title: What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.
abstract: OBJECTIVE
  To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of continuity corrections.
  
  
  BACKGROUND
  Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as continuity corrections are often needed, but, these can influence results and conclusions.
  
  
  METHODS
  A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative continuity corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes.
  
  
  RESULTS
  Mantel-Haenszel summary estimates using the alternative continuity correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative continuity corrections outperformed the constant correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the continuity correction used.
  
  
  CONCLUSIONS
  Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Sweeting
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Sutton
authors.forename: Paul C
authors.initials: PC
authors.lastname: Lambert
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000453:epidemiology; D004812:Epidemiologic Methods; D015914:Estrogen Replacement Therapy / Q000009:adverse effects; D005260:Female; D005323:Fetal Monitoring; D006339:Heart Rate; D006801:Humans; D007226:Infant Mortality; D007231:Infant, Newborn; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D011247:Pregnancy; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012307:Risk Factors; D018401:Sample Size; D013223:Statistics as Topic / Q000379:methods*