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.
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Target exams
Red flags
- Reading a small p-value as a large or clinically important effect
- Treating a confidence interval that crosses the null as a positive result
- Choosing a parametric test when the assumptions of normality or equal variance are not met
- Ignoring multiple comparisons and data-dredged subgroup analyses
- Conflating correlation with causation, or a confounder with a true predictor
- Applying an adult-derived threshold or centile to a child without age- and sex-standardisation
Life stages
Care settings
Clinical exam formats
Board mappings
- General and Community Paediatrics
- Biostatistics and critical appraisal
- Renewed curriculum — Learning goal 12: Critically appraising evidence and applying it to practice
- Scholarly activity and research methods
- Professional practice and ethics
- Interpretation of statistical results in written papers
- Structured discussion of a statistical result or a growth-chart finding
- Long Cases integrating effect size and confidence intervals with patient values
- Short Cases on interpreting a forest plot, survival curve, or regression output
- 2. Professional skills and knowledge: Evidence-based practice and research methodology
- Quality improvement and patient safety themes
- Safeguarding and screening themes
- Foundation of Practice (FOP)
- Applied Knowledge in Practice (AKP)
- Communication
- History taking and management
- Child development
- General Pediatrics Content Outline — evidence-based medicine, biostatistics, and critical appraisal
- General Pediatrics EPA: Interpreting quantitative evidence and growth data in patient care
- Bioethics and professional conduct
- Practice-based Learning and Improvement: interpreting statistical evidence to guide care
- Systems-based Practice: applying quantitative evidence appropriately
- Professionalism
- Scholar
- Medical Expert
- Collaborator
- Health Advocate
- Pediatrics EPA — interpreting quantitative evidence and growth data
Two questions statistics answers
Descriptive statistics
Inferential statistics
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]
References13ShowHide
- [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