Why a P-Value is Not Enough

Solla, Federico; Tran, Antoine; Bertoncelli, Domenico; Musoff, Charles; Bertoncelli, Carlo M.

doi:10.1097/bsd.0000000000000695

Cited by 55 publications

(35 citation statements)

References 16 publications

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“…ML is a contemporary branch of statistics and artificial intelligence for analysis of complex data using algorithms to find data patterns that are not apparent to humans and make predictions or infer new knowledge . In ML, “A computer program is said to learn from experience with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience.”…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Gastrostomy Placement in Adolescents With Developmental Disabilities and Cerebral Palsy

et al. 2019

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Background Factors associated with gastrostomy placement in adolescents with developmental disabilities (DDs) and cerebral palsy (CP) are poorly investigated. We aimed to develop and validate a machine learning (ML) model for gastrostomy placement in adolescents with DDs and CP. Methods We performed a multinational, double‐blinded, case‐control study including 130 adolescents with severe DD and CP (72 males, 58 females; mean age 16 ± 2 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, and functional assessments such as the Eating and Drinking Ability Classification System, Manual Ability Classification System, and Gross Motor Function Classification System were collected between 2005 and 2015. Analysis included Fisher exact test, multiple logistic regressions, and a supervised ML model, named PredictMed, to identify factors associated with gastrostomy placement. “Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis” guidelines were followed. Results Poor motor function (P < 0.001), trunk muscle tone disorder (P < 0.001), male gender (P < 0.01), epilepsy (P = 0.01), and severe neuromuscular scoliosis (P = 0.04) were factors linked with gastrostomy placement in univariate analysis. Epilepsy (P = 0.03), poor motor function (P = 0.04), and male gender (P = 0.04) were associated with gastrostomy placement in multivariate analysis with 95% accuracy. Conclusion Epilepsy, poor motor function, trunk muscles tone disorder, and male gender were accurate, sensitive, and specific factors associated with gastrostomy need.

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Section: Introductionmentioning

confidence: 99%

Predictive Model for Gastrostomy Placement in Adolescents With Developmental Disabilities and Cerebral Palsy

et al. 2019

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“…Initial analysis included using contingency tables and Fisher's exact test to identify associated factors, confidence intervals, and distribution frequencies for ADS versus etiologic, functional, and neurologic features. 27 The univariate analysis was done using OpenEpi software 28 and the MedCalc statistical software. 29 Second step: we generated all possible combinations (tuples) from the 10 independent variables based on Combinatorics theorems, 29 and got 2^10-1 ¼ 1,023 tuples (each tuple having from 1 to 10 elements).…”

Section: Discussionmentioning

confidence: 99%

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy

et al. 2019

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Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to investigate this field and this population in particular. The aim of this study is to develop and test a predictive learning model to identify factors associated with ASD in adolescents with CP. This was a multicenter controlled cohort study of 102 adolescents with CP (61 males, 41 females; mean age ± SD [standard deviation] = 16.6 ± 1.2 years; range: 12–18 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected between 2005 and 2015. Statistical analysis included Fisher's exact test and multiple logistic regressions to identify factors associated with ASD. A predictive learning model was implemented to identify factors associated with ASD. The guidelines of the “transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” (TRIPOD) statement were followed. Type of spasticity (hemiplegia > diplegia > tri/quadriplegia; OR [odds ratio] = 1.76, SE [standard error] = 0.2785, p = 0.04), communication disorders (OR = 7.442, SE = 0.59, p < 0.001), intellectual disability (OR = 2.27, SE = 0.43, p = 0.05), feeding abilities (OR = 0.35, SE = 0.35, p = 0.002), and motor function (OR = 0.59, SE = 0.22, p = 0.01) were significantly associated with ASD. The best average prediction model score for accuracy, specificity, and sensitivity was 75%. Motor skills, feeding abilities, type of spasticity, intellectual disability, and communication disorders were associated with ASD. The prediction model was able to adequately identify adolescents at risk of ASD.

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“…This means that for each tuple, we randomly re-shuffle the composition of training and test. On a total of 130 patients, 80% of them (104) are in the training set and 20% (26) in the test set. Composition of the training a test set was randomly determined through the R function createDa-taPartition().…”

Section: Cross Correlationmentioning

confidence: 99%

PredictMed: A logistic regression–based model to predict health conditions in cerebral palsy

Bertoncelli

Altamura

Vieira³

et al. 2020

Health Informatics J

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Logistic regression–based predictive models are widely used in the healthcare field but just recently are used to predict comorbidities in children with cerebral palsy. This article presents a logistic regression approach to predict health conditions in children with cerebral palsy and a few examples from recent research. The model named PredictMed was trained, tested, and validated for predicting the development of scoliosis, intellectual disabilities, autistic features, and in the present study, feeding disorders needing gastrostomy. This was a multinational, cross-sectional descriptive study. Data of 130 children (aged 12–18 years) with cerebral palsy were collected between June 2005 and June 2015. The logistic regression–based model uses an algorithm implemented in R programming language. After splitting the patients in training and testing sets, logistic regressions are performed on every possible subset (tuple) of independent variables. The tuple that shows the best predictive performance in terms of accuracy, sensitivity, and specificity is chosen as a set of independent variables in another logistic regression to calculate the probability to develop the specific health condition (e.g. the need for gastrostomy). The average of accuracy, sensitivity, and specificity score was 90%. Our model represents a novelty in the field of some cerebral palsy–related health outcomes treatment, and it should significantly help doctors’ decision-making process regarding patient prognosis.

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Why a P-Value is Not Enough

Abstract: Scientific publications require more parameters than a P-value. Statistical results should also include effect sizes and CIs to allow for a more complete, honest, and useful interpretation of scientific findings.

Cited by 55 publications

References 16 publications

Predictive Model for Gastrostomy Placement in Adolescents With Developmental Disabilities and Cerebral Palsy

Predictive Model for Gastrostomy Placement in Adolescents With Developmental Disabilities and Cerebral Palsy

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy

PredictMed: A logistic regression–based model to predict health conditions in cerebral palsy

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