A Statistician’s View on Clinical Trials
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Clinical Data Management in Clinical Trials: Clinical trial data usually flows from the patient to a clinical study report or publication. The data goes through several steps between the collection, verification, and analysis. Most clinical research stakeholders look at studies using this workflow and discuss milestones like the first patient, first patient visit, last patient, last patient visit, the database lock, and the first and final drafts of a clinical study report. These milestones are tied to progress and performance metrics, and sometimes funding.
Also, statisticians can design clinical trial studies by considering the flow in reverse order. The first point is the research question: What questions must the study answer? Writing down the answer to the question is the best way to formulate the research question or objective. That is, what would you like to write in a publication, study report, or even in the product monograph?
At 1 year, approximately 45% of the treated patients were disease-free. The response was long-lasting and lasted nearly 3 years in patients who were disease-free at the time.
The statistician will then choose the best statistical analysis options to produce the desired results
For example, statisticians may use estimators of proportions to estimate the percentage of disease-free patients, or they can consider statistical modeling methods if they need to account for baseline patient attributes or covariates. Estimating a duration requires different methods, such as estimators or survival models, or time-to-events.
Once the analysis options are established, the statisticians meet with clinicians, key opinion leaders, and subject matter experts to discuss the data requirements for the analyses. The statistician’s job is to make sure that the data collected will support the analyses. Clinicians, SMEs, and KOLs must ensure that data is collected in a reliable manner, taking into account factors such as memory bias, clinic staff availability and capabilities (e.g., image adjudication), and invasiveness to the patient (e.g., biopsies).
Data requirements discussions will be combined with information about the patient population, which will influence the study’s design. This step is important in the process in which statisticians will make recommendations to stakeholders for the study design to make it as efficient as possible. For example, statisticians may learn from these discussions that patients who have never received treatment are expected to respond differently than those who have. This may need a different approach to study design, such as stratifying subjects to confirm this expectation. If this is true, appropriate design features can increase the power of statistical tests and make the overall trial more efficient.
This process of consulting with clinicians, experts, and leaders to design the study and analyses can be repeated as many as required until a final study design is decided.
Although stakeholders often want to jump right to the sample size question, doing so without first considering the previous steps risks making decisions based on faulty information. Studies that fail to answer the right research questions or meet regulatory requirements have been conducted, putting patients at risk and costing funders a lot of money. Sample size should be determined only after the previous steps have settled on appropriate study objectives, with supporting data, analysis, and design, on which all stakeholders agree.
The statistician’s role is to make sure that research questions are answered with high confidence while meeting regulatory requirements, adhering to business constraints, and limiting patients’ risk exposure.
Clinical data management in clinical trials is an important, complex, and multifaceted process. Without information technology, clinical trials and data management would be difficult and expensive. A clinical trial data management system is software that is widely used for managing clinical trial data.
Clinical Data Management services by Element Technologies help organizations to comply with global data standards (CDISC) by implementing best practices. We leverage industry standards for clinical data management, GCDMP guidelines that include best business practices, and acceptable regulatory standards. Thus, Element helps clients improve data quality, reduce data management costs, and ensure data portability and accountability.