Three common misuses of <i>P</i> values

Kim, Jeehyoung; Bang, Heejung

doi:10.4103/2155-8213.190481

Cited by 47 publications

(35 citation statements)

References 26 publications

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“…Are they clinically relevant [ 13 , 22 , 26 , 27 ]? Despite substantially different study populations and sample sizes, dramatically different p-values for two validated outcomes (CHD and hypertension) are noteworthy: p-values<0.001 adjusted for 1,688 comparisons (or p-value ~10 -22 unadjusted using our best guess from the Manhattan plot) in the original study vs. unadjusted p-values=0.03-0.58 in our study [ 28 , 29 ]. When a number of p-values − probably the most popular statistical measure in research − are computed, a simple ‘p-value plot’ together with AUC could be helpful for assessing overall randomness in associations [ 8 , 30 , 31 ].…”

Section: Discussionmentioning

confidence: 95%

Birth Month and Cardiovascular Disease Risk Association: Is meaningfulness in the eye of the beholder?

Poltavskiy

Spence

Kim³

et al. 2016

OJPHI

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In the modern era, with high-throughput technology and large data size, associational studies are actively being generated. Some have statistical and clinical validity and utility, or at least have biologically plausible relationships, while others may not. Recently, the potential effect of birth month on lifetime disease risks has been studied in a phenome-wide model. We evaluated the associations between birth month and 5 cardiovascular disease-related outcomes in an independent registry of 8,346 patients from Ontario, Canada in 1977-2014. We used descriptive statistics and logistic regression, along with model-fit and discrimination statistics. Hypertension and coronary heart disease (of primary interest) were most prevalent in those who were born in January and April, respectively, as observed in the previous study. Other outcomes showed weak or opposite associations. Ancillary analyses (based on raw blood pressures and subgroup analyses by sex) demonstrated inconsistent patterns and high randomness. Our study was based on a high risk population and could not provide scientific explanations. As scientific values and clinical implications can be different, readers are encouraged to read the original and our papers together for more objective interpretations of the potential impact of birth month on individual and public health as well as toward cumulative/total evidence in general.

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Section: Discussionmentioning

confidence: 95%

Birth Month and Cardiovascular Disease Risk Association: Is meaningfulness in the eye of the beholder?

Poltavskiy

Spence

Kim³

et al. 2016

OJPHI

Self Cite

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“…This could be explained by a larger sample size (i.e. crude frequency that is not unadjusted for different factors) and smaller p-value (tendency to be significant) effect [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Effect of smoking on tuberculosis treatment outcomes: A systematic review and meta-analysis

Burusie¹,

Enquesilassie

Addissie

et al. 2020

PLoS ONE

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Introduction Numerous studies have explored an effect of cigarette smoking on tuberculosis treatment outcomes but with dissimilar conclusions. Objective To determine the effect of cigarette smoking on tuberculosis treatment outcomes. Methods PubMed, Cochrane library and Google scholar databases were searched last on February 27, 2019. We applied the random-effects model for the analysis. Publication bias was assessed using funnel plot and Egger’s regression. Furthermore, we performed Orwin’s Fail-Safe N and cumulative meta-analysis to check for small studies’ effect. Results Out of 22 studies we included in the qualitative synthesis, 12 studies reported p-values less than 0.05 where smoking significantly favored poor treatment outcomes. The remaining 10 studies reported p-values larger than 0.05 implying that smoking does not affect the treatment outcomes. Twenty studies met the criteria for inclusion in a meta-analysis. The meta-analysis found that smoking significantly increased the likelihood of poor tuberculosis treatment outcomes by 51% (OR = 1.51; 95% CI = 1.30 to 1.75 and I-square = 75.1%). In a sub-group analysis, the effect was higher for low- and middle-income countries (OR = 1.74; 95% CI = 1.31 to 2.30) and upper-middle-income economies (OR = 1.52; 95% CI = 1.16 to 1.98) than for high-income ones (OR = 1.34; 95% CI = 1.03 to 1.75) even though the differences in the effects among the strata were not statistically significant as demonstrated by overlapping of confidence intervals of the effects. Meta-regression analysis, adjusted for income economies, found the effect of smoking has not significantly improved over the years (p = 0.92) and thus implying neither of the covariates were source of the heterogeneity. Egger’s regression test indicated that publication bias is unlikely (p = 0.403). Conclusion Cigarette smoking is significantly linked with poor tuberculosis treatment outcomes.

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“…However, the L1 group's minimum FOM was equal to its median FOM, which was equal to 0. This highlights the limitations of hypothesis testing, which can be misleading, as several authors have concluded [48][49][50][51]. Here, differences among L1 and other aggregation methods were not significant, but half of L1 s FOM values were less than the lower quartiles for all other aggregations.…”

Section: Effects Of Fom and Fom Componentsmentioning

confidence: 58%

Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data

et al. 2020

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Several factors influence the performance of land change simulation models. One potentially important factor is land category aggregation, which reduces the number of categories while having the potential to reduce also the size of apparent land change in the data. Our article compares how four methods to aggregate Corine Land Cover categories influence the size of land changes in various spatial extents and consequently influence the performance of 114 Cellular Automata-Markov simulation model runs. We calculated the reference change during the calibration interval, the reference change during the validation interval and the simulation change during the validation interval, along with five metrics of simulation performance, Figure of Merit and its four components: Misses, Hits, Wrong Hits and False Alarms. The Corine Standard Level 1 category aggregation reduced change more than any of the other aggregation methods. The model runs that used the Corine Standard Level 1 aggregation method tended to return lower sizes of changing areas and lower values of Misses, Hits, Wrong Hits and False Alarms, where Hits are correctly simulated changes. The behavior-based aggregation method maintained the most change while using fewer categories compared to the other aggregation methods. We recommend an aggregation method that maintains the size of the reference change during the calibration and validation intervals while reducing the number of categories, so the model uses the largest size of change while using fewer than the original number of categories.

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Three common misuses of P values

Cited by 47 publications

References 26 publications

Birth Month and Cardiovascular Disease Risk Association: Is meaningfulness in the eye of the beholder?

Birth Month and Cardiovascular Disease Risk Association: Is meaningfulness in the eye of the beholder?

Effect of smoking on tuberculosis treatment outcomes: A systematic review and meta-analysis

Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data

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