Specialist subjects

Methods Development

We provide expert guidance in finding the appropriate statistical approach to answer your question… and if there isn’t yet a method, well, we can develop one.

While applying biostatistics to address your research question is essential, there may be times when existing methods fall short for your specific problem. In such cases, we’re here to extend or adapt existing methods to meet your unique requirements. RDA has been at the forefront of statistical methods development, contributing to the creation of several specialised software packages, including:

merlin offers a comprehensive modelling framework in Stata, including linear regression, Weibull survival models, three-level logistic mixed-effects models, and multivariate joint models.
survsim is a R package offering a state-of-the-art framework for simulating survival data.
multistate includes a rich toolset for multi-state survival analysis in Stata.
stacked simplifies the creation of stacked probability plots in R, making it easier to visualise results after fitting a multi-state model.

Our team is actively engaged in methodological research, not just applying biostatistics but also advancing it, leading to more than 45 publications in statistical or software journals such as Statistics in Medicine, the American Journal of Epidemiology, and Biometrical Journal. This puts us in an ideal position to help you with your data analysis needs.

Selected publications

Entrop JP, Jakobsen LH, Crowther MJ, Clements M, Eloranta S, Dietrich CE. Parametric estimation of the mean number of events in the presence of competing risks Biometrical Journal 2025;67(1):e70038
Curnow E, Hughes RA, Birnie K, Tilling K, Crowther MJ. Multiple imputation strategies for missing event times in a multi-state model analysis. Statistics in Medicine 2024;43(6):1238-1255.
Crowther MJ, Royston P, M Clements M. A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors. Biostatistics2023;24(3):811-831.
Bower H, Andersson TML, Crowther MJ, Lambert PC. Flexible parametric survival analysis with multiple timescales: Estimation and implementation using stmt. The Stata Journal 2022;22(3):679-701.
Gasparini A, Clements MS, Abrams KR, Crowther MJ. Impact of model misspecification in shared frailty survival models. Statistics in Medicine 2019;38(23):4477-4502.
Crowther MJ, Lambert PC. A general framework for parametric survival analysis. Statistics in Medicine2014;33(30):5280-5297.

Specialist subjects

Methods Development

Methods Development We provide expert guidance in finding the appropriate statistical approach to answer your question… and if there isn’t yet a method, well, we can develop one. While applying biostatistics to address your research question is essential, there may be times when existing methods fall short for your specific problem. In such cases, we’re […]

Specialist subjects

Applied Biostatistics

Applied Biostatistics Biostatistics plays a crucial role in advancing medical research. Whether it’s clinical trials, epidemiological studies, or pre-clinical research, biostatistics is essential for drawing meaningful, impactful conclusions from complex data. Our team consists of internationally recognized experts in applied biostatistics, with deep experience in a wide range of areas such as survival analysis, multi-state […]

Specialist subjects

Haematology

Haematological malignancies At Red Door Analytics, we have extensive experience in working with haematological malignancies, demonstrated through 18 publications in peer-reviewed journals. Our expertise spans epidemiological studies on prognosis and late effects, as well as randomised clinical trials. Based in Stockholm, we have unique experience in accessing and working with registry data from the Nordic […]

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 […]

Specialist subjects

Real-World Evidence (RWE)

Real-World Evidence Real-world evidence (RWE) refers to 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, […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]