Another example is for a study with 6-Min walk distance (6MWD) as the primary endpoint. The treatment difference was estimated using least-squares mean difference. The corresponding p-value was calculated using the analysis of covariance (ANCOVA) approach.
In these examples, the p-value and the estimate of the treatment difference were from the same test statistic.
We see many examples where two different methods are used for estimating the treatment difference and for calculating the p-value - I call it 'splitting the p-value and the estimate of the treatment difference'. Here are two situations where this splitting situation occurs.
"In time-to-event analyses, end points were estimated with the use of the Kaplan–Meier method and were analyzed with the use of the log-rank test. Hazard ratios with 99% confidence intervals (for primary and secondary end points) and 95% confidence intervals (for exploratory end points) were estimated with the use of proportional-hazard models."
The primary efficacy endpoints in studies LX301 and LX303 were analyzed by the blocked 2- sample Wilcoxon rank sum statistic stratified by the baseline urinary 5-HIAA levels (≤ upper limit of normal reference range [ULN], >ULN, and Unknown). Descriptive statistics of the primary endpoints and the Hodges-Lehmann estimator of location shift with its respective CLs were reported for each comparison.