Clinical Research, Statistical Concepts, and Tests

Presentation of Findings in Clinical Research, Statistical Concepts, and Tests

Basic Concepts

In clinical research, risk, effect, and outcomes are quantified using various statistical methods. Absolute risk increase indicates a harmful effect, whereas absolute risk reduction reflects a beneficial intervention. Bayesian analysis begins with a prior probability and updates it with new data.
Blinding is employed to reduce bias. Outcomes may be dichotomous (e.g., presence/absence of disease) or continuous variables (e.g., blood pressure value). Effect size expresses the difference between two groups using standardized measures.

Statistical Methods

The p-value represents the probability that the observed result occurred by chance. However, it does not, on its own, indicate clinical relevance. Confidence intervals reflect the precision of an estimate. Odds ratio, risk ratio, and hazard ratio measure differences in probabilities or time-related risks between groups.
Meta-analyses and systematic reviews increase the strength of evidence by pooling data from multiple studies. A forest plot provides a visual summary of individual study results and overall effect.

Presentation of Findings

Distinguishing between statistical significance and clinical significance is essential. A statistically significant difference may not always be clinically meaningful. Tables and figures facilitate transparent reporting of findings. CONSORT guidelines ensure standardized reporting of randomized controlled trials.

Statistical Tests and Applications

• Chi-square      test (χ²): Examines the association between two categorical variables      (e.g., treatment groups and recovery rate).

• Fisher’s      exact test: Preferred for categorical data with small sample sizes.

• Student’s      t-test: Compares continuous variable means between two groups; assumes      normal distribution.

• Mann-Whitney      U test: Used for comparing two groups when continuous data are not      normally distributed.

• Paired      t-test: Compares pre- and post-intervention values within the same      group.

• Wilcoxon      signed-rank test: Suitable for paired non-normally distributed data.

• ANOVA:     Assesses mean differences among three or more groups; assumes normality      and homogeneity of variance.

• Kruskal-Wallis      test: Nonparametric alternative for comparing three or more groups.

• Correlation      tests: Pearson correlation evaluates linear associations between      continuous variables; Spearman correlation is used for ordinal or      non-normally distributed variables.

• Regression      analyses: Describe the relationship between a dependent variable and      one or more independent variables. Logistic regression is applied for      dichotomous outcomes.

• Kaplan-Meier      analysis: Demonstrates time-to-event probabilities in survival      analysis.

• Log-rank      test: Compares survival curves between two groups.

• Cox      regression: Assesses the impact of multiple variables on survival      outcomes.

Clinical Decision-Making

Interpretation of evidence should consider not only statistical outcomes but also patient benefit, adverse effects, and feasibility. Clinical research findings must be integrated with individual patient characteristics and physician expertise.

References

1. Rovetta A. P-values and confidence intervals as compatibility measures. J Transl Med. 2025 doi:10.1186/s12967-025-?

2. Phillips MR, Wykoff CC, Thabane L, Bhandari M, Chaudhary V. The clinician’s guide to p values, confidence intervals, and magnitude of effects. Eye (Lond). 2022;36:341–342. doi:10.1038/s41433-021-01863-w

3. van Zwet E, Tong TJK. A new look at P values for randomized clinical trials. NEJM Evid. 2023;2(6):? (sayfa no). doi:10.1056/EVIDoa2300003