ijsmr
|
|
Abstract: Clinical trials comparing a test treatment with an active control therapy have become very popular in drug and medical device development in the last decade. An active controlled trial without a placebo, however, exhibits some major challenges in design, analysis, and interpretation, such as the determination of the non-inferiority margin or the assumption of constancy condition. When there are no ethical concerns, the comparison of a test drug with placebo usually provides the most convincing proof of the efficacy of a new treatment. Therefore, it may be advisable to conduct a three-arm trial — including placebo, active control, and the new treatment — if it is ethically justifiable such as a papulopustular acne study and an oral prophylactic antibiotics study. In this paper, we propose a statistical methodology for a three-arm non-inferiority trial with binary outcomes. We adapt the step-down hierarchical hypotheses and give a three-step testing procedure which is more realistic in conducting a clinical trial. We derived an optimal sample size allocation rule in an ethical and reliable manner to minimize the total sample size and hence to shorten the duration of the trials. Real examples from a papulopustular acne study and an oral prophylactic antibiotics study are used to demonstrate our methodology. Keywords: Clinical trial, binary outcomes, gold standard design, optimal sample size allocation, restricted maximum likelihood.Download Full Article |
|
|
Abstract: Both maternal and paternal disease history can be important predictors of the risk of common conditions such as heart disease or cancer because of shared environmental and genetic risk factors. Sometimes maternal and paternal history can have remarkably different effects on offspring’s status. The results are often affected by how the maternal and paternal disease histories are quantified. We proposed using the log-rank score (LRS) to investigate the separate effect of maternal and paternal history on diseases, which takes parental disease status and theage of theirdisease onset into account. Through simulation studies, we compared the performance of the maternal and paternal LRS with simple binary indicators under two different mechanisms of unbalanced parental effects. We applied the LRS to a national cohort study to further segregate family risks for heart diseases. We demonstrated using the LRS rather than binary indicators can improve the prediction of disease risks and better discriminate the paternal and maternal histories. In the real study, we found that the risk forstroke is closely related with maternal history but not with paternal history and that maternal and paternal disease history have similar impacton the onset of myocardial infarction. Keywords: Family history, stroke, risk score, maternal effect, imprinting.Download Full Article |
|
|
Abstract: When designing studies to assess occupational exposures, one persistent decision problem is the selection between two technical methods, where one is expensive and statistically efficient and the other is cheap and statistically inefficient. While a few studies have attempted to determine the relatively more cost-efficient design between two technical methods, no successful study has optimized the fraction of the expensive efficient method in a combined technique intended for long-run exposure assessment studies. The purpose of this study was therefore to optimize the fraction of the expensive efficient measurements by resolving a precision-requiring cost minimization problem.For an indefinite total number of measurements, the total cost of a working posture assessment study was minimized by performing only expensive direct technical measurements. However, for a definite total number of measurements, the use of combined techniques in assessing the posture could be optimal, depending on the constraints placed on the precision and on the research budget. Keywords: Statistical efficiency, combined measurement technique, productive efficiency, cost savings, marginal cost-benefit ratio, cost function.Download Full Article |
|
|
Abstract: The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers’ association with ventilation-free survival. Keywords: Bayesian, Biomarker, Detection limit, Lung Injury, Proportional hazards models.Download Full Article |
|
|
Abstract: Background: Significantly high concentration of secondary bile acids and low concentration of primary acids are reported by the surgeons in patients with gallbladder carcinoma (GBC) compared to control and cholelithiasis groups. Aim: To examine the effect of primary and secondary bile acids on the development of cholelithiasis and GBC and to investigate the association, if any, among the two groups of bile acids. Methods: The study uses two groups of patients at a time selected in accordance with some pre-fixed inclusion and exclusion criteria. Informed consent was obtained from all patients. The demographic characteristics such as mean age, sex ratio and body mass index, etc. are obtained for the selected groups of patients. The study defines dichotomous responses and the four bile acid constituents, namely cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA) and lithocholic acid (LCA), as the predictors. It then assumes logistic regression model to associate the binary responses with the predictors by using probability scores. Bayes analysis is developed using Markov chain Monte Carlo (MCMC) pack in R software for the posterior simulation. Results:Twenty one cholelithiasis patients and twenty patients in each of control and GBC groups are studied. It is seen that a unit decrement in the level of CA (CDCA) increases the log (odds ratio) for cholelithiasis by an amount of 0.49 (0.14) and odds ratio by almost 1.5 (1.12). Similarly, a unit increment in the level of DCA (LCA) provides the log (odds ratio) for cholelithiasis as 0.18 (1.3) and odds ratio as 1.16 (2.95). Comparing GBC with control population, it is noted that a unit decrease in the level of CA (CDCA) in the control population increases the log (odds ratio) for GBC by an amount of 1.16(0.26) and odds ratio by almost 2.63 (1.24) times. Similarly, the log(odds ratio) for GBC increases by 0.77(1.94) and the odds ratio increases by 1.9 (5.0) for the unit increment in the level of DCA(LCA). Conclusions: The study observes relatively high variations in the primary and secondary bile acids in the cholelithiasis and GBC groups as compared with the control group. It, in turn, reflects strong association among the two categories of bile acids in gallbladder diseases. Keywords: Bile acid constituents, Cholelithiasis, Gallbladder carcinoma, Logistic regression model, Vague prior, Odds ratio, Posterior simulation.Download Full Article |