# Asthma Trial – a double blinded, randomized, placebo-controlled study Team Moser: Jing Dong Yan Yan Wu Haipeng Yao.

## Presentation on theme: "Asthma Trial – a double blinded, randomized, placebo-controlled study Team Moser: Jing Dong Yan Yan Wu Haipeng Yao."— Presentation transcript:

Asthma Trial – a double blinded, randomized, placebo-controlled study Team Moser: Jing Dong Yan Yan Wu Haipeng Yao

Purpose The new puffer is effective or not Target population: Physician-diagnosed severe asthma patients

Objectives and Endpoints Primary objective: the new puffer is effective or not Primary endpoint: the measurement of FEV1 Secondary objective : - if the puffer is effective for patients of different age group - if the puffer will reduce symptoms of different severity levels Secondary endpoint: - record the numbers of Hospitalization, emergency visits, the use of rescue medication etc * Rescue medication is allowed in this trial. And the # of the use of rescue medication will be one of the measures of the secondary endpoint

Evaluation design: A double blinded, randomized, placebo-controlled study 1. Control - Intervention Group: patients take the new puffer twice a day for 12 weeks and record FEV1 weekly. - Control Group : patients take placebo puffer twice a day for 12 weeks and record FEV1 weekly * 12 weeks later, all patients visit physician and physician diagnose if the puffer is effective based on the FEV1 value The type of design is superiority because our goal is that the treatment if more effective than placebo

2.Randomization Sample size α=0.05 β=1- power(0.8) =0.2 (Zα=1.645, Zβ=1.28) Assume : effective rate of the intervention group (Pi ) = 0.4 effective rate of the placebo group (Pc) = 0.2 2N = 2 = 126 Randomize Assign trial subject to intervention or control group using computer-generated randomization with 50-50 chance for each group for a total of 126 patients 3.Double - Blind Both patients and physician are unaware of treatment assignment

Drop - outs complete analysis P = # effective case / N weighting method P = # effective case / N – drop outs imputation method - LOCF (Last Observation Carry Forward) incomplete case analysis - Analyze the odds ratio of drop-outs in different groups - Use regression analysis to analyze the reasons of drop outs

Repeated Measures - Pre-post treatment on the same observation Intervention group Placebo Group Pre-treatment measure treatment measure Pre-treatment measure treatment measure Analyze the correlation and the superiority for the pre-post treatment on the same observation for each group

Statistical Analysis 1. Pi – Pc ≥0.2 (difference in proportion) 2. Ө > 2 ( Ө: odds ratio)

Similar presentations