PubMed · 15063219
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Gary L Grunkemeier, YingXing Wu. 2004. Bootstrap resampling methods: something for nothing?. https://doi.org/10.1016/j.athoracsur.2004.01.005
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Meta analysis is a collection of quantitative methods devoted to combine summary information from related but independent studies. Because research reports usually present only data reductions and summary statistics rather than detailed data, the reviewer must often resort to rather crude methods for constructing summary effect estimate suitable for meta analysis pooling methods. When the studies involve a binary variable, both number of events and sample sizes are required to compute pooled estimate and its confidence interval. Sometimes, only summary statistics and related confidence intervals are provided in the publication. Although it is possible to estimate the standard error of each study's effect measure using the confidence interval from each study, this lack of detailed data compels the reviewers to use the inverse variance method to perform meta analysis, or to exclude the works with incomplete data. This paper shows three methods to reconstruct four-fold tables when summary measures for binary data and related confidence intervals and sample sizes are provided. The methods are discussed through a wider application example to assess the reconstruction precision, and the impact of using reconstructed data on meta analysis results. These methods seem to yield a correct reconstruction if original measures are reported at least with two decimal places. Meta analysis results do not seem seriously affected by the use of reconstructed data. These methods allow the reviewer to use full meta analysis statistical tools, instead of the simple inverse variance method, and can greatly contribute to the completeness of systematic reviews.
BACKGROUND: Animal models have implicated the alpha(1)-adrenergic subtypes in cognitive functions relevant to schizophrenia, but no consensus exists with regard to the status of noradrenergic receptor populations in psychiatric patients. We focused on one alpha(1)-adrenergic subtype, the alpha(1A)-adrenergic receptor, and proposed that genetic variants within the regulatory region of this gene (ADRA1A) alter the expression of this receptor, influencing susceptibility toward schizophrenia. METHODS: This study examined this proposal by testing the hypothesis that single nucleotide polymorphisms (SNPs) in the promoter region of the alpha(1A)-adrenergic gene were associated with schizophrenia by performing case-control association analysis on SNPs found in a 5' upstream region, which included the putative promoter region and 5' untranslated region. Our sample consisted of 103 schizophrenia and 14 schizoaffective disorder patients and 176 control subjects. All recruits were from a Spanish population isolate of Basque origin that is characterized by low heterogeneity, which was selected with the intent that it might facilitate the identification of disease-related polymorphisms. RESULTS: A total of eight SNPs (-9625 G/A, -7255 A/G, -6274 C/T, -4884 A/G, -4155 C/G, -2760 A/C, -1873 G/A, and -563 C/T) were confirmed at a rare allele frequency of >5%. Association with schizophrenia and schizoaffective disorder was found for the -563 C/T SNP (p = .0005 for allele and p = .007 for genotype, Bonferroni corrected) and -9625 G/A SNP (p = .02 for allele and p = .03 for genotype, Bonferroni corrected). Significant differences in the 54 haplotypes formed by these eight SNPs were also found between patients and control subjects (p = .008, Bonferroni corrected). CONCLUSIONS: Because of the strength of these results and the location of these SNPs in the regulatory region of this gene, functional studies investigating the possible influence of these SNPs on receptor expression levels in schizophrenia are warranted.
A recent investigation of hormone replacement therapy and breast cancer risk used a method called "floating absolute risks" (FARs) to compute confidence intervals for relative hazards. This method has been used in other medical studies and has received controversy. This controversy stems from the correct implementation of this method. However, there has been no direct comparison of the FAR method, as it is sometimes incorrectly applied and reported, with the conventional approach for computing confidence intervals from proportional hazards regression. In this paper, the author reports simulation results comparing these two methods and demonstrates that the FAR method, when applied incorrectly, can produce confidence intervals that are substantially too narrow.