by | Nov 16, 2015 | Medical Writing

Clinical studies are conducted to test the drugs and medical devices in humans before their approval and availability for the commercial market. To understand the results of clinical studies, clinical research professionals need to use statistical analysis. Biostatistics helps them to understand the different aspects of the study which enable to interpret the results of the study in more scientific way. It involves the biostatistician immediately after conceptualization of the research question. This will make sure that you design the study accurately. Introduction of the biostatistician in the middle of the study can result in several pitfalls in the study design.

Now, question may arise why there is need for biostatistics in human research? The simple answer can be to understand the concept of variation and sampling. Attributes are not only different among the individuals but they could also be different within the same individual over the time. Human research mostly conducted on small sample size and it’s indeed a tedious task to express the results from small sample size. Therefore, biostatistics plays an important role in human medical research.

The most common medical writing tips for the use of biostatistics in human research are provided below:

  • How was data collected?

For a survey type of study, describe the method of data collection and provide pertinent details, such as the number of questions, range of questionnaire response scores and the meaning of each score.

  • Use simple models for defined parameters

In data analysis, particularly for complex high-dimensional data, it is frequently better to choose simple models for clearly defined parameters.

  • Which statistical software used?

Mention the statistical software and the version used in the analysis, and details such as the manufacturer of the software, city, and country of origin in parenthesis.

  • What all to describe?

Provide clear descriptions of the main features of the statistical analysis (e.g. confidence interval, including degree of confidence; hypothesis tests, including null and alternative hypotheses; level of significance; particular tests and test statistics).

  • Why is data review essential?

To determine the quality of the data, initially look at the bad data first and then remove the suspected data. This would save a lot of time and rework later.

  • How did you manage outliers?

Describe the procedures that were put in place to handle missing values and data, as well as any outliers (provide the definition used for an outlier).

  • What should be the sample size?

To minimize the sampling error effect, large sample size need to be collected. The confidence interval can then be computed and visualized in excel. This enables to make most of small and large sample sizes.

  • Why should you describe study design?

The study design should be described accurately and in detail so that the study can be reproduced, if required. Study/sampling/data flow charts can be used to describe complicated sampling/study designs.

  • What is the role of data management?

Data management is the foundation to well done studies. Statistical team’s participation during study design is important for well reported results.

  • Some more tips
    • A pleasant appearance, coupled with visual interest, can draw the reader through the text and reduce the effort of reading. Use of tables, figures, and graphs provide visual interest and can help clarify a complex message.
    • Use appropriate measures of central tendency and spread as summary statistics; for instance, report medians for highly skewed data. If using the notation a ± b, clearly state what b is – it could be standard deviation or standard error.
    • Use tables to report summary statistics, summaries of results, or the quantities associated with a p-value.

Biostatistics gives you the rationale behind extrapolating the results to larger population and these figures are very important in our day to day science. Learning the basics of biostatics’ is necessary for medical writers as this knowledge can help them to give the explanations of the results and what impact will they have on patient care and outcome.

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