Proportionality Assumption Sample Clauses

Proportionality Assumption. The assumption of proportionality, for all time to event analyses, will be assessed firstly by examining plots of complementary log-log (event times) versus log (time) and, if this raises concerns, subsequently fitting a time dependent covariate (adding a treatment-by-time or treatment-by-ln(time) interaction term) to the model to assess the extent to which this represents random variation. If a lack of proportionality is evident then the HR can still be meaningfully interpreted as an average HR over time unless there is extensive crossing of thetime to event curves. The following sensitivity analyses will be performed on PFS data using the ITT. For PFS, a supportive analysis will be performed to assess time assessment bias via an Evaluation-time bias approach (▇▇▇▇▇▇ et al. 1983, ▇▇▇ et al. 2010). This will be performed using a validated SAS program for generation of the p-value. The most pragmatic and preferred approach to assessing evaluation-time bias is to analyse, using standard log-rank , the midpoint between the time of progression and the previous visit, this approach has been shown to be robust to even highly asymmetric assessment schedules. To support this analysis, the mean of patient-level average inter-assessment times will be tabulated for each treatment. A further analysis of the primary endpoint will be conducted, whereby patients who received subsequent anti-cancer therapies prior to progression (or death in the absence of progression) will be censored at the time patients received subsequent therapies, while patients who progress or die following two or more missed visits will not be censored. The analysis will use the same approach as the primary analysis model (outlined within Section 4.2.2). A list of relevant anti-cancer therapies will be provided by the study team prior to database lock (DBL). In support of this analysis, a ▇▇▇▇▇▇- ▇▇▇▇▇ plot where the roles of events and censored observations are reversed will be presented to assess the impact of rate and nature of censoring on final results. A summary of the number of patients who have incomplete PFS follow-up, i.e. were alive and progression free at the date of data cut-off and had not had a RECIST assessment within 12 weeks of the data cut-off will be provided by treatment group. Additionally, summary statistics will be given for the number of days from censoring to data cut-off for all censored patients. A summary of the median duration of follow-up will also be presented. Each...