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G. Berry

Bio: G. Berry is an academic researcher from University of Sydney. The author has contributed to research in topics: Categorical variable & Enteroendocrine cell. The author has an hindex of 7, co-authored 13 publications receiving 696 citations.

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TL;DR: Statistical methods in medical research, Statistical methods inmedical research, and statistical methods in scientific research are used in medicine, education and research.
Abstract: Statistical methods in medical research , Statistical methods in medical research , کتابخانه دیجیتال جندی شاپور اهواز

491 citations


Cited by
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Journal ArticleDOI
TL;DR: Refugees resettled in western countries could be about ten times more likely to have post-traumatic stress disorder than age-matched general populations in those countries.

1,892 citations

Journal ArticleDOI
10 Nov 2001-BMJ
TL;DR: In many randomised trials researchers measure a continuous variable at baseline and again as an outcome assessed at follow up to see whether a treatment can reduce pre-existing levels of pain, anxiety, hypertension, and the like.
Abstract: In many randomised trials researchers measure a continuous variable at baseline and again as an outcome assessed at follow up. Baseline measurements are common in trials of chronic conditions where researchers want to see whether a treatment can reduce pre-existing levels of pain, anxiety, hypertension, and the like. Statistical comparisons in such trials can be made in several ways. Comparison of follow up (post-treatment) scores will give a result such as “at the end of the trial, mean pain scores were 15 mm (95% confidence interval 10 to 20 mm) lower in the treatment group.” Alternatively a change score can be calculated by subtracting the follow up score from the baseline score, leading to a statement such as “pain reductions were 20 mm (16 to 24 mm) greater on treatment than control.” If the average baseline scores are the same in each group the estimated treatment effect will be the same using these two simple approaches. If the treatment is effective the statistical significance of the treatment effect by the two methods will depend on the correlation between baseline and follow up scores. If the correlation is low using the change score will …

1,721 citations

Journal ArticleDOI
TL;DR: A new index for analysis of single-case research data was proposed, Tau-U, which combines nonoverlap between phases with trend from within the intervention phase and provides the option of controlling undesirable Phase A trend.

1,088 citations

Journal ArticleDOI
TL;DR: The raising of awareness and implementation of effective interventions for modifiable risk factors, such as overweight, obesity, maternal age, and smoking, are priorities for stillbirth prevention in high-income countries.

1,053 citations

Journal ArticleDOI
TL;DR: The Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment and can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.
Abstract: Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment. In clinical trials or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. The time starting from a defined point to the occurrence of a given event, for example death is called as survival time and the analysis of group data as survival analysis. This can be affected by subjects under study that are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experienced or died if observation continued, or we lose touch with them midway in the study. We label these situations as censored observations. The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. The survival curve can be created assuming various situations. It involves computing of probabilities of occurrence of event at a certain point of time and multiplying these successive probabilities by any earlier computed probabilities to get the final estimate. This can be calculated for two groups of subjects and also their statistical difference in the survivals. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.

824 citations