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Beyond Parametric and Nonparametric Tests: A Practical Guide to Table 1 in Clinical Research
Jongkyeong Kang1, Sook-Young Woo2, Han Zo Choi3
1Department of Statistics, Kangwon National University, Chuncheon, Republic of Korea.
Abstract:
In many clinical research reports, Table 1 presents the baseline or general characteristics of study participants and helps readers understand the study population and observed between-group differences. Despite its apparent simplicity, constructing and interpreting Table 1 requires several distinct decisions, including whether formal between-group inference is scientifically justified, how variables should be summarized, and how observed differences should be interpreted. Routine significance testing of baseline characteristics is generally discouraged in randomized trials and often provides limited information in observational studies, where group differences may reflect treatment selection, exposure patterns, disease severity, or other systematic mechanisms. We propose a purpose-first framework in which investigators first clarify the intended role of Table 1, consider the study design, and determine whether a comparison addresses a prespecified and scientifically meaningful inferential question. They should then identify the variable type, evaluate distributional shape, influential observations, and clinical interpretability, and select appropriate summary statistics. For descriptive applications, transparent group-specific summaries should be emphasized, with observed absolute or standardized differences added when useful and without routine p-values. Confidence intervals should be reported when a population-level estimation objective has been defined. When formal inference is justified, investigators should define the target quantity or estimand and select a statistical method according to the variable type, data structure, distributional characteristics, and model assumptions. Effect estimates and confidence intervals should receive greater emphasis than isolated p-values. This framework distinguishes description from inference and promotes transparent, statistically coherent, and clinically meaningful construction and interpretation of Table 1.
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