We consider the problem of estimating the best subgroup and testing for treatment effect in a clinical trial. We define the best subgroup as the subgroup that maximizes a utility function that reflects the trade-off between the subgroup size and the treatment effect. For moderate effect sizes and sample sizes, simpler methods for subgroup estimation worked better than more complex treebased regression approaches. We propose a three-stage design with a weighted inverse normal combination test to test the hypothesis of no treatment effect across the three stages.
We consider the problem of estimating a biomarker-based subgroup and testing for treatment effect in the overall population and in the subgroup after the trial. We define the best subgroup as the subgroup that maximizes the power for comparing the experimental treatment with the control. In the case of continuous outcome and a single biomarker, both a non-parametric method of estimating the subgroup and a method based on fitting a linear model with treatment by biomarker interaction to the data perform well. Several procedures for testing for treatment effect in all and in the subgroup are discussed. Cross-validation with two cohorts is used to estimate the biomarker cutoff to determine the best subgroup and to test for treatment effect. An approach that combines the tests in all patients and in the subgroup using Hochberg's method is recommended. This test performs well in the case when there is a subgroup with sizable treatment effect and in the case when the treatment is beneficial to everyone.
bOHP increased dental visits among children ≤3 years but the finding might be attributable to clinic changes coinciding with bOHP implementation that were not controlled with the study design. Additional studies are needed in populations experiencing challenges accessing dental care.
Background: Mothers and new-borns are vulnerable to illness and deaths during the postnatal period. Care during postnatal period is the important part of maternal health care as the serious and life-threatening complications can occur in postnatal period.Methods: A Quantitative approach with pre- experimental design was used to study the effectiveness of STP on postnatal care. Sixty postnatal mothers were selected from tertiary care hospital. The Purposive sampling techniques was used to select the study subjects. Data was collected by using Structured Knowledge questionnaire.Results: The results show that the overall mean pre-test knowledge score of postnatal mothers was 19.8±2.98 and mean post-test knowledge score of postnatal mothers was 26.28±1.89 and the mean difference was 6.48. This revealed that the STP was an effective method in improving mother’s knowledge on postnatal care. There was no significant association found between pre-test knowledge score with their demographic variables except occupation.Conclusions: The findings of the study revealed that STP was effective in enhancing the knowledge of postnatal mothers on postnatal care.
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