Statistical Analysis Plan (SAP) and Biostatistics
The Clinical Biostatisticians at BioPharma Services can develop a Statistical Analysis Plan (SAP) for your clinical trial. This is a more detailed and technical elaboration of the analysis described in the protocol. The SAP includes comprehensive procedures for executing the statistical analysis of the primary and secondary variables and other datasets.
Clinical Statistical Consulting
Phase 1 Study Types of Analysis
BioPharma Services offers the following Phase 1 clinical trials. The Biostatistics team will contribute the statistical elements in these areas of clinical research, using the appropriate type of analysis depending on the study to achieve reliable statistical results. Click each to Learn More.
Our Expertise & Team of PhDs
Our team of Biostatisticians from around the world all have PhDs from prestigious institutions and extensive industry experience. Supported by our team of highly skilled statistical programmers, the Biostatistics team delivers high quality biostatistics for clinicians with rapid turnaround time.
→ DMP and SAP development
→ Safety Data Management
→ Medical Coding
→ Safety TLFs
→ CDISC (SDTM, ADaM)
BioPharma’s suite of Biostatistical Services include:
- Sample size calculations
- Randomization schemes
- Statistical Analysis Plan (SAP including mock shells)
- SAS programming and validation
- Tables, listings and figures
Standard CDISC services offered by BioPharma include:
- CDASH compliant CRF’s
- SDTM and ADaM datasets
- Define .XML formats
- Ancillary services are also provided through our preferred partners
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Learn how BioPharma Services can be your trusted clinical trial partner.
What is Biostatistics and what is it used for?
Biostatistics bridges the gap between living organisms and quantifiable data. It is the process of analyzing health and biological data. Biostatisticians are crucial in clinical trials, epidemiology and public health. In clinical trials, Biostatisticians analyze the trial participant’s data. In epidemiology and public health, the study of the exact way in which living things are affected helps to form a picture of what has occurred as well as what may occur in the future. This influences how policymakers decide what actions should be taken to help a population.
What is the Importance of Biostatistics in Clinical Research?
Biostatistics in clinical research goes beyond simply applying statistics to data. The Biostatisticians help design procedures and use the context of the research to find data that is disqualified from the study, as well as outliers. Biostatistics combines mathematics, statistics, and biology. The knowledge of all three helps facilitate the design, analysis, and interpretation of the research and raw data.
When is Biostatistics used?
Biostatistics is implemented when biologically related information needs to be interpreted. In a clinical trial setting, Biostatisticians can help design the statistical analysis and then once the lab data has been collected, the Biostatistician will begin the statistical analysis. In clinical trials, Biostatisticians ensure that the trial includes the statistical results required by the FDA or the NMPA. Biostatisticians can aid in the design of the statistical analysis in addition to the analysis itself.
Why Do You Need Biostatistics in Clinical Research?
Biostatistics encompasses both an understanding of statistics and biology, and how the two affect each other. The knowledge of both is what makes Biostatistics so important. The skills to conduct the statistical analysis must be accompanied by the knowledge of the context that the biology provides.
How is Biostatistics Significant in Pharmaceutical Research?
Biostatistics is critical in the design of pharmaceutical research, as well as executing the analysis of the data. A strong knowledge of statistics as well as the biology pertinent to pharmaceutics is needed to properly determine how raw lab data should be analyzed and then to also conduct the analysis. Biostatisticians will conduct the statistical analysis on the clinical data and help the researchers understand the outcome of the analysis.