Small-Sample Bias of Point Estimators of the Odds Ratio from Matched Sets

Jewell, Nicholas P.

doi:10.2307/2531395

Cited by 72 publications

(43 citation statements)

References 15 publications

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“…With small numbers, bias can be severe. 17 We observed some bias, generally away from the null, which increased as the pool size increased. This bias is attributable to the decreasing number of pooling sets, which reduces the effective sample size.…”

Section: Discussionmentioning

confidence: 66%

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Pooled Exposure Assessment for Matched Case-control Studies

2011

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Exposure assessment using biologic specimens is important for epidemiology but may become impracticable if assays are expensive, specimen volumes are marginally adequate, or analyte levels fall below the limit of detection. Pooled exposure assessment can provide an effective remedy for these problems in unmatched case-control studies. We extend pooled exposure strategies to handle specimens collected in a matched case-control study. We show that if a logistic model applies to individuals, then a logistic model also applies to an analysis using pooled exposures. Consequently, the individual-level odds ratio can be estimated while conserving both cost and specimen. We discuss appropriate pooling strategies for a single exposure, with adjustment for multiple, possibly continuous, covariates(confounders)and assessment of effect modification by a categorical variable. We assess the performance of the approach via simulations and conclude that pooled strategies can markedly improve efficiency for matched as well as unmatched case-control studies.

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Section: Discussionmentioning

confidence: 66%

“…Nevertheless, when employing pooled exposure assessment, one should consider this issue and may need to adopt alternative estimators to cope with small numbers. 17 …”

Section: Discussionmentioning

confidence: 99%

Pooled Exposure Assessment for Matched Case-control Studies

2011

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“…This "" (n1o+O.S) was 'tH = ln 0 5 , which seemed to perform better in Jewell's (1984) no1+ · comparison of several sample estimators of the log odds ratio. Medians were used as a robust measure since forming ratios sometimes produced extreme values.…”

Section: 3) Results and Discussionmentioning

confidence: 97%

When Does It Pay to Break the Matches for Analysis of a Matched-Pairs Design?

Lynn¹,

McCulloch²

1992

Biometrics

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SUMMARYTwo methods of analyses are compared to estimate the treatment effect of a comparative study where each treated individual is matched with a single control at the design stage. The usual matched pairs analysis accounts for the pairing directly in its model, whereas regression adjustment ignores the matching but instead models the pairing using a set of covariates. For a normal linear model, the estimated treatment effect from the matched pairs analysis (paired t-test) is more efficient. For a Bernoulli logistic model, matched pairs analysis performs better when the sample size was small, but is inferior to logistic regression for large sample sizes.

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“…While these estimators of the (adjusted) log-odds ratio have attractive asymptotic properties (e.g., unbiasedness and normality), these properties do not to apply in small samples. For example, the logit coefficients suffer from small sample bias [4, 5], leading to systematically overestimated associations. Also, asymptotic confidence intervals often do not have nominal coverage rates in studies with small data sets [12, 15].…”

Section: Introductionmentioning

confidence: 99%

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

Smeden

Groot

Moons

et al. 2016

BMC Med Res Methodol

341

272

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BackgroundTen events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies.MethodsThe current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared.ResultsThe results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation.ConclusionsThe current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

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Small-Sample Bias of Point Estimators of the Odds Ratio from Matched Sets

Cited by 72 publications

References 15 publications

Pooled Exposure Assessment for Matched Case-control Studies

Pooled Exposure Assessment for Matched Case-control Studies

When Does It Pay to Break the Matches for Analysis of a Matched-Pairs Design?

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

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