ArticleBMC medicine2020
How to design a pre-specified statistical analysis approach to limit p-hacking in clinical trials: the Pre-SPEC framework.
Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Considerations for pooling real-world data as a comparator cohort to a single arm trial: a simulation study on assessment of heterogeneity.BMC medical research methodology · 2023Pooled it
- Technology-supported Acceptance and Commitment Therapy for chronic health conditions: A systematic review and meta-analysis.Behaviour research and therapy · 2022Pooled it
- Handling misclassified stratification variables in the analysis of randomised trials with continuous outcomes.Statistics in medicine · 2023Trial
- Estimands for factorial trials.Statistics in medicine · 2022Trial
- Data Analysis Planning and Reporting for Confirmatory Multi-Lab Preclinical Trials: A Tutorial.Biometrical journal. Biometrische Zeitschrift · 2026Review
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- A Statistical Analysis Plan Template for Observational Studies: Promoting Quality and Rigor in Research.Journal of statistical theory and practice · 2025Article
- Analysis of interaction effect between within- and between-subject factors in repeated measures analysis of variance for longitudinal data.Korean journal of anesthesiology · 2025Article
- Multiplicity corrections in life sciences: challenges and consequences.International journal of epidemiology · 2025Article
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- Reporting of cluster randomised crossover trials: extension of the CONSORT 2010 statement with explanation and elaboration.BMJ (Clinical research ed.) · 2025Article
- ICU-acquired hypernatremia: Prevalence, patient characteristics, trajectory, risk factors, and outcomes.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2024Article
- Regression without regrets -initial data analysis is a prerequisite for multivariable regression.BMC medical research methodology · 2024Article
- Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers.Trials · 2023Article
- Estimands in cluster-randomized trials: choosing analyses that answer the right question.International journal of epidemiology · 2023Article
- Article
- The Effects of Dietary Protein Supplementation on Exercise-Induced Inflammation and Oxidative Stress: A Systematic Review of Human Trials.Antioxidants (Basel, Switzerland) · 2021Review
- Evidence of unexplained discrepancies between planned and conducted statistical analyses: a review of randomised trials.BMC medicine · 2020Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Results from clinical trials can be susceptible to bias if investigators choose their analysis approach after seeing trial data, as this can allow them to perform multiple analyses and then choose the method that provides the most favourable result (commonly referred to as 'p-hacking'). Pre-specification of the planned analysis approach is essential to help reduce such bias, as it ensures analytical methods are chosen in advance of seeing the trial data. For this reason, guidelines such as SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and ICH-E9 (International Conference for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) require the statistical methods for a trial's primary outcome be pre-specified in the trial protocol. However, pre-specification is only effective if done in a way that does not allow p-hacking. For example, investigators may pre-specify a certain statistical method such as multiple imputation, but give little detail on how it will be implemented. Because there are many different ways to perform multiple imputation, this approach to pre-specification is ineffective, as it still allows investigators to analyse the data in different ways before deciding on a final approach. In this article, we describe a five-point framework (the Pre-SPEC framework) for designing a pre-specified analysis approach that does not allow p-hacking. This framework was designed based on the principles in the SPIRIT and ICH-E9 guidelines and is intended to be used in conjunction with these guidelines to help investigators design the statistical analysis strategy for the trial's primary outcome in the trial protocol.
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