Biostatistics and Research Methods for Ophthalmologists

Last updated: October 2026

The statistics, study design and research ethics that appear in every ophthalmology exam and every journal club, explained with eye examples. Includes a calculator for diagnostic test accuracy.

Diagnostic tests: the 2 × 2 table

• Sensitivity = TP ÷ (TP + FN): of those with disease, how many test positive. A sensitive test, if negative, rules disease out (SnNout).
• Specificity = TN ÷ (TN + FP): of those without disease, how many test negative. A specific test, if positive, rules disease in (SpPin).
• Positive predictive value = TP ÷ (TP + FP) and negative predictive value = TN ÷ (TN + FN). These depend on prevalence: screening for glaucoma in the general population (low prevalence) gives many false positives even with a good test.
• Likelihood ratios: LR+ = sensitivity ÷ (1 − specificity); LR− = (1 − sensitivity) ÷ specificity. Unlike predictive values, they do not change with prevalence.

The default numbers show a test with 90% sensitivity and 90% specificity used where prevalence is 2%: the positive predictive value is only about 16%.

Measures of effect

• Confidence interval: the range of values compatible with the data. If a 95% CI for an RR or OR includes 1 (or for a difference includes 0), the result is not statistically significant at p < 0.05.
• p-value: the probability of a result at least this extreme if there were truly no difference. It does not measure the size or importance of an effect.
• Type I error (α): finding a difference that is not real (false positive). Type II error (β): missing a real difference. Power = 1 − β, usually set at 80–90%; it rises with sample size and effect size.

Study designs and the evidence pyramid

Common biases

• Selection bias: the study population does not represent the target population (for example, tertiary hospital patients used to estimate community prevalence).
• Lead-time bias: earlier diagnosis by screening makes survival look longer even if outcome is unchanged. Length-time bias: screening picks up slower, milder disease.
• Recall bias (case–control studies), observer bias (reduced by masking), attrition bias (dropouts), and publication bias.
• Confounding: a third factor linked to both exposure and outcome (for example, age in studies of diabetes and cataract). Randomisation, matching and multivariable analysis reduce it.

Trials: key terms

• Intention-to-treat: analyse people in the groups they were randomised to, whatever happened; preserves randomisation. Per-protocol: only those who completed treatment as planned.
• Masking (blinding): single (patient), double (patient and assessor). Non-inferiority trials test whether a new treatment is not worse than the standard by more than a set margin (for example faricimab vs aflibercept).
• Survival analysis: Kaplan–Meier curves for time-to-event data such as graft survival or trabeculectomy failure, compared with the log-rank test; hazard ratios from Cox regression.

Agreement and reliability

• Bland–Altman plot: agreement between two measurement methods (for example two tonometers): plots the difference against the mean, with the mean bias and 95% limits of agreement. Correlation is not agreement.
• Kappa statistic: agreement between observers beyond chance for categories (for example grading of diabetic retinopathy): above 0.8 is very good, 0.6–0.8 good.
• Intraclass correlation: reliability of continuous measurements.

Research ethics and consent

• The Declaration of Helsinki sets the principles for research involving people; studies need ethics committee approval and informed consent. In India, research follows the ICMR National Ethical Guidelines and trials are registered with CTRI.
• Consent for treatment: patients must be told about material risks, benefits and reasonable alternatives (UK: Montgomery 2015). Record the discussion, not just a signature.
• Confidentiality can be broken only in limited situations, such as serious risk to others (for example an unsafe driver who refuses to stop).

Related: landmark trials, thesis topics, how to write a case report.

Key sources

• Kirkwood BR, Sterne JAC. Essential Medical Statistics, 2nd ed. Blackwell; 2003
• Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307–10
• Indian Council of Medical Research. National Ethical Guidelines for Biomedical and Health Research Involving Human Participants, 2017
• World Medical Association. Declaration of Helsinki (2024 revision)