PubMed · 1543389
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M J Healy. 1992. Statistics from the inside. 4. Sampling distributions.. https://doi.org/10.1136/adc.67.2.249
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BACKGROUND: There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions. METHODS: We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects. RESULTS: Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population. CONCLUSION: Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population.
A natural randomization process, sometimes called Mendelian randomization, occurs at conception to determine a person's genotype. By combining information from genotype-disease and genotype-phenotype studies, it is possible to use this Mendelian randomization to derive an estimate of the association between phenotype (risk factor) and disease that is free of the confounding and reverse causation typical of classical epidemiology. When one is synthesizing evidence, studies evaluating genotype-phenotype associations, studies evaluating genotype-disease associations, and studies evaluating both are encountered, and methods should be used that allow for this structure. Plotting the log odds ratio of genotype-disease against the mean genotype-phenotype difference may help investigators detect departures from the assumptions underlying Mendelian randomization. Testing for differences between studies reporting on only the genotype-phenotype or genotype-disease association and those reporting on both associations may help in detecting reporting bias. This integrated approach to the meta-analysis of genotype-phenotype and genotype-disease studies is illustrated here using the example of the methylenetetrahydrofolate reductase (MTHFR) gene, homocysteine level, and coronary heart disease. An integrated meta-analytical approach may increase the precision of this estimate and provide information on the assumptions underlying Mendelian randomization. Serious biases may arise if the assumptions behind the analysis based on Mendelian randomization are not met.
BACKGROUND: In the diagnosis of rhabdomyolysis, the microconcentrator qualitative assay for urine myoglobin (uMb) is often used as a screening tool. The accuracy and clinical utility of this assay in screening patients with rhabdomyolysis have not been examined. METHODS: We conducted a retrospective analysis of the relationship between creatine kinase (CK), serum myoglobin (sMb), the urine qualitative assay for myoglobin and the semi-quantitative assay for urine haem pigments (uH) in patients evaluated for rhabdomyolysis. RESULTS: There were 673 patients with CK and uMb recorded on the same day. The uMb assay had a sensitivity of only 26.4% [95% confidence interval (CI): 23.1-29.7%] and specificity of 96.8% (95% CI: 95.5-98.1%) for the detection of severe rhabdomyolysis, defined as a CK >10 000 U/l. SMb and CK measured simultaneously in 83 patients were highly correlated (R(2) = 0.72 for log-transformed values), suggesting that the negative uMb test was not a result of the absence of sMb. In 241 patients who had CK, uMb and uH measured on the same day, the presence of 'moderate' or 'large' uH in the absence of haematuria, indicating presence of myoglobinuria, had a sensitivity of 81% (95% CI: 76-86%) for the detection of CK >10 000 U/l vs a sensitivity of 22% (95% CI: 17-27%) for the uMb assay. CONCLUSIONS: The microconcentrator-based uMb assay has a poor and clinically inadequate sensitivity in the detection and diagnosis of rhabdomyolysis.