| Popis: |
Pharmaceutical drug development is a costly and time consuming process. Reportedly, it takes about 10-15 years and ~900 million dollars of investment to launch a new drug in the world market. Any measure that increases the power and also decreases uncertainty about that power also increases drug net present value. For some time now, it has been argued that judicious utilization of available data might lead to more efficient use of resources during drug development. Conventionally, assessment of effectiveness has been based on comparing change from baseline at some pre-specified time for the control and test treatment (SPA). The last observation carry forward (LOCF) is a widely used technique if the data are missing due to any reason. Although, LOCF is known to introduce bias, the direction and magnitude is debatable.The primary aim of the proposed simulation experiments was to assess the properties of the random effects model (REM) and mixed model repeated measures (MMRM) methods that utilize all the data collected during pivotal trials. A total of 43 scenarios based on disease progression, magnitude of drug effect, between and within subject variability and patient drop-outs were analyzed. Three analysis methods, viz. SPA, REM and MMRM, were investigated. For the SPA method, the missing data were imputed with four different methods, such as LOCF, mean imputation, population and individual regression. The false-positive, false-negative inferences and bias in estimating the effect size for each method was assessed.The most important finding of this report is that the REM and MMRM methods are efficient alternatives to the SPA methods with ~50% savings on sample size. These methods are based on sound scientific principles and provide stronger evidence against the null hypothesis. The choice of the REM versus MMRM method is dependent on the purpose of the analysis and data gathered from the experimental design. The results support the use of likelihood-based MMRM methods for regulatory decision making. The REM methods are useful in understanding the time course of the disease and drug effect, making predictions based on the data and gaining insights into time to steady state effect for rational decision making. The SPA methods are less powerful across all the scenarios. The SPA-LOCF yielded anticonservative results in some cases with type-1 error rate exceeding 15% if data were missing due to toxicity. On the other hand, the drug effect was consistently underestimated (~40%), if data were missing due to lack of effectiveness. The results demonstrate that the SPA-LOCF methods make it practically impossible to establish effectiveness in these areas with a reasonable sample size. |
