Paeds · professional-practice-and-evidence

Biostatistics for paediatric exams

Also known as Medical statistics for paediatric fellowship · Descriptive and inferential statistics in child health · Confidence intervals, p-values and hypothesis testing · Choosing the right statistical test · Correlation, regression and survival analysis

Fellowship guide to the biostatistics a paediatric candidate must read, calculate and defend: classifying data, summarising with mean, median, standard deviation and interquartile range, the normal distribution and the standard error, confidence intervals and p-values, Type I and Type II errors, power and sample size, choosing the correct comparison and association test, correlation and regression, survival analysis, multiple comparisons, and paediatric-specific issues of growth centiles, z-scores and clustering — with ANZ, UK, US and Canada teaching anchors.

high13 referencesUpdated 11 July 202620 min readVerification in progress

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Target exams

RACP General PaediatricsRACP DWERACP DCERCPCH Progress+MRCPCH TheoryMRCPCH ClinicalABP General PediatricsACGME PediatricsRCPSC Pediatrics

Red flags

Reading a small p-value as a large or clinically important effectTreating a confidence interval that crosses the null as a positive resultChoosing a parametric test when the assumptions of normality or equal variance are not metIgnoring multiple comparisons and data-dredged subgroup analysesConflating correlation with causation, or a confounder with a true predictorApplying an adult-derived threshold or centile to a child without age- and sex-standardisation

Life stages

neonateinfanttoddlerpreschoolschool-ageadolescent

Care settings

preventive-medical-homecommunity-schooloutpatientwarded-acutenicupicutelehealth

Clinical exam formats

written-onlyracp-dce-long-caseracp-dce-short-casemrcpch-history-managementrcpsc-structured-oral

Board mappings

General and Community PaediatricsBiostatistics and critical appraisalRenewed curriculum — Learning goal 12: Critically appraising evidence and applying it to practiceScholarly activity and research methodsProfessional practice and ethicsInterpretation of statistical results in written papersStructured discussion of a statistical result or a growth-chart findingLong Cases integrating effect size and confidence intervals with patient valuesShort Cases on interpreting a forest plot, survival curve, or regression output2. Professional skills and knowledge: Evidence-based practice and research methodologyQuality improvement and patient safety themesSafeguarding and screening themesFoundation of Practice (FOP)Applied Knowledge in Practice (AKP)CommunicationHistory taking and managementChild developmentGeneral Pediatrics Content Outline — evidence-based medicine, biostatistics, and critical appraisalGeneral Pediatrics EPA: Interpreting quantitative evidence and growth data in patient careBioethics and professional conductPractice-based Learning and Improvement: interpreting statistical evidence to guide careSystems-based Practice: applying quantitative evidence appropriatelyProfessionalismScholarMedical ExpertCollaboratorHealth AdvocatePediatrics EPA — interpreting quantitative evidence and growth data

Your progress

Saved locally on this device.

Practise this topic

Target exams

RACP General PaediatricsRACP DWERACP DCERCPCH Progress+MRCPCH TheoryMRCPCH ClinicalABP General PediatricsACGME PediatricsRCPSC Pediatrics

Red flags

Life stages

neonateinfanttoddlerpreschoolschool-ageadolescent

Care settings

preventive-medical-homecommunity-schooloutpatientwarded-acutenicupicutelehealth

Clinical exam formats

written-onlyracp-dce-long-caseracp-dce-short-casemrcpch-history-managementrcpsc-structured-oral

Board mappings

The fellowship answer

Read the effect size and its confidence interval before you read the p-value. Statistics answers two questions about a child: how big is the effect, and how sure are we. Descriptive statistics summarise a sample with the right measure of centre and spread for the data type. Inferential statistics ask whether an observed difference is more than chance could explain, using a confidence interval for the size and precision of the estimate and a p-value for the strength of evidence against the null hypothesis. A small p-value on its own says nothing about importance, so report the effect size with its 95 percent confidence interval, check the right test was used for the data, and never let a lone p-value do your clinical thinking. In a child, remember the paediatric extras: standardise growth by age and sex, watch for clustering when children are nested in schools or wards, and treat an underpowered subgroup analysis with the suspicion it deserves. [4] [5] [6]

Two questions statistics answers

Descriptive statistics

Inferential statistics

Read any statistical result in the right order

Data type — categorical or numerical · Estimate — mean or median, with its spread · Test — the right one for the data, with assumptions checked · Effect size — how big, with its 95 percent confidence interval · Context — p-value read in context, confounding considered, applied to the child. Remember it as D-E-T-E-C: data, estimate, test, effect, context. Never read the p-value before you know the effect size. [4] [6]

Overview & Definition

A registrar hands you a trial abstract claiming a new therapy reduces the length of a child's hospital stay, and you must decide whether the result is real, large enough to matter, and applicable to your patient. The abstract reports a p-value and a confidence interval, a mean difference and a test you half remember. Before you can use the number, you must read it correctly. [4]

Biostatistics is the branch of statistics applied to health and biology. Descriptive statistics summarise the data you collected, while inferential statistics use a sample to reason about the population it came from, always carrying an explicit measure of uncertainty. A confidence interval quantifies that uncertainty around an estimate, and a p-value measures the strength of evidence against a null hypothesis. The two together — an effect size with its interval, read against a p-value — are the engine of quantitative inference. [5] [9]

This page owns the descriptive and inferential methods a fellowship candidate must read, calculate and defend in paediatric exams. The measures of risk and benefit — relative risk, absolute risk reduction, number needed to treat — belong to the clinical epidemiology leaf, the test characteristics of sensitivity and specificity belong to the diagnostic accuracy leaf, and the pooling of studies into a forest plot belongs to the systematic reviews leaf. Read this page as the statistical foundation beneath all of them. [6]

Paediatrics Fellowship Pro

You have read the opening of this topic. The complete unit — every section and its primary-source references — is part of the Paediatrics Fellowship fellowship atlas.

References13Show ledger

[1]Bland JM, Altman DG Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1986.PMID 2868172
[2]Bland JM, Altman DG Multiple significance tests: the Bonferroni method. BMJ, 1995.PMID 7833759
[3]Akobeng AK Understanding type I and type II errors, statistical power and sample size. Acta Paediatr, 2016.PMID 26935977
[4]Sullivan GM, Feinn R Using Effect Size-or Why the P Value Is Not Enough. J Grad Med Educ, 2012.PMID 23997866
[5]Nakagawa S, Cuthill IC Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc, 2007.PMID 17944619
[6]Sedgwick PM, Hammer A, Kesmodel US, Pedersen LH Current controversies: Null hypothesis significance testing. Acta Obstet Gynecol Scand, 2022.PMID 35451497
[7]Cortés J, González JA, Campbell MJ, Cobo E A hazard ratio was estimated by a ratio of median survival times, but with considerable uncertainty. J Clin Epidemiol, 2014.PMID 25063554
[8]Sedgwick P Spearman's rank correlation coefficient. BMJ, 2014.PMID 25432873
[9]Altman DG, Bland JM How to obtain the confidence interval from a P value. BMJ, 2011.PMID 21824904
[10]Altman DG, Bland JM Uncertainty beyond sampling error. BMJ, 2014.PMID 25424583
[11]Concato J, Peduzzi P, Holford TR, Feinstein AR Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol, 1995.PMID 8543963
[12]Sedgwick P What are the odds? BMJ, 2015.PMID 25934660
[13]Freeman JV, Cole TJ, Chinn S, Jones PR, White EM, Preece MA Cross sectional stature and weight reference curves for the UK, 1990. Arch Dis Child, 1995.PMID 7639543

Biostatistics for paediatric exams

high13 referencesUpdated 11 July 202620 min readVerification in progress

Practise this topic

Overview & Definition

Paediatrics Fellowship Pro

You have read the opening of this topic. The complete unit — every section and its primary-source references — is part of the Paediatrics Fellowship fellowship atlas.

References13Show ledger

[1]Bland JM, Altman DG Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1986.PMID 2868172
[2]Bland JM, Altman DG Multiple significance tests: the Bonferroni method. BMJ, 1995.PMID 7833759
[3]Akobeng AK Understanding type I and type II errors, statistical power and sample size. Acta Paediatr, 2016.PMID 26935977
[4]Sullivan GM, Feinn R Using Effect Size-or Why the P Value Is Not Enough. J Grad Med Educ, 2012.PMID 23997866
[5]Nakagawa S, Cuthill IC Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc, 2007.PMID 17944619
[6]Sedgwick PM, Hammer A, Kesmodel US, Pedersen LH Current controversies: Null hypothesis significance testing. Acta Obstet Gynecol Scand, 2022.PMID 35451497
[7]Cortés J, González JA, Campbell MJ, Cobo E A hazard ratio was estimated by a ratio of median survival times, but with considerable uncertainty. J Clin Epidemiol, 2014.PMID 25063554
[8]Sedgwick P Spearman's rank correlation coefficient. BMJ, 2014.PMID 25432873
[9]Altman DG, Bland JM How to obtain the confidence interval from a P value. BMJ, 2011.PMID 21824904
[10]Altman DG, Bland JM Uncertainty beyond sampling error. BMJ, 2014.PMID 25424583
[11]Concato J, Peduzzi P, Holford TR, Feinstein AR Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol, 1995.PMID 8543963
[12]Sedgwick P What are the odds? BMJ, 2015.PMID 25934660
[13]Freeman JV, Cole TJ, Chinn S, Jones PR, White EM, Preece MA Cross sectional stature and weight reference curves for the UK, 1990. Arch Dis Child, 1995.PMID 7639543