Assessment of P values for demographic data in randomized controlled trials

Ahn, Eun Jin; Kim, Jong Hae; Kim, Tae Kyun; Park, Jae Hong; Lee, Dong Kyu; Lee, Sang Seok; In, Junyong; Kang, Hyun

doi:10.4097/kja.d.18.00333

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Although participants will be randomly assigned (in groups of about 5-7 individuals) to either the TMI Adjunct or Standard Care Alone conditions, imbalance between the two groups on some of the baseline characteristics may occur. However, since assignment to treatment condition is random, any differences that arise are due to random chance [57,58]. We will present balance tables of baseline data to confirm the two groups are similar.…”

Section: Preliminary Analysesmentioning

confidence: 90%

A text message intervention for quitting cigarette smoking among young adults experiencing homelessness: study protocol for a pilot randomized controlled trial

Tucker

Pedersen

Linnemayr

et al. 2020

Addict Sci Clin Pract

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Background: Cigarette smoking is much more prevalent among young people experiencing homelessness than in the general population of adolescents and young adults. Although many young homeless smokers are motivated to quit, there are no empirically-evaluated smoking cessation programs for this population. It is important that any such program address the factors known to be associated with quitting-related outcomes among homeless young people, to provide ongoing support in a way that accommodates the mobility of this population, and does not rely on scarce service provider resources for its delivery. The objective of this project is to develop and pilot test a text messagingbased intervention (TMI), as an adjunct to brief cessation counseling and provision of nicotine patches, to help homeless young people who want to quit smoking. Methods/design: This pilot study will utilize a cluster cross-over randomized controlled design with up to 80 current smokers who desire to quit and are recruited from three drop-in centers serving young people experiencing homelessness in the Los Angeles area. All participants will be provided with a minimum standard of care: a 30-min groupbased smoking cessation counseling session and free nicotine replacement. Half of these smokers will then also receive the TMI, as an adjunct to this standard care, which will provide 6 weeks of ongoing support for quitting. This support includes continued and more intensive education regarding nicotine dependence, quitting smoking, and relapse; does not require additional agency resources; can be available "on demand" to users; and includes features to personalize the quitting experience. This study will investigate whether receiving the TMI adjunct to standard smoking cessation care results in greater reductions in cigarette smoking compared to standard care alone over a 3-month period. Discussion: This study has the potential to address an important gap in the clinical research literature on cigarette smoking cessation and provide empirical support for using a TMI to provide ongoing assistance and support for quitting among young smokers experiencing homelessness. Trial registration ClinicalTrials.gov Identifier NCT03874585. Registered March 14, 2019, https ://clini caltr ials.gov/ct2/ show/recor d/NCT03 87458 5.

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Section: Preliminary Analysesmentioning

confidence: 90%

A text message intervention for quitting cigarette smoking among young adults experiencing homelessness: study protocol for a pilot randomized controlled trial

Tucker

Pedersen

Linnemayr

et al. 2020

Addict Sci Clin Pract

View full text Add to dashboard Cite

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Section: Relation Previous Workmentioning

confidence: 99%

“…Previous studies have examined the percentage of randomised trials that erroneously used p-values to compare randomised groups [1,44]. A study of over 300 trials from 2010 to 2017 in one journal and found 27% used p-values in tables [44]. An older study examined 50 trials from four high pro le journals and found that 48% included signi cance tests [1].…”

Section: Relation Previous Workmentioning

confidence: 99%

Automated detection of over- and under-dispersion in baseline tables in randomised controlled trials

Barnett

2022

Preprint

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Background: Papers describing the results of a randomised trial should include a table of summary statistics that compares randomised groups at baseline. Researchers who fraudulently generate trials often create randomised groups that are implausibly similar (under-dispersed) or accidentally create large differences between groups (over-dispersed) because they do not understand how to create realistic summary statistics from truly random data. We aimed to automatically screen for under- and over-dispersion in the baseline tables of published randomised trials.Methods: We examined randomised controlled trials published in open access health and medical journals on PubMed Central. We estimated probability that a trial’s summary statistics were under- or over-dispersed using a Bayesian model that examined the distribution of t-statistics for the between-group differences from the baseline table, and compared this with an expected distribution without dispersion. We used a simulation study to test the ability of the model to find over- or under-dispersion and compared its performance with an existing test of dispersion based on a uniform test of p-values.Results: The algorithm had a relatively good accuracy for extracting the data from baseline tables, matching well on the size of the tables and sample size. Using t-statistics in the Bayesian model out-performed the uniform test of p-values, as it had a higher probability of detecting over-dispersed data and lower false positive percentage for skewed data that was not under- or over-dispersed. For the trials published on PubMed Central, some tables were flagged because: they were not from trials, had a non-standard presentation, or had likely reporting errors. The algorithm also flagged some trials as under-dispersed where there was a striking similarity between groups.Conclusion: Due to the variance in reporting across journals, the current method may not be suitable for automated screening for fraud of all submitted trials, but could be useful in targeted checks of suspected trials. If used in automated screening of papers at the submission stage, it would likely flag many instances of non-standard table presentation and poor reporting.

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“…In this issue of the Korean Journal of Anesthesiology (KJA), Ahn et al [5] evaluated a total of 312 randomized controlled trials published in the Journal of Anesthesiology between 2010 and 2017 analyzing Table 1 presented in each study. The authors concluded that, unlike the intentions of the researchers, it was not important to present P values in Table 1 as a means of showing that the random assignment has been carried out properly.…”

mentioning

confidence: 99%