Trusted Resource for Clinical Trials, Protocols & Progress
Time-to-event endpoints measure the duration from a well-defined starting point (e.g., randomization) to the occurrence of a specified event. These endpoints are especially relevant in cancer trials where the timing of progression, death, or recurrence holds clinical significance.
Click to read the full article.
A hazard ratio compares the hazard (i.e., the event rate) in the treatment group to the hazard in the control group at any point in time. It is defined mathematically from the Cox proportional hazards model and is interpreted as a relative risk over time.
Click to read the full article.
In clinical trials—especially oncology, cardiovascular, and infectious disease studies—outcomes are often time-to-event based. These require not just statistical reporting but visual clarity:
Click to read the full article.
In clinical trials evaluating long-term outcomes like death or disease progression, final results can take years. Interim survival analysis allows investigators to evaluate trends and make data-driven decisions while maintaining statistical rigor.
Click to read the full article.
Overview: R is an open-source statistical programming language widely used for clinical trial analysis. The survival package is its cornerstone for TTE analysis.
Click to read the full article.
Time-to-event data is unique due to censoring, variable follow-up times, and the need for visual survival curves. The right software can:
Click to read the full article.
The Cox proportional hazards model assumes that the ratio of hazard functions between treatment groups is constant over time. This implies that survival curves should not cross and that the treatment effect is consistent throughout follow-up.
Click to read the full article.
Incomplete data can:
Click to read the full article.
Regulators like EMA and CDSCO expect sponsors to:
Click to read the full article.
Missing data may lead to:
Click to read the full article.
