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Background <p>Likert-based scales are a popular tool in clinical trials for assessing patient-reported outcomes. A key analytical decision involves whether to treat these data as binary, continuous, or ordinal. Each approach has implications for statistical power, bias, and interpretation of the results. In this report, we examine methods for evaluating Likert scales, with a particular focus on ordinal approaches, including win probability methods and proportional odds models.</p> Methods <p>We examined the use of proportional odds logistic regression, win probability estimation, dichotomisation with binary logistic regression, and linear regression for analysis of Likert scale-based outcomes. We applied these analytical approaches to patient-reported discomfort data from MyTEMP, a randomised trial comparing personalised cooler dialysate to standard-temperature dialysate in patients undergoing hemodialysis. We also conducted a simulation study to evaluate bias, coverage, and statistical power for each method under proportional and non-proportional odds scenarios across varying sample sizes and outcome distributions.</p> Results <p>In the MyTEMP trial, ordinal analyses showed patients receiving personalised cooler dialysate reported greater discomfort related to feeling cold than those receiving standard dialysate (win probability 64%, win difference 28%, win ratio 1.70; all <i>p</i> ≤ 0.001). The proportional odds model suggested an average twofold increase in the odds of greater discomfort for the intervention (odds ratio 2.25), though the model assumption was violated. A partial proportional odds model revealed stronger intervention effects at higher discomfort thresholds (e.g., nearly sixfold odds at the highest discomfort scores). Simulations demonstrated that ordinal methods (win probability and proportional odds models) generally had higher statistical power and lower bias than methods involving dichotomisation or treating ordinal data as continuous, particularly in the presence of skewed outcome distributions.</p> Conclusion <p>Our work demonstrated that analysing Likert-based outcomes using ordinal methods yields greater statistical power and more nuanced interpretations than dichotomisation or treating data as continuous.</p>

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Statistical analysis of Likert-based ordinal scales: a guide for clinical trialists

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Ahmed A. Al-Jaishi,
  • Meaghan S. Cuerden,
  • Bin Luo,
  • Pavel S. Roshanov,
  • Amit X. Garg

摘要

Background

Likert-based scales are a popular tool in clinical trials for assessing patient-reported outcomes. A key analytical decision involves whether to treat these data as binary, continuous, or ordinal. Each approach has implications for statistical power, bias, and interpretation of the results. In this report, we examine methods for evaluating Likert scales, with a particular focus on ordinal approaches, including win probability methods and proportional odds models.

Methods

We examined the use of proportional odds logistic regression, win probability estimation, dichotomisation with binary logistic regression, and linear regression for analysis of Likert scale-based outcomes. We applied these analytical approaches to patient-reported discomfort data from MyTEMP, a randomised trial comparing personalised cooler dialysate to standard-temperature dialysate in patients undergoing hemodialysis. We also conducted a simulation study to evaluate bias, coverage, and statistical power for each method under proportional and non-proportional odds scenarios across varying sample sizes and outcome distributions.

Results

In the MyTEMP trial, ordinal analyses showed patients receiving personalised cooler dialysate reported greater discomfort related to feeling cold than those receiving standard dialysate (win probability 64%, win difference 28%, win ratio 1.70; all p ≤ 0.001). The proportional odds model suggested an average twofold increase in the odds of greater discomfort for the intervention (odds ratio 2.25), though the model assumption was violated. A partial proportional odds model revealed stronger intervention effects at higher discomfort thresholds (e.g., nearly sixfold odds at the highest discomfort scores). Simulations demonstrated that ordinal methods (win probability and proportional odds models) generally had higher statistical power and lower bias than methods involving dichotomisation or treating ordinal data as continuous, particularly in the presence of skewed outcome distributions.

Conclusion

Our work demonstrated that analysing Likert-based outcomes using ordinal methods yields greater statistical power and more nuanced interpretations than dichotomisation or treating data as continuous.