Beyond Parametric and Nonparametric Tests: A Practical Guide to Table 1 in Clinical Research

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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Publication Details

Journal
Clinical and Experimental Emergency Medicine
Published
2026-10-15
DOI
https://doi.org/10.15441/ceem.26.333
Primary Topic
Statistical Methods in Clinical Trials
Type
article
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article

Beyond Parametric and Nonparametric Tests: A Practical Guide to Table 1 in Clinical Research

Sook-Young Woo, Han Zo Choi, Jongkyeong Kang
Clinical and Experimental Emergency Medicine
Statistical Methods in Clinical Trials
article

Beyond Parametric and Nonparametric Tests: A Practical Guide to Table 1 in Clinical Research

Sook-Young Woo, Han Zo Choi, Jongkyeong Kang
article en

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.

Clinical and Experimental Emergency Medicine
Daejeon University (KR), Kangwon National University (KR), Kyung Hee University Hospital at Gangdong (KR)
Openalex Percentile: Top 11%
Statistical Methods in Clinical Trials
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