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Abstract: Following the well-established approach on how to deal with competing risks in the situation of time-to-event endpoints, cumulative incidences have to be used to analyse each single category of an outcome in the situation of competing risks without a time-to-event structure as well. This can be easily done by applying a simple chi-square test. Nevertheless, these categorial outcomes are usually combined to get a composed dichotomous outcome to face the problem on how to deal with a significant chi-square omnibus test in the situation of more than 1 df, i.e. > 2x2 tables. The aim of this report is to question the practice of combined, i.e. composed dichotomized, endpoints because important information is lost and the real effect of interest in confirmatory phase III studies may only become apparent in explorative secondary analyses. It is shown – by using hypothetical data and by recalculation of published phase III studies’ results – how the use of a chi-square omnibus test and the scarcely known post-hoc testing answers the real question of interest within one primary confirmatory analysis. This method reveals insight into the actual effect of a new treatment or therapy on the event of interest in the presence of a mutually exclusive competing risk. Keywords: Competing risk, randomized controlled trial, composite outcome, chi-square test, post-hoc testing.Download Full Article |
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Abstract: Statistical approaches for drug effectiveness studies after liver transplant have used a survival model with changes in treatment as a time-dependent covariate. However, the approach requires that changes in the time-dependent covariate be unrelated to survival outcome. Usually this is not the case, as one drug may be discontinued and an alternative chosen due to the declining health status of the patient. Other approaches examine only subjects who remain on the same drug over a time window, which discards valuable data and may lead to biased effects since this excludes data related to early deaths and to individuals who perform poorly on the drug and had to switch treatments. Because of these issues there are conflicting results seen in the evaluation of immunosuppressive drug effectiveness after liver transplant. We propose a joint survival outcome model with a time-to-drug-change event and a terminal event in graft failure that is useful in drug effectiveness studies where subjects are discontinued from an immunosuppressant (in favour of alternative treatment) due to health reasons. We also include a longitudinal biomarker component. The model takes account of the dependencies across out- comes through shared random effects. Using a Markov chain Monte Carlo approach, we fit the joint model to data from liver transplant recipients from the Scientific Registry for Transplant Recipients. Keywords: Joint models, longitudinal, survival, transplant, joint outcome.Download Full Article |
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Abstract: The calculation of net dietary energy is in great triumph on the helm of designing an apt dieting for both the therapeutic and normal diet. There are some procedures in this connection in nutritional science which is relatively time consuming, laboratory tests induced and often the misleading data contributors in view of assuring balanced dieting. The dietician is often at bay to approve an exact dieting to sustain health and nutritional soundness adhering to the existing dietary energy measuring methods because the frequently using methods are not informing the net dietary energy level required at all in correct amount for the sample at a population in a community. The aim of the current study is to make a dot over these ongoing panics exploring an easy and accurate way in prescribing a confounding free diet. The study can divulge an open secret in measuring net dietary energy which is mandatory for dieting practices worldwide to resist the possible health horrors in nutritional epidemiology. The study finding is the Modified Mass Energy Equivalence [equation (xi)] can be an outstanding biophysical model in measuring net dietary energy as a dieting tool in health pedagogy of health science. Keywords: Mass Energy Equivalence, Health Pedagogy, Biophysical Modeling, Nutritional Epidemiology, Health Physics.Download Full Article |
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Abstract: The issue of borderline p-value seems to divide health scientists into two schools of thought. One school of thought argues that when the p-value is greater than or equal to the statistical significance cut-off level of 0.05, it should not be considered statistically significant and the null hypothesis should be accepted no matter how close the p-value is to the 0.05. The other school of thought believes that by doing so one might be committing a Type 2 error and possibly missing valuable information. In this paper, we discuss an approach to address this issue and suggest the test of random duplication of participants as a way to interpret study outcomes when the statistical significance is borderline. This discussion shows the irrefutability of the concept of borderline statistical significance, however, it is important that one demonstrates whether a borderline statistical significance is truly borderline or not. Since the absence of statistical significance is not necessarily evidence of absence of effect, one needs to double check if a borderline statistical significance is indeed borderline or not. The p-value should not be looked at as a rule of thumb for accepting or rejecting the null hypothesis but rather as a guide for further action or analysis that leads to correct conclusions. Keywords: P-value, Sample Size, Statistical Significance, Borderline Significance, Participant Random Duplication.Download Full Article |
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Abstract: The paper deals with the constrained Bayesian Method (CBM) for testing composite hypotheses. It is shown that, similarly to the cases when CBM is optimal for testing simple and multiple hypotheses in parallel and sequential experiments, it keeps the optimal properties at testing composite hypotheses. In particular, it easily, without special efforts, overcomes the Lindley’s paradox arising when testing a simple hypothesis versus a composite one. The CBM is compared with Bayesian test in the classical case and when the a priori probabilities are chosen in a special manner for overcoming the Lindley’s paradox. Superiority of CBM against these tests is demonstrated by simulation. The justice of the theoretical judgment is supported by many computation results of different characteristics of the considered methods. Keywords: CBM, Bayesian test, Composite hypotheses, Lindley’s paradox, Hypotheses testing.Download Full Article |