Interim Analysis and Data Monitoring

Clinical trials are often longitudinal in nature. It is often impossible to enroll all subjects at the same time, so it can take a long time to complete a longitudinal study. Over the course of the trial one needs to consider administrative monitoring, safety monitoring and efficacy monitoring.

Efficacy monitoring can be performed by taking interim looks at the primary endpoint data (prior to all subjects being enrolled or all subjects completing treatment). This is because:

It potentially stops the trial early if there is convincing evidence of benefit or harm of the new product

It potentially stops the trial for futility, in other words the chance of significant beneficial effect by the end of the study is small given observed data

Re-estimate final sample size required to yield adequate power to obtain a significant result

Interim analysis evaluates for early efficacy, early futility, safety concerns, or adaptive design with respect to sample size or power.

Group Sequential Design

A common type of study design for interim analysis is GSD, in which data are analyzed at regular intervals.

Determine a priori the number of interim "looks"

Let K = # of total planned analyses including final (K >= 2)

For simplicity, assume 2 groups and that subjects are randomized in a 1:1 manner

After every n = N/K subjects are enrolled and followed for a specific time period, perform an interim analysis on all subjects followed cumulatively

If there is a significant treatment difference at any point, consider stopping the study

Due to multiple testing the probability of observing at least one significant interim result is much greater than the overall α = .05, as a result the interim analyses should NOT be performed using the family-wise error rate. The data at each interim analysis contains data from the previous interim and thus are not independent.

Equivalently we would have K critical values for each interim:

First interim analysis compare test statistic to critical value Z1 |> c1. If significant then stop the trial.

Second interim analysis compare test statistic to critical value Z2 |> c2. If significant stop the trial

Final analysis compare test statistic to critical value Xk |> ck.

Pocock Approach

Derives constant critical values across all stages to maintain the overall significance level at .05. The critical value depends on the number of interim analyses, but is the same for each interim look.

Ex. When K=5, Z critical value = 2.413 for each interim and the final analysis. When K=4, Z critical value = 2.361.