# The Key Statistical Operations in Clinical Trials

Anyone reading a published medical research paper or a clinical study will eventually stumble upon data-derived study conclusions. However simple the results might seem, they are actually a product of complex study-specific calculations, which are created through statistical operations.

Statistical calculation is an integral part of any research and clinical trial, and is often performed by top-grade biostatisticians. In fact, they perform analytical operations throughout the whole trial process, from protocol design, through the creation of Statistical Analysis Plan, to the final Tables, Listings and Figures.

Among the many data operations that a biostatistician does, there are some that are considered essential.

## Framing of the Hypothesis

Statistically, most of the studies are designed to disapprove of the null-hypothesis (H0) or/and approve the alternative hypothesis (Ha). Framing of the hypotheses is crucial for defining the precise goals of a trial.

Biostatistics uses data acquired from previous researches or Real-World Evidence to define the hypothesis based on rational inputs. When applying statistical models, the trial design is more firmly stated, and the framework better established.

## Testing of the Hypothesis

Statistical testing of the hypothesis is achieved through determining the level of significance (α) of the study, adopting adequate statistical tests, and assessing the associated p-value.

The statistical tests that are to be applied will depend on the study design and the type of predictor and outcome variable. Factors that influence the power of statistical tests that are used are sample size (n), variance (σ2), effect size (δ), level of significance (α), and type II error (β).

## Defining Trial Errors

The most common errors in clinical trials are false positive (type I error) and false negative (type II error).

Biostatisticians carefully track the statistical parameters to measure them and assure against data corruption.

## Calculating Sample Size

If the sample size calculation is not done correctly, it may result in quantitative research that is unable to detect the genuine relationship between the predictor and outcome variables. In other words, the sample should be of appropriate size and representative of the population being studied.

Sample size calculations require the pre-defined knowledge of the effect size, type I error level, type II error level, and the standard deviation (σ).

Modern trial services offer a solution in the form of a sample size calculator, a tool based on statistical models used to calculate the sample size.

## Calculating Relative Risk (RR) and Odds Ratio (OR)

Risk and odds ratios are used when a comparison is made between two groups.

In clinical studies, Risk is used to define an outcome, and Odds is used to identify a precursor or antecedent.

## Determining Correlation and Regression

Calculating the strength of the relationship between variables, which are associated with the disease, is essential in clinical trials.

Correlation and regression analysis help quantify these relationships.

## Overcome Multiplicity Issues and Making Clinical Trial Adjustments

Multiple testing occurs when more than one independent statistical test is performed. This happens when a medicine in a clinical trial targets more than one symptom, or if there are multiple test subgroups within a trial.

When multiple statistical tests are applied, it is more likely for a study to reach statistical significance. These multiplicity issues are overcome with in-depth biostatistical analysis or by choosing a single primary endpoint to track.

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