Clinical trials are an excellent approach to testing the effectiveness of new medicines and treatment procedures before endorsing them for use. As you undertake data collection and analysis, it’s vital to avoid the inefficiencies and pitfalls that’d affect your study. You can reduce your clinical research costs and improve its quality through this.
Avoiding common mistakes also enhances your subject’s safety, comfort, and success in your clinical trial. Collecting quality and consistent data can be challenging. However, advanced data collection and analysis methods exist, which you can leverage.
The following are some pitfalls you should avoid:
Using Paper
You should avoid using paper in your data collection and analysis. Documenting your data in paper forms is tedious, and you’re prone to making mistakes and mixing up some data. You should consider going digital with your collection and analysis. Using technology saves you resources and time.
Furthermore, with technologies like case report forms, you can adhere to compliance protocols in data collection. Case report forms are electronic documents you can use to record all the information about a patient during clinical trials. They help you assess the efficacy and safety of clinical products. You may want to check out this complete guide to case report forms for a deeper understanding of how they work.
Also, using paper is unreliable because you can quickly lose or damage it. However, technology allows you to access your data anywhere and get an overview of what’s going on with your trial in real-time. Remember, clinical data is robust, and it can be confusing and tiresome to analyze manually on paper. Going digital with your data analysis allows you to simultaneously process large amounts of raw data.
Collecting Too Much Data
You should avoid collecting more data than you need for the study. This dramatically increases your workload during the data collection and analysis, leaving you frustrated and less motivated. It’d help to ensure the dataset you’re getting is precise and allocate more time to analysis.
Ensure you collect accurate data by doing the following:
- Define your hypothesis at the beginning
- Determine your statistical analysis plan
- Have a data collection strategy
- Design your data collection activity
If you have these defined at the start of your clinical trial, you can be sure to collect correct and valuable data.
Utilizing Complicated Forms
Most participants in a study aren’t experts in the field. Having them fill out complicated forms full of clinical jargon can result in the respondents filling in random answers that don’t reflect how they feel about completing the survey. Keep the format and language simple, and remember to validate the forms before using them.
Ignoring Privacy And Security Considerations
There are security and privacy principles concerning collecting data from participants. Ignoring these laws can jeopardize your study. You should refer to any regulatory, legislative, and administrative provisions regarding clinical data collection, transfer, and storage. There are also rules that govern the data analysis technologies you have.
Hence, ensure you ask for advice from your legal team before starting the trial. This ensures you’re compliant with privacy laws like the health privacy principles (HPP) and information privacy principles (IPP).
Facing Literacy And Language Comprehension Barrier
The language barrier is a pitfall during clinical data collection because the demographic you use for your study could be illiterate, or they may speak a different language. You can avoid this by utilizing the translation or read-aloud feature on mobile devices for your respondents. Also, you could have a professional translator to help with the exercise.
Having Poorly Trained Staff
Enumerators’ lack of or inadequate training is a common pitfall in clinical data collection and analysis. Rushing to get your clinical trial running without training your staff never ends well. Before sending out your team, ensure their interpretation of the research questions is the same as yours and is consistent with your intentions.
Your enumerators can quickly introduce biases in interpreting the questions if you don’t train them. For example, if someone lives with their partner for four months, are they married? Some staff will say no, others yes. For your study, you must clarify the correct interpretations. Ensure you clear up most grey areas that affect your study as much as possible.
Also, if you’re using a software application in your data collection and analysis, your staff should be comfortable using it. Good data collection software should be easy to use and intuitive; however, this shouldn’t be an excuse not to train your staff.
Conclusion
Getting right or close to perfect is critical to clinical data collection and analysis. Having incorrect data means you can’t answer your research questions, you’re unable to validate your results, you have distorted findings, you caused your participants harm, and you provided misleading decisions and recommendations. Therefore, avoid the pitfalls stated above at all costs.
