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Background Increasingly, network meta-analysis (NMA) of published survival data are based on parametric survival curves as opposed to reported hazard ratios to avoid relying on the proportional hazards assumption. If a Bayesian framework is used for the NMA, rank probabilities associated with the alternative treatments can be obtained, which directly support decision-making. In the context of survival analysis multiple treatment effect measures are available to inform the rank probabilities. Methods A fractional polynomial NMA of overall survival in advanced melanoma was performed as an illustrative example. Rank probabilities were calculated and presented for the following effect measures: 1) median survival; 2) expected survival; 3) mean survival at the follow-up time point of the trial with the shortest follow-up; 4) hazard or hazard ratio over time; 5) cumulative hazard or survival proportions over time; and 6) mean survival at subsequent time points. The advantages and disadvantages of the alternative measures were discussed. Results Since hazard and survival estimates may vary over time for the compared interventions, calculations of rank probabilities for an NMA of survival curves may depend on the effect measure. With methods 1-3 rank probabilities do not vary over time, which are easier to understand and communicate than rank probabilities that vary over time as obtained with methods 4-6. However, rank probabilities based on methods 4-6 provide useful information regarding the relative treatment effects over time. Conclusions Different approaches to summarize results of a NMA of survival curves with rank probabilities have pros and cons. Rank probabilities of treatment effects over time provide a more transparent and informative approach to help guide decision-making than single rank probabilities based on collapsed measures, such as median survival or expected survival. Rank probabilities based on survival proportions are the most intuitive and straightforward to communicate, but alternatives based on the hazard function or mean survival over time may also be useful. [ABSTRACT FROM AUTHOR]

Background: This paper reflects the empirical findings of an ethnobotanical survey which was undertaken in Patriata (New Murree) of district Rawalpindi in Pakistan. The aims and objectives of the study were to document indigenous knowledge of plants particularly of medicinal, veterinary, fruit, vegetable, fodder, fuel etc. Methods: For this purpose, the whole area was surveyed for documenting folk knowledge using a semi-structured questionnaire. A total of 93 plants species belonging to 80 genera and 56 families were found in a variety of uses by the local people for the accomplishment of their basic needs. The study further employs binary logit regression model of medicinal uses of these plants so as to identify the probability of occurrence of medicinal use of woody or non-woody plants keeping other plant characteristics in view. Results: Ethnobotanical data shows that most plants are used for medicinal and fodder purposes (27.93% each), followed by fuel (16.90%), fruit (6.55%), vegetable (5.52%) and ethno-veterinary (3.79%). There is also an established association of medicinal use of plants to the fruits use. Non-woody plants have high tendency towards medicinal use of the plants as compared to woody plants. Annual plants are less likely to be directly associated with medicinal use of plants in the surveyed vegetation. Underground plant parts are more likely to be used for medicinal purposes as revealed from the Logit expressions. Conclusions: The study revealed that most of the plants are used for medicinal and fodder purposes. The results of Logit Model showed that the probabilities of plant species for their medicinal use are associated to the woody or non-woody, aerial or underground, perennial or annual characteristics of plants. One should be careful in completely generalizing the results as the survey findings are sensitive to the plant species and the vegetation under consideration. But it can be specified that there exists either some positive or negative association of medicinal use of plants to the various characteristics of plant species. [ABSTRACT FROM AUTHOR]

Background: Inferential statistical tests that approximate measurement are called acceptance procedures. The procedure includes type 1 error, falsely rejecting the null hypothesis, and type 2 error, failing to reject the null hypothesis when the alternative should be supported. This approach involves repeated sampling from a distribution with established parameters such that the probabilities of these errors can be ascertained. With low error probabilities the procedure has the potential to approximate measurement. How close this procedure approximates measurement was examined. Findings: A Monte Carlo procedure set the type 1 error at p = 0.05 and the type 2 error at either p = 0.20 or p = 0.10 for effect size values of d = 0.2, 0.5, and 0.8. The resultant values are approximately 15 and 6.25 % larger than the effect sizes entered into the analysis depending on a type 2 error rate of p < 0.20, or p < 0.10 respectively. Conclusions: Acceptance procedures approximate values wherein a decision could be made. In a health district a deviation at a particular level could signal a change in health. The approximations could be reasonable in some circumstances, but if more accurate measures are desired a deviation could be reduced by the percentage appropriate for the power. The tradeoff for such a procedure is an increase in type 1 error rate and a decrease in type 2 errors. [ABSTRACT FROM AUTHOR]