Industry Issues: Dataset Preparation for Time to Event Analysis Davis Gates Schering Plough Research Institute

Not all Clinical Trials are Designed for Time to Event Analysis However, time to events are common secondary analyses in many clinical trials, later requiring PRIMARY attention. These comments address those types of studies.

Obstacles to optimal Time-to-event Analysis Secondary Parameter: study not powered for this analysis Secondary Parameter: study not powered for this analysis Low Incidence of Events: resulting in poor or impossible estimate of time-to-event by treatment Low Incidence of Events: resulting in poor or impossible estimate of time-to-event by treatment Determination of Event: During vs. after the treatment period Determination of Event: During vs. after the treatment period Censoring Issues: Rate very high or poorly defined Censoring Issues: Rate very high or poorly defined Bias due to Per Patient Duration of Treatment: another term meaning early drop outs Bias due to Per Patient Duration of Treatment: another term meaning early drop outs

Secondary Parameter Study may be powered for a defined delta of a continuous outcome variable. Study may be powered for a defined delta of a continuous outcome variable. Time-to-event among a long list of secondary analyses. Time-to-event among a long list of secondary analyses. Cannot rely on statistical inference for final conclusions (PROC LIFETEST), therefore, a clinically meaningful difference should be assessed in lieu of a p-value. Cannot rely on statistical inference for final conclusions (PROC LIFETEST), therefore, a clinically meaningful difference should be assessed in lieu of a p-value.

Low Incidence of Events Time to event analysis may not be feasible for a population of patients who experience a low proportion of events. Time to event analysis may not be feasible for a population of patients who experience a low proportion of events. How many times have we seen non estimable MEDIAN time to events? How many times have we seen non estimable MEDIAN time to events? May have to break down to a categorical analysis (EVENT=YES or NO), losing the time to event element. (We should have enrolled more sickly subjects) May have to break down to a categorical analysis (EVENT=YES or NO), losing the time to event element. (We should have enrolled more sickly subjects)

Determination of Event Even though the event itself may be clearly defined, the probability of event vs. relation to treatment may not be so clear. (are all events treated equally?) Even though the event itself may be clearly defined, the probability of event vs. relation to treatment may not be so clear. (are all events treated equally?) The evaluation of event can differ in two contrasting treatment methods such as daily topical vs. monthly intravenous. The evaluation of event can differ in two contrasting treatment methods such as daily topical vs. monthly intravenous. Generally analyze ALL data, regardless of temporal relationship with treatment, but why not define the drug relation interval, and use this to enforce follow-up? Generally analyze ALL data, regardless of temporal relationship with treatment, but why not define the drug relation interval, and use this to enforce follow-up?

Simple Case – Daily Dosing Regimen Daily/periodic Inhaled/Topical Drug with minimal /no Blood Level Impact: Treatment Period: Last Day of Dose + Delta Last Day of Dose + Delta Delta = period of time no shorter than treatment duration claim, but can include a wash-out period treatment duration claim, but can include a wash-out period

Treatment Period for Drugs with Long Half-Life/Infrequent Dosing Regimen Long-acting Drug such as a monthly IV or single dose with long Blood level half life… Treatment Period can last through: Last Dose Date +N*t 1/2 N = number of half lives (t 1/2 ), each of which can be of several days or weeks in length (N=5 generally determines drug wash-out) This criteria can require a considerable amount of follow-up.

Censoring Issues General Time-to-event methodology, even though censors are handled, were not optimally designed for data with heavy censoring. Large scale clinical trials can have a high proportion of censored subjects (20% or more). This places an emphasis in improvements in follow-up for all subjects, even those discontinued from the trial. This is related to the issue of low proportion of events, which leave the potential for high proportion of censors.

Patient Duration of Treatment Is time-to-event (or survival) analysis the ultimate missing data analysis? Certainly not, subjects drop out for many reasons, related or not related to treatment or covariates. Is time-to-event (or survival) analysis the ultimate missing data analysis? Certainly not, subjects drop out for many reasons, related or not related to treatment or covariates. Calculation of event rates can be biased by early drop-outs. Calculation of event rates can be biased by early drop-outs.

Duration of Treatment (Cont…) Early Drop Outs: it is in our interest to follow up subjects, give them a chance to report an event – even if discontinued from a trial, to reduce this bias. Early Drop Outs: it is in our interest to follow up subjects, give them a chance to report an event – even if discontinued from a trial, to reduce this bias. In many cases the probability of a subject reporting an event is reduced with an early drop out, producing by treatment event rates biased downward. In many cases the probability of a subject reporting an event is reduced with an early drop out, producing by treatment event rates biased downward.

The Perfect Dataset First Day of Treatment Documented First Day of Treatment Documented Each Event Date Recorded with pre or post dose information for that day Each Event Date Recorded with pre or post dose information for that day Last Day of Treatment Documented Last Day of Treatment Documented Last Visit Day Documented Last Visit Day Documented All Subjects receive follow-up regardless of treatment or discontinuation status (Insure open communication opportunity – at least remotely). All Subjects receive follow-up regardless of treatment or discontinuation status (Insure open communication opportunity – at least remotely). Last contact date Documented Last contact date Documented Last Day of follow-up clearly documented (last contact date is optimal, but may have to settle for last visit date) Last Day of follow-up clearly documented (last contact date is optimal, but may have to settle for last visit date) Censoring and Events to be determined by availability of above data and a pre-specified algorithm. Censoring and Events to be determined by availability of above data and a pre-specified algorithm.