Statistical analysis is a critical component in an evidence-based clinical research process. Statistical analysis is the process of collecting and analyzing data gathered to determine trends and patterns. Using statistical analysis allows researchers to establish whether the data they’ve obtained supports their research question. When approaching a clinical research study, it’s important to start the process with a statistical analysis plan (SAP), choosing the statistical methods which are appropriate for the study at hand.
Despite its importance, developing a statistical analysis plan for clinical research can be a complicated task, especially if your research framework isn’t totally air tight. If you’re considering a career in clinical research, here are five tips for conducting statistical analysis during your research process.
1. During Your Clinical Research Training, Your Research Framework Should Be Clear
One of the keys to developing a solid statistical analysis plan is to develop a sound research framework for your study. By planning before you begin the clinical research process, you’ll save yourself a lot of time and confusion when you’re conducting your statistical analysis. The research framework should include:
- A clear research question
- Your research goals
- Study design elements, including the target population, measurements and control group
Your research question should identify the focus of your research and what outcomes you’re attempting to test for, while your research goals should clearly state which patterns, associations, or causes you are seeking to identify.
Establishing a clear research framework will make your statistical analysis more effective
Conducting statistical analysis will be much easier when you develop a logically sound research framework. A well-designed research framework will make it simpler to troubleshoot when problems occur, and if you’re in clinical research training, you’ll make your statistical analysis much more efficient by taking the time to formulate clear research questions and goals.
2. Choose Your Statistical Analysis Method Carefully
Although many statistical tests could be used to conduct different analyses on your data, usually with legitimate results, you will want to choose the test that fits your research goals. It’s important to do your research to ensure that the method you choose is compatible with your project. Generally, you’ll want to choose your statistical method based on:
- The number of dependent variables (the variables you’re attempting to measure)
- The nature of the dependent variables
- The number of independent variables (the variables changed to test a dependent variable)
- The nature of the independent variables
Understanding how your variables relate to your research question and to each other will make it easier to come up with a statistical analysis plan that is right for your data.
Statistical analysis is a critical component of clinical research
3. Plan for Attrition
One thing that’s important to remember about statistical analysis is that attrition, the loss of participants during a study, is often inevitable. While attrition can cause imbalance in the populations you’re studying, it can be planned for by adding a cushion for attrition to your statistical analysis plan. Knowing the attrition rates of studies similar to your own can help you to ensure that you have a large enough sample size to produce sufficient statistical power. Statistical power is the ability to detect an actual effect in the variables being tested. By accounting for attrition before you begin your research process, you’ll have a better chance of obtaining usable data.
Planning for attrition can make your statistical results more accurate
4. Be Vigilant About Data Management
During your clinical research program, it’s likely that at one point during the research process, you’ll become overwhelmed by the amount of data you’re working with. Data cleaning will help you to streamline your statistical analysis by filtering out any non-normal variables and identifying missing variables, improving the accuracy of the results obtained. You can clean your data by following a checklist:
- Measure the accuracy of your input in your univariate descriptive statistics. Look for univariate outliers, out-of-range values and standard deviations.
- Determine how much (if any) data is missing and where it’s distributed.
- Identify non-normal variables and outliers. You may need to transform some variables to restore them as usable.
Once you’ve conducted an inspection of your data, you can decide on the measures which need to be taken to restore accuracy in your data sets. Cleaning your data can be time consuming, but it will be worth it when you’re crunching the numbers to find out the results of your research.
5. Don’t Expect a Perfect Data Set
Statistical analysis is only expected to explain the general characteristics of your data in most instances. It’s important to remember that your database will never be perfect, and expecting perfection will only make conducting statistical analysis more frustrating. There are limitations to any set of data, and rather than attempting to force results or ignore suboptimal results, it’s best to determine whether your data actually answers your research question.
Even if you don’t arrive at the desired outcome, it’s better to have analyzed your data accurately, as you can implement what you’ve learned from past mistakes in your future research.
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