common approaches to dealing with missing values, with more complex methods such as multiple imputation being extremely rare (in 7 out of 195 articles). An example analysis (with associated code) is also provided using hematopoietic stem cell transplantation data, illustrating the different approaches to handling missing values. We conclude with various recommendations regarding the reporting and handling of missing values for future studies in clinical haematology. Development and application of statistical models for medical scientific researc

Transition probability estimates for non-Markov multi-state models

Non-parametric estimation of the transition probabilities in multi-state models is considered for non-Markov processes. Firstly, a generalization of the estimator of Pepe et al, 1991 (Statistics in Medicine) is given for a class of progressive multi-state models based on the difference between Kaplan-Meier estimators. Secondly, a general estimator for progressive or non-progressive models is proposed based upon constructed univariate survival or competing risks processes which retain the Markov property. The properties of the estimators and their associated standard errors are investigated through simulation. The estimators are demonstrated on datasets relating to survival and recurrence in patients with colon cancer and prothrombin levels in liver cirrhosis patients

Personalized decision making on genomic testing in early breast cancer: expanding the MINDACT trial with decision-analytic modeling

BackgroundGenomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical validity) can contribute to individualized decision making on starting chemotherapy for women with breast cancer (clinical utility).MethodsThe MINDACT trial was a randomized trial that enrolled 6693 women with early-stage breast cancer. A 70-gene signature (Mammaprint) was used to estimate genomic risk, and clinical risk was estimated by a dichotomized version of the Adjuvant!Online risk calculator. Women with discordant risk results were randomized to the use of chemotherapy. We simulated the full risk distribution of these women and estimated individual benefit, assuming a constant relative effect of chemotherapy.ResultsThe trial showed a prognostic effect of the genomic signature (adjusted hazard ratio 2.4). A decision-analytic modeling approach identified far fewer women as candidates for genetic testing (4% rather than 50%) and fewer benefiting from chemotherapy (3% rather than 27%) as compared with the MINDACT trial report. The selection of women benefitting from genetic testing and chemotherapy depended strongly on the required benefit from treatment and the assumed therapeutic effect of chemotherapy.ConclusionsA high-quality pragmatic trial was insufficient to directly inform clinical practice on the utility of a genomic test for individual women. The indication for genomic testing may be far more limited than suggested by the MINDACT trial.Development and application of statistical models for medical scientific researchAnalysis and support of clinical decision makin

He Puts Out His Hand. You Put Out Your Hand. Emerging, Urban, Aboriginal Theatre-Makers. What Does it Take to Emerge?

The largest percentage of Aboriginal and Torres Strait Islanders in Australia live in Sydney. Despite this large Aboriginal and Torres Strait Islander population, there is there is very little recorded evidence of a prominent artistic presence of Aboriginal theatre-makers who are creating new, contemporary expressions of urban culture. From 2007-2011, PACT centre for emerging artists (PACT) created a series of Aboriginal-specific opportunities and programs for emerging, urban, Aboriginal theatre-makers who were interested in experimenting in new methods of creation and exploring their urban, lived experience. These opportunities generated a small, critical mass of Aboriginal theatre-makers. The program was in many aspects successful, however it also faced various challenges and misunderstandings. When one of the participating artists, Björn Stewart, presented a new performance work that expressed confusion, dislike and a sense of manipulation in the opportunities he was being offered as an artist by various organisations, it highlighted that perhaps the opportunities being offered to these theatre-makers were not what was perceived as being needed, and that there are varying motivations, agendas and expectations behind such opportunities by those providing them. This study identifies three key stakeholders who contribute to different points of the development of opportunities and new Aboriginal works: the funding body, the arts organisation and the artists. Using PACT’s Aboriginal-specific opportunities as a case study, this research set out to discover: (i) if current opportunities being offered to urban, emerging, Aboriginal theatre-makers are effective; (ii) what are the stakeholders’ perceptions about what is required; and most importantly, (iii) do these perceptions align with each other, and if not, what is the impact on Sydney, urban, emerging Aboriginal theatre-makers? To date, there has been no record of emerging, urban, theatre-makers having been consulted or given the opportunity to voice what they believe an emerging, urban, Aboriginal theatre-maker requires to “emerge”. This study begins that record

Multiple imputation for cause-specific Cox models: assessing methods for estimation and prediction

In studies analyzing competing time-to-event outcomes, interest often lies in both estimating the effects of baseline covariates on the cause-specific hazards and predicting cumulative incidence functions. When missing values occur in these baseline covariates, they may be discarded as part of a complete-case analysis or multiply imputed. In the latter case, the imputations may be performed either compatibly with a substantive model pre-specified as a cause-specific Cox model [substantive model compatible fully conditional specification (SMC-FCS)], or approximately so [multivariate imputation by chained equations (MICE)]. In a large simulation study, we assessed the performance of these three different methods in terms of estimating cause-specific regression coefficients and predicting cumulative incidence functions. Concerning regression coefficients, results provide further support for use of SMC-FCS over MICE, particularly when covariate effects are large and the baseline hazards of the competing events are substantially different. Complete-case analysis also shows adequate performance in settings where missingness is not outcome dependent. With regard to cumulative incidence prediction, SMC-FCS and MICE are performed more similarly, as also evidenced in the illustrative analysis of competing outcomes following a hematopoietic stem cell transplantation. The findings are discussed alongside recommendations for practising statisticians.Development and application of statistical models for medical scientific researc

Breast cancer mortality of older patients with and without recurrence analysed by novel multi-state models

Introduction: In older patients with breast cancer, the risk of dying from other causes than breast cancer strongly increases after the age of 70. The aim of this study was to assess contributions of breast cancer mortality versus other-cause mortality after locoregio-nal or distant recurrence in a population-based cohort of older patients analysed by multi-state models. Methods: Surgically treated patients >70 years diagnosed with stage I-III breast cancer in 2003-2009 were selected from the Netherlands Cancer Registry. A novel multi-state model with locoregional and distant recurrence that incorporates relative survival was fitted. Other-cause and breast cancer mortality were indicated as population and excess mortality. Results: Overall, 18,419 patients were included. Ten-year cumulative incidences of locoregio-nal and distant recurrence were 2.8% (95%CI 2.6-3.1%) and 12.5% (95%CI 11.9-13.1%). Other-cause mortality increased from 23.9% (95%CI 23.7-24.2%) in patients 70-74 years to 73.8% (95%CI 72.2-75.4%) in those >80 years. Ten-year probabilities of locoregional or distant recurrence with subsequent breast cancer death were 0.4-1.3% and 10.2-14.6%, respectively. For patients with a distant recurrence in the first two years after diagnosis, breast cancer death probabilities were 95.3% (95%CI 94.2-96.4%), 93.1% (95%CI 91.6-94.6%), and 88.6% (95%CI 86.5-90.8%) in patients 70-74, 75-79, and >80 years. Conclusion: In older patients without recurrence, prognosis is driven by other-cause mortality. Although locoregional recurrence is a predictor for worse outcome, given its low incidence it contributes little to breast cancer mortality after diagnosis. For patients who develop a distant recurrence, breast cancer remains the dominant cause of death, even at old age.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Experimentele farmacotherapi