Biostatistics

Original Editor - User: Juwayriah Dalvi Top Contributors - Juwayriah Dalvi

Introduction

Biostatistics is of utmost importance while conducting research. It helps in analysing and interpreting data obtained via clinical studies. In Biostatistics, different statistical methods are applied to biological, medical and, public health data. Biostatistics ensures evidence-based practices in health care.

Importance of Biostatistics in Health Care

Biostatistics have some key advantages in health research and evidence generation:

• Making reasonable estimates depending on the data collected
• Comparing between different interventions
• Evaluating the impact of intervention
• Establishing the relationship between a suspected cause and the health problem
• Health education
• Organization and planning of a clinical trial
• Identification of syndromes by understanding associations and correlations
• Useful to find out sensitivity and specificity of a diagnostic test
• Used to determine the risk factor for a disease

Sampling Methods

Sampling involves the selection of a number of a study units from a well-defined population as consideration of the entire population is impossible. If the entire population was taken into account there would be no need of statistical inference. The subgroup included in the investigation that represents the entire population is known as sample.

The four most commonly used sampling methods in medicine are:

Simple Random Sampling: Every subject has an equal chance of being selected for the study.

Systematic Random Sampling: Every nth person is selected where the 'n' is obtained by dividing the number of people in the sampling frame by sample size.

Stratified Random Sampling: The overall population is divided into relevant subgroups and then the simple random sampling method is used for each sub-group.

Cluster Sampling: This requires a two-stage process in which the population is divided into clusters, and a subset of the clusters is randomly selected. Clusters are commonly based on geographic areas hence this kind of sampling is implemented commonly in epidemiologic research.

Hypothesis Testing

Hypothesis testing involves choosing between two opposing hypotheses.

The null hypothesis (H0) posits that there is no significant difference between a proposed value of a population parameter and the value derived from a sample taken from that population. In contrast, the alternative hypothesis (H1 or Ha) suggests that there is a significant difference between the proposed value and its estimated counterpart.

When testing the null hypothesis, the outcome can be either correct or incorrect. An incorrect decision can occur in two ways: we might reject the null hypothesis when it is actually true (Type I error), or we might fail to reject it when it is false (Type II error). The likelihood of committing Type I and Type II errors is represented by the symbols alpha (α) and beta (β), respectively.

The p-value indicates the smallest significance level at which the null hypothesis would be rejected. It serves as a measure of confidence only when the null hypothesis is rejected; it is not meaningful when we conclude that the null hypothesis is true.

Statistical Significance

In research, statistical significance assesses the likelihood that the null hypothesis is true, balanced against an acceptable level of uncertainty about the actual result. Even when a hypothesis is disproven, researchers cannot be entirely certain of the outcome. Therefore, they must accept a certain level of confidence—or significance level—that reflects how confident they are in their findings. This significance level, denoted by alpha (α), represents the probability the researcher is willing to accept for being incorrect.

Typically, researchers aim for a 95% confidence level, accepting a 5% chance of error. Probabilities range from 0 (completely incorrect) to 1 (entirely correct), meaning a researcher seeking 95% certainty is prepared to be wrong 5% of the time. In this context, an alpha of 0.05 indicates the threshold of acceptable uncertainty.

A study is deemed statistically significant if the P value is less than the predetermined alpha. In summary:

• A P value less than the specified alpha indicates statistical significance.
• A P value equal to or greater than alpha does not indicate statistical significance.

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Statistical vs. Clinical Significance

It's important to distinguish between statistical and clinical significance. Statistical significance assesses whether the results have mathematical relevance, while clinical significance evaluates whether the differences are meaningful for patients and clinicians.

Tools for Statistical Analysis

Ensuring the correctness of research is vital in medical sciences as the evidence generated will be implemented further in clinical practice. Due to lack of appropriate statistical knowledge, published research articles contain various errors related to the design, analysis and interpretation of results in the area of biomedical research the purpose of research is defeated.

With advances in technology, there are certain tools which can be used by clinicians as well as statisticians to derive accurate results. Following are a few examples of commonly used software for biostatistics:

• Microsoft Excel
• Statistical Package for Social Sciences (SPSS)
• GraphPad