Matched-pair design is often used in clinical trials to increase the efficiency of establishing equivalence between two treatments with binary outcomes. In this article, we consider such a design based on rate ratio in the presence of incomplete data. The rate ratio is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this article, we propose 10 confidence-interval estimators for the rate ratio in incomplete matched-pair designs. A hybrid method that recovers variance estimates required for the rate ratio from the confidence limits for single proportions is proposed. It is noteworthy that confidence intervals based on this hybrid method have closed-form solution. The performance of the proposed confidence intervals is evaluated with respect to their exact coverage probability, expected confidence interval width, and distal and mesial noncoverage probability. The results show that the hybrid Agresti-Coull confidence interval based on Fieller's theorem performs satisfactorily for small to moderate sample sizes. Two real examples from clinical trials are used to illustrate the proposed confidence intervals.
Potassium-modified graphitic carbon nitride (K-g-C3N4) nanosheets are synthesized by a facile KCl-template method that holds the advantage of easy removal of residual template. A combination of XRD, X-ray photoelectron spectroscopy, and inductively coupled plasma analyses are utilized to characterize the obtained resultant K-g-C3N4 architectures, which are composed of nanosheets of variable thickness (<10?nm). Photocatalytic hydrogen evolution experiments under visible light irradiation showed that K-g-C3N4 nanosheets have high photocatalytic activities (up to about thirteen times higher than that of pure g-C3 N4 ) as well as good stability (no reduction in activity within 16?h); both features emanate from their unique structural characteristics. These results illustrate the viability of this methodology for the facile synthesis of efficient heterogeneous photocatalysts for potential commercial applications.
This meta-analysis was performed to assess the relationships between the PON1 Q192R (rs662 T>C) polymorphism and the clinical outcome of antiplatelet treatment after percutaneous coronary intervention (PCI). A range of electronic databases were searched: Web of Science (1945-2013), the Cochrane Library Database (Issue 12, 2013), PubMed (1966-2013), EMBASE (1980-2013), CINAHL (1982-2013) and the Chinese Biomedical Database (CBM) (1982-2013) without language restrictions. Meta-analysis was conducted using the STATA 12.0 software. The crude odds ratio (OR) with their 95 % confidence interval (CI) were calculated. Six clinical cohort studies with a total number of 5,189 patients undergoing PCI for coronary heart disease were included. Our meta-analysis revealed that the PON1 Q192R polymorphism was correlated with an increased risk of major adverse cardiovascular events (MACE) in patients receiving antiplatelet treatment after PCI (C allele vs. T allele: OR = 1.22, 95 % CI 1.04-1.43, P = 0.014; CT+CC vs. TT: OR = 1.38, 95 % CI 1.03-1.86, P = 0.029; CC vs. TT: OR = 1.45, 95 % CI 1.05-1.99, P = 0.024; respectively), especially among Asians. Furthermore, we found significantly positive correlations between the PON1 Q192R polymorphism and the incidence of stent thrombosis in patients receiving antiplatelet treatment after PCI (C allele vs. T allele: OR = 1.42, 95 % CI 1.08-1.87, P = 0.011; CT+CC vs. TT: OR = 1.93, 95 % CI 1.01-3.67, P = 0.046; CC vs. TT: OR = 2.18, 95 % CI 1.09-4.35, P = 0.027; respectively). Our meta-analysis of clinical cohort studies provides evidence that the PON1 Q192R polymorphism may increase the risk of MACE and stent thrombosis in patients receiving antiplatelet treatment after PCI.
Stratified matched-pair studies are often designed for adjusting stratification factors in modern medical researches. This article investigates a homogeneity test of differences between two correlated proportions in stratified matched-pair studies. We propose three test procedures, including an asymptotic test, bootstrap test, and multiple comparison procedures, and determine sample size requirements for such tests in a stratified matched-pair study. Simulation studies are conducted to evaluate the performance of the three test procedures and the accuracy of our derived sample size formulas. Empirical results show that (1) the likelihood ratio statistic is robust, while the score statistic and the modified score statistic are conservative in some cases of our considered settings; (2) the likelihood ratio statistic and the score statistic with the bootstrap method and the MaxT procedure behave satisfactorily in the sense that their type I error rates are close to the pre-given significance level; and (3) the derived sample size formulas are rather accurate. A real example from a clinical laboratory study is used to illustrate the proposed methodologies.
In stratified matched-pair studies, risk difference between two proportions is one of the most frequently used indices in comparing efficiency between two treatments or diagnostic tests. This article presents five simultaneous confidence intervals and two bootstrap simultaneous confidence intervals for risk differences in stratified matched-pair designs. The proposed confidence intervals are evaluated with respect to their coverage probabilities, expected widths, and ratios of the mesial noncoverage to noncoverage probability. Empirical results show that (1) hybrid simultaneous confidence intervals outperform nonhybrid simultaneous confidence intervals; (2) hybrid simultaneous confidence intervals based on median estimator outperform those based on maximum likelihood estimator; and (3) hybrid simultaneous confidence intervals incorporated with Wilson score and Agresti coull intervals and the bootstrap t-percentile simultaneous interval based on median unbiased estimators behave satisfactorily for small to large sample sizes in the sense that their empirical coverage probabilities are close to the prespecified nominal confidence level, and their ratios of the mesial noncoverage to noncoverage probabilities lie in [0.4,0.6] and are hence recommended. Real examples from clinical studies are used to illustrate the proposed methodologies.
We explore measuring Scleroderma patient disease improvement at the paired body part level and account for their correlation with the long term goal of possibly redefining disease progression using a shorter clinical examination. We propose using a binary outcome to measure disease progression at each paired body part level, construct tests for assessing equality of the correlations between groups for each paired body part and determine sample size requirements for such tests in a two-arm randomized clinical trial. Simulations are performed to evaluate properties of the tests and the accuracy of our sample size formulae. We demonstrate our method with data from a multi-center two-arm randomized clinical trial.
This paper investigates homogeneity test of rate ratios in stratified matched-pair studies on the basis of asymptotic and bootstrap-resampling methods. Based on the efficient score approach, we develop a simple and computationally tractable score test statistic. Several other homogeneity test statistics are also proposed on the basis of the weighted least-squares estimate and logarithmic transformation. Sample size formulae are derived to guarantee a pre-specified power for the proposed tests at the pre-given significance level. Empirical results confirm that (i) the modified score statistic based on the bootstrap-resampling method performs better in the sense that its empirical type I error rate is much closer to the pre-specified nominal level than those of other tests and its power is greater than those of other tests, and is hence recommended, whilst the statistics based on the weighted least-squares estimate and logarithmic transformation are slightly conservative under some of the considered settings; (ii) the derived sample size formulae are rather accurate in the sense that their empirical powers obtained from the estimated sample sizes are very close to the pre-specified nominal powers. A real example is used to illustrate the proposed methodologies.
In this article, we consider confidence interval construction for proportion ratio in paired samples. Previous studies usually reported that score-based confidence intervals consistently outperformed other asymptotic confidence intervals for correlated proportion difference and ratio. However, score-based confidence intervals may not possess closed-form solutions and iterative procedures are therefore required. This article investigates the problem of confidence interval construction for ratio of two correlated proportions based on a hybrid method. Briefly, the hybrid method simply combines two separate confidence intervals for two individual proportions to produce a hybrid confidence interval for the ratio of the two individual proportions in paired studies. Most importantly, confidence intervals based on this hybrid method possess explicit solutions. Our simulation studies indicate that hybrid Wilson score confidence intervals based on Fiellers theorem performs well. The proposed confidence intervals will be illustrated with three real examples.
We sought to study the effect of a combination therapy comprised of hyperbaric oxygen (HBO) and ulinastatin on the plasma levels of endotoxin, soluble CD14 (sCD14), endotoxin neutralizing capacity (ENC) and cytokines in acute necrotizing pancreatitis (ANP) in rats.
A series of MnO(x)/TiO(2) composite nanoxides were prepared by deposition-precipitation (DP) method, and the sample with the Mn/Ti ratio of 0.3 showed a superior activity for NO catalytic oxidation to NO(2). The maximum NO conversion over MnO(x)(0.3)/TiO(2)(DP) could reach 89% at 250°C with a GHSV of 25,000h(-1), which was much higher than that over the catalyst prepared by conventional wet-impregnation (WI) method (69% at 330°C). Characterization results including XRD, HRTEM, FTIR, XPS, H(2)-TPR, NO-TPD and Nitrogen adsorption-desorption implied that the higher activity of MnO(x)(0.3)/TiO(2)(DP) could be attributed to the enrichment of well-dispersed MnO(x) on the surface and the abundance of Mn(3+) species. Furthermore, DRIFT investigations and long-time running test indicated that NO(2) came from the decomposition of adsorbed nitrogen-containing species.
Bilateral dichotomous data are very common in modern medical comparative studies (e.g. comparison of two treatments in ophthalmologic, orthopaedic and otolaryngologic studies) in which information involving paired organs (e.g. eyes, ears and hips) is available from each subject. In this article, we study various confidence interval estimators for proportion difference based on Wald-type statistics, Fieller theorem, likelihood ratio statistic, score statistics and bootstrap resampling method under the dependence or/and independence models for bilateral binary data. Performance is evaluated with respect to the coverage probability and expected width via simulation studies. Our empirical results show that (1) ignoring the dependence feature of bilateral data could lead to severely incorrect coverage probabilities; and (2) Wald-type, score-type and bootstrap confidence intervals based on the dependence model perform satisfactorily for small to large sample sizes in the sense that their empirical coverage probabilities are close to the pre-specified nominal confidence level and are hence recommended. A real data from an otolaryngologic study is used to illustrate the proposed methods.
In this article, we consider approximate sample size formulas for testing difference between two proportions for bilateral studies with binary outcomes. Sample size formulas are derived to achieve a prespecified power of a statistical test at a prechosen significance level. Four statistical tests are considered. Simulation studies are conducted to investigate the accuracy of various formulas. In general, the sample size formula for Rosners statistic based on the dependence assumption is highly recommended in the sense that its actual power is satisfactorily close to the desired power level. An example from an otolaryngological study is used to demonstrate the proposed methodologies.
Pd-modified TiO(2) prepared by thermal impregnation method was used in this study for photocatalytic oxidation of NO in gas phase. The physico-chemical properties of Pd/TiO(2) catalysts were characterized by X-ray diffraction analysis (XRD), Brunauer-Emmett-Teller measurements (BET), X-ray photoelectron spectrum analysis (XPS), transmission electron microscopy (TEM), high resolution-transmission electron microscopy (HR-TEM), UV-vis diffuse reflectance spectra (UV-vis DRS) and photoluminescence spectra (PL). It was found that Pd dopant existed as PdO particles in as-prepared photocatalysts. The results of PL spectra indicated that the photogenerated electrons and holes were efficiently separated after Pd doping. During in situ XPS study, it was found that the content of hydroxyl groups on the surface of Pd/TiO(2) increased when the catalyst was irradiated by UV light, which could result in the improvement of photocatalytic activity. The activity test showed that the optimum Pd dopant content was 0.05 wt.%. And the maximum conversion of NO was about 72% higher than that of P25 when the initial concentration of NO was 200 ppm, which showed that Pd/TiO(2) photocatalysts could be potentially applied to oxidize higher concentration of NO.
Sample size determination is an essential component in public health survey designs on sensitive topics (e.g. drug abuse, homosexuality, induced abortions and pre or extramarital sex). Recently, non-randomised models have been shown to be an efficient and cost effective design when comparing with randomised response models. However, sample size formulae for such non-randomised designs are not yet available. In this article, we derive sample size formulae for the non-randomised triangular design based on the power analysis approach. We first consider the one-sample problem. Power functions and their corresponding sample size formulae for the one- and two-sided tests based on the large-sample normal approximation are derived. The performance of the sample size formulae is evaluated in terms of (i) the accuracy of the power values based on the estimated sample sizes and (ii) the sample size ratio of the non-randomised triangular design and the design of direct questioning (DDQ). We also numerically compare the sample sizes required for the randomised Warner design with those required for the DDQ and the non-randomised triangular design. Theoretical justification is provided. Furthermore, we extend the one-sample problem to the two-sample problem. An example based on an induced abortion study in Taiwan is presented to illustrate the proposed methods.
Many studies have reported the association between the FASLG -844T/C polymorphism and cancer risk, but the data are remaining controversial. A pooled analysis was performed to assess this relationship comprehensively. Medline, PubMed, Embase and Web of Science were searched, and data were extracted and cross-checked independently by three authors. A total of 18 published studies including 22389 subjects were involved in this analysis. Overall, the -844C allele was associated with a significantly increased cancer risk (for CC versus TT: OR=1.23, 95% confidence interval (CI)=1.04-1.45; for CC+TC versus TT: OR=1.15, 95% CI=1.01-1.30; for CC versus TT+TC: OR=1.20, 95% CI=1.05-1.38). In the subgroup analysis by ethnicity, significantly elevated risks were found among Asians (for CC versus TT: OR=1.61, 95% CI=1.37-1.89; for CC+TC versus TT: OR=1.36, 95% CI=1.16-1.60; for CC versus TT+TC: OR=1.44, 95% CI=1.22-1.70). In the subgroup analysis by study design, significantly increased risks were found among population-based case-control studies (for CC versus TT: OR=1.40, 95% CI=1.06-1.84; for CC+TC versus TT: OR=1.25, 95% CI=1.01-1.55; for CC versus TT+TC: OR=1.31, 95% CI=1.06-1.61). These findings indicate that the FASLG -844C allele is emerging as a low-penetrant cancer susceptibility allele for cancer development. However, more comprehensive understanding of the association would certainly have an immense prospect in the promising field of individualised preventive care.
K correlated 2 x 2 tables with structural zero are commonly encountered in infectious disease studies. A hypothesis test for risk difference is considered in K independent 2 x 2 tables with structural zero in this paper. Score statistic, likelihood ratio statistic and Wald-type statistic are proposed to test the hypothesis on the basis of stratified data and pooled data. Sample size formulae are derived for controlling a pre-specified power or a pre-determined confidence interval width. Our empirical results show that score statistic and likelihood ratio statistic behave better than Wald-type statistic in terms of type I error rate and coverage probability, sample sizes based on stratified test are smaller than those based on the pooled test in the same design. A real example is used to illustrate the proposed methodologies.
Polyunsaturated omega-3 fatty acids may beneficially influence healing processes and patient outcomes. The aim of this research was to study the clinical efficacy of fish oil enriched total parenteral nutrition in elderly patients after colorectal cancer surgery.
Investigating the prevalence of a disease is an important topic in medical studies. Such investigations are usually based on the classification results of a group of subjects according to whether they have the disease. To classify subjects, screening tests that are inexpensive and nonintrusive to the test subjects are frequently used to produce results in a timely manner. However, such screening tests may suffer from high levels of misclassification. Although it is often possible to design a gold-standard test or device that is not subject to misclassification, such devices are usually costly and time-consuming, and in some cases intrusive to the test subjects. As a compromise between these two approaches, it is possible to use data that are obtained by the method of double-sampling. In this article, we derive and investigate four test statistics for testing a hypothesis on disease prevalence with double-sampling data. The test statistics are implemented through both the asymptotic method suitable for large samples and approximate unconditional method suitable for small samples. Our simulation results show that the approximate unconditional method usually produces a more satisfactory empirical type I error rate and power than its asymptotic counterpart, especially for small to moderate sample sizes. The results also suggest that the score test and the Wald test based on an estimate of variance with parameters estimated under the null hypothesis outperform the others. An real example is used to illustrate the proposed methods.
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