Welcome to Baojing - Red Door Analytics

We are excited to introduce our newest team member, Dr Baojing Li, who is joining us as a Biostatistician. Baojing Li is a statistician specialised in register-based research, multigenerational data linkages, and social determinants of health. She has a solid foundation in survival analysis and structural equation modelling. Her research interests include population mental health, life course and multigenerational research.

Three questions to Baojing

What did you work on before you joined Red Door Analytics?

After finishing my master’s degree in 2020 at Karolinska Institutet, I worked there right after as a research assistant. I designed population‑based epidemiological studies, managed and analysed longitudinal register data, and provided statistical expertise within the research group. I became a PhD candidate at Stockholm University in 2021, where I led multigenerational register‑based research to explore social factors and mental health across multiple generations.

Why did you decide to join Red Door Analytics?

I decided to join Red Door Analytics for two key reasons. First, it offered me the opportunity to deepen my interests in statistics, especially in applying advanced methods to real-world data in a rigorous and reproducible way. Second, I was excited by the consulting aspect of the role: by working closely with clients to solve their problems, I could translate complex data into clear insights and actionable strategies. Such a combination allows me to stay grounded in application of statistics while making a tangible impact.

What are you most excited about in your new role at Red Door Analytics?

What excites me most about my new role at Red Door Analytics is the chance to leverage data to tackle complex, real-world problems, and to do so in a way that combines methodological depth with practical impact. I find problem-solving genuinely fun, especially when it involves untangling patterns in large, messy datasets and turning them into meaningful insights. I’m especially looking forward to working on diverse, data-rich projects where I can apply my expertise in statistics and register-based research to continue growing as both a statistician and a strategic thinker.

List of selected publications

Baojing Li. Misfortune (dis)continues across generations: Multigenerational studies linking socioeconomic and psychosocial disadvantages to psychiatric disorders. PhD dissertation, Department of Public Health Sciences, Stockholm University 2025.
Baojing Li, Can Liu, Ylva B Almqvist, Lisa Berg. Psychiatric disorders following the clustering of family disadvantages in previous generations: a multigenerational cohort study. Social Psychiatry and Psychiatric Epidemiology 2025.
Baojing Li, Can Liu, Ylva B Almqvist, Lisa Berg. Disentangling the multigenerational transmissions of socioeconomic disadvantages and mental health problems by gender and across lineages: Findings from the Stockholm Birth Cohort Multigenerational Study. SSM – Population Health 2023; 22 (101357).
Baojing Li, Peter Allebeck, Bo Burstöm, Anna-Karin Danielsson, Louisa Degenhardt, Terje A Eikemo, Alize Ferrari, Ann Kristin Knudsen, Andreas Lundin, Hélio Manhica, John Newton, Harvey Whiteford, Pär Flodin, Hugo Sjöqvist, Emilie E. Agardh. Educational level and the risk of mental disorders, substance use disorders and self-harm in different age-groups: A cohort study covering 1,6 million subjects in the Stockholm region. International Journal of Methods in Psychiatric Research 2023; 32 (4).

Resources

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 […]