CONSULTANCY SERVICES

Find out more about our specialist subjects
BiostatisticsEpidemiology & Public HealthStatistical methods developmentClinical trial designSoftware applicationReal-world evidenceSimulation studiesCausal inferenceHealth Technology Assessment

Biostatistics

Our Team Members have internationally leading expertise in applied biostatistics, covering a broad range of topics such as survival analysis, multi-state modelling, joint modelling, relative survival and data visualisation. We have years of experience conducting statistical analyses of clinical trials, observational and register-based studies, across numerous clinical areas.

Methods development

We are leaders in the development and application of cutting-edge statistical methods, with all of our team members actively involved in methodological research. We can provide expert guidance in finding the appropriate statistical approach to answer your question… and if there isn’t yet a method, well, we can help develop one.

Software application

Using one of our freely available software packages in your project? We have in house experts in development and application of statistical software, ready to provide direct advice and guidance on how to make the most of our cutting-edge solutions. Arrange a one-on-one session, or consider our range of training courses.

Real-world evidence

Data and information that, unlike data generated in clinical trials conducted in controlled environments, has been obtained from everyday clinical practice, patient registers.

Read more

Related Resources

Videos

Introduction to Epidemiological Study Designs
This video offers a comprehensive introduction to epidemiological study designs, emphasising their classification, key definitions, strengths, limitations, and practical applications. We will dive into the most commonly used study designs, exploring their structure, purpose, and the contexts in which they are most effective. Throughout the video, real-world case studies of landmark research will be used […]
Read more

Specialist subjects

Real-world evidence (RWE)
Real-world evidence (RWE) Data and information that, unlike data generated in clinical trials conducted in controlled environments, has been obtained from everyday clinical practice, patient registers, or other sources outside the clinical trial setting.   RWE plays a crucial role in complementing traditional clinical trial data, providing insights into the safety, effectiveness, and overall performance […]
Read more

Videos

State-of-the-art statistical models for modern HTA
At @RedDoorAnalytics, we develop methodology and software for efficient modelling of biomarkers, measured repeatedly over time, jointly with survival outcomes, which are being increasingly used in cancer settings. We have also developed methods and software for general non-Markov multi-state survival analysis, allowing for the development of more plausible natural history models, where patient history can […]
Read more

Videos

Multilevel (hierarchical) survival models: Estimation, prediction, interpretation
Hierarchical time-to-event data is common across various research domains. In the medical field, for instance, patients are often nested within hospitals and regions, while in education, students are nested within schools. In these settings, the outcome is typically measured at the individual level, with covariates recorded at any level of the hierarchy. This hierarchical structure […]
Read more

Statistical Primers

What are competing risks?
Competing risks In survival analysis, competing risks refer to the situation when an individual is at risk of experiencing an event that precludes the event under study to occur. Competing risks commonly occur in studies of cause-specific mortality, as all other causes of death than the one under study might happen before the individuals “have […]
Read more

Statistical Primers

What is immortal time bias?
Immortal time bias Immortal time bias is a type of bias that can occur in observational research when the study design allows for a period of time during which the outcome of interest cannot occur, often referred to as “immortal time”. Simply put, immortal time bias occurs when information from a future event is incorporated into the […]
Read more

Statistical Primers

What is the proportional hazards assumption?
Proportional hazards Proportional hazards in survival analysis means that the rate at which an event of interest occurs over time for two or more groups or individuals is proportional over time. Specifically, it assumes that the hazard ratio, which represents the relative rate of an event occurring between two groups or individuals, is constant over […]
Read more

Statistical Primers

What is censoring?
Censoring refers to a situation in survival analysis where the event of interest is not observed for some of the individuals under study. In this Statistical Primer, we’ll define three types of censoring often seen in survival analysis studies. Censoring occurs when the information on the survival time is incomplete or only partially observed. Censoring […]
Read more

Statistical Primers

What is the Cox model?
The Cox model The Cox model, also known as the proportional hazards model, is a popular statistical tool used to analyse survival data. It was developed by British statistician Sir David Cox, and published in 1972. It has gained popularity largely by avoiding making parametric assumptions about the shape of the baseline rate in a […]
Read more

Statistical Primers

What is survival analysis?
Survival analysis is a statistical method used to analyse the time until an event of interest occurs. The key feature of survival analysis is that the outcome has two dimensions: – an event indicator (yes/no), and – the time spent at risk for the event All survival analyses require precise definitions of start and end of […]
Read more

Tutorials

Multivariate joint longitudinal-survival models
Joint longitudinal-survival models have been widely developed, but there are many avenues of research where they are lacking in terms of methodological development, and importantly, accessible implementations. We think merlin fills a few gaps. In this post, we’ll take a look at the extension to modelling multiple continuous longitudinal outcomes, jointly with survival. For simplicity, I’ll concentrate […]
Read more

Tutorials

Simulation and estimation of three-level survival models: IPD meta-analysis of recurrent event data
In this example I’ll look at the analysis of clustered survival data with three levels. This kind of data arises in the meta-analysis of recurrent event times, where we have observations (events or censored), k (level 1), nested within patients, j (level 2), nested within trials, i (level 3). Random intercepts The first example will […]
Read more
Read all

Posts

stmt: Modelling multiple timescales using flexible parametric survival models in Stata

Posted by Michael Crowther

4th October 2023

Learn more Read all Blogs