SStatistics and biometrics in clinical studies
Digitale Health applications (DiGA)
Biometric consultation under the specific requirements of
Digital Health Applications Ordinance (DiGAV)
Contact ACOMED statistics (Tel.: 0341/3910195, E-mail:info@acomed-statistik), if you need support in planning and analyzing a clinical study for your digital health application.
The increasing digitalization of healthcare opens the door to the introduction of health applications based on digital processes and processes. These can include diagnostic, therapeutic, or combined approaches. Digital health applications are diverse, ranging from self-monitoring apps to telemedicine solutions.
Among the providers of DiGA (Digital Health Applications), we usually encounter a new generation of customers: often they are young, committed, digitally savvy and also economically focused entrepreneurs who want to bring innovative products to the market.
During the approval process, digitalThe evaluation of therapeutic effects and efficacy is central to digital health applications (DiGA). The dynamic nature of these technologies necessitates a flexible and well-structured statistical approach to meet the regulatory requirements of the Digital Health Applications Ordinance (DiGAV) in Germany: the quality, safety, and efficacy of DiGA must be ensured.
The approval of digital health applications (DiGA) is a key milestone in their market launch. The effort required for the necessary clinical studies is often underestimated, both in terms of content and workload.
In defining the goals, endpoints (variables used to measure goal achievement), and study population of such studies, biometric aspects are considered alongside medical and economic factors. It is important to consider the high dropout rate observed in studies. This occurs both due to the effectiveness of the treatment (i.e., successful treatment) and due to a lack of effectiveness—which can be associated with adverse events or switching to other treatment options.
From a biometric perspective, this situation will be resolved with the introduction of Estimands encountered that address the treatment, including the different courses of the disease.
ACOMED statistik has extensive experience regarding the design of
Estimands as well as analyses from studies for the pharmaceutical industry and from DiGA studies. We are pleased to support you in the conception, planning, and analysis of your studies.
We offer the following statistical services for the evaluation of DiGA:
Statistical advice regarding biometric aspects of the study protocol:
Conception and planning of your DiGA study title
- concepttion of the study design
- Support in defining study objectives and endpoints
- Support in selecting suitable instruments for determining patient-reported outcomes (PROs), e.g. questionnaires
- Ensuring biometric aspects of the internal and external validity as well as the sensitivity of the study
- Support in defining inclusion and exclusion criteria (study population)
- Conception and construction of the Estimands
- Determining the sample size (number of cases)
- Possibly planning interim analyses
- Preparation of the statistics chapter of the study protocol (hypotheses, definition of primary and secondary variables, statistical methodology of the analyses, interim analyses, definition of the evaluation populations, handling of missing values, definition of estimand-specific analysis procedures, handling of missing values)
- If necessary: Determination of the randomization procedure
- Proposal of solutions in case of critical aspects within the clinical trial
Support in conducting the study
- Assistance in responding to inquiries from authorities, possibly including meetings with authorities.
- Randomization
- We do not offer the following services directly, but we can support you, for example, by reviewing documents: data management, review of external documents (electronic data collection forms (eCRF), data management plan, data validation plan)
Statistical evaluation plan for your DiGA studyr title
Details can be found on the information page about pharmaceutical studies:SAP
In particular, the SAP also describes and explains the estimand-specific statistical analyses. The procedures for detecting the causes of missing values (missing at random [MAR], missing not at random [MNAR]) and the associated imputation procedures (replacing missing values) are listed.
SAS programming, R programming
More often than usual, we encounter digitally savvy clients in these projects who would like to understand, expand upon, and ultimately take control and conduct the analysis themselves. Therefore, we also offer programming in the open-source software R (link) on.
Statistical analysis of your DiGA study
- Possibly providing (blinded) tables and lists for data review meetings (DRM) before the actual evaluation.
- Descriptive statistics
- Endpoint analysis
- Analysis of the estimates including determination of the cause of missing values (Missing at random [MAR], missing not at random [MNAR]), imputation, sensitivity analyses
- planned analyses of additional endpoints and subgroups
Contribution to the clinical report title
- Statistical report or contribution to the clinical report
- Chapters on Materials and Methods and Results within the Clinical Study Report (CSR)
- Support with publications
