Specialist subjects

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, and overall performance of medical interventions in diverse and representative populations. It is valuable for assessing long-term outcomes, treatment patterns, and the impact of healthcare interventions on real-world patients. Policymakers, healthcare providers, researchers, and pharmaceutical companies increasingly rely on RWE to make informed decisions about healthcare delivery, regulatory approvals, and treatment strategies, contributing to a more comprehensive understanding of healthcare outcomes in diverse and real-world scenarios.

At RDA, we understand that real-world evidence is generated by using cutting edge methods from biostatistics and epidemiology on observational data to obtain meaning and impactful insights for patients and health care professionals. Our expertise in biostatistics, epidemiology, and the use of Nordic registers make us the perfect partner for your real-world evidence project. Check out our resource page to see all our specialist subjects, such as:

Causal inference,
Competing risks,
Multi-state models,
Joint models.

Selected Publications

Jerkeman M, Ekberg S, Glimelius I, Albertsson-Lindblad A, Entrop JP, Ellin F, Sonnevi K, Lewerin C, Brandefors L, Smedby KE. Nationwide Assessment of Patient Trajectories in Mantle Cell Lymphoma: The Swedish MCLcomplete Project. HemaSphere 2023; 7(8): p e928
Illipse M, Gasparini A, Christoffersen B, Hall P, Czene K, Humphreys K. Studying the association between longitudinal non-dense breast tissue and breast cancer risk: a joint modelling approach. American Journal of Epidemiology, 2024
Ekberg S,Crowther M, Harrysson S, Jerkeman M, Ekström Smedby K, Eloranta S. Patient trajectories after diagnosis of diffuse large B-cell lymphoma—a multistate modelling approach to estimate the chance of lasting remission. British Journal of Cancer 2022;127(9):1642-1649.
Achana F, Gallacher D, Oppong R, Kim S, Petrou S, Mason J,Crowther M. Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial–Based Cost-Effectiveness Data. Medical Decision Making 2021;41(6):667-684.
Evans M,Bower H, Cockburn E, Jacobson SH, Barany P, Carrero JJ. Contemporary management of anaemia, erythropoietin resistance and cardiovascular risk in patients with advanced chronic kidney disease: a nationwide analysis. Clinical Kidney Journal 2020;13(5):821-7.
Bower H, Björkholm M, Dickman PW, Höglund M, Lambert PC, Andersson TML. Life expectancy of patients with chronic myeloid leukemia approaches the life expectancy of the general population. Journal of Clinical Oncology2016;34(24):2851-7.

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 […]