Sample size estimation for case-crossover studies.
Stat Med
; 38(6): 956-968, 2019 03 15.
Article
em En
| MEDLINE
| ID: mdl-30397907
ABSTRACT
Case-crossover study designs are observational studies used to assess postmarket safety of medical products (eg, vaccines or drugs). As a case-crossover study is self-controlled, its advantages include better control for confounding because the design controls for any time-invariant measured and unmeasured confounding and potentially greater feasibility as only data from those experiencing an event (or cases) are required. However, self-matching also introduces correlation between case and control periods within a subject or matched unit. To estimate sample size in a case-crossover study, investigators currently use Dupont's formula (Biometrics 1988; 431157-1168), which was originally developed for a matched case-control study. This formula is relevant as it takes into account correlation in exposure between controls and cases, which are expected to be high in self-controlled studies. However, in our study, we show that Dupont's formula and other currently used methods to determine sample size for case-crossover studies may be inadequate. Specifically, these formulas tend to underestimate the true required sample size, determined through simulations, for a range of values in the parameter space. We present mathematical derivations to explain where some currently used methods fail and propose two new sample size estimation methods that provide a more accurate estimate of the true required sample size.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Tamanho da Amostra
/
Estudos Cross-Over
Tipo de estudo:
Clinical_trials
/
Observational_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article