Composite Strategy for Intercurrent Event and the Use of Trimmed Means in Clinical Trial Data Analyses

When composite strategy is used to handle the intercurrent event (ICE), specially the terminal event such as death, the occurrence of the ICE is integrated into the endpoint definition, often by assigning a specific value to participants who experience the event.

In ICH E9-R1 "Addendum on Estimands and Sensitivity Analysis in Clinical Trials" training material, about the composite strategy to handle the intercurrent event, trimmed mean is mentioned to be an approach in handling the intercurrent event.

A trimmed mean may also be called truncated mean and is the arithmetic mean of data values after a certain number or proportion of the highest and/or lowest data values have been discarded. The data values to be discarded can be one-sided or two-sided. A trimmed mean can be defined as a robust average computed by discarding a specified fraction of the lowest and highest observations and averaging the remainder. In effect, it “trims” the tails of the data, reducing the influence of outliers. For example, a 50% trimmed mean discards the bottom 25% and top 25% of values, averaging the middle 50% In general, an alpha‑trimmed mean removes the lowest and highest alpha/2 fraction of data (where alpha is expressed as a percentage of the total).

After trimming, the mean of the remaining values is computed by the usual arithmetic formula. In practical use, common trims range from 5–20% per tail (e.g. alpha=10%-40% total) in robust estimation, though some clinical examples have trimmed up to 50%. By removing extreme observations, trimmed means downweight outliers and model the assumption that missing or dropout outcomes are worse than any observed values.

Advantages of Trimmed Means in Handling Outliers and Skewed Data

Trimmed means are recognized as robust estimators of central tendency, demonstrating less sensitivity to deviations from assumed models or distributions, such as the presence of outliers or non-normality, when compared to classical methods like the sample mean. This robustness translates into more stable and reliable results under challenging data conditions.

For asymmetric distributions, where variability is more pronounced on one side, trimmed means can provide a superior estimation of the location of the main body of observations. By removing extreme values, they offer a more robust estimate of the central value and are less influenced by skewed data distributions. Furthermore, the standard error of the trimmed mean is less susceptible to the effects of outliers and asymmetry than that of the traditional mean, which can lead to increased statistical power for tests employing trimmed means.

The advantages of trimmed means extend beyond mere statistical robustness; they enable a more clinically meaningful interpretation of treatment effects, particularly in heterogeneous patient populations or when extreme outcomes (e.g., severe adverse events, rapid disease progression) might otherwise obscure the true effect in the majority of patients. This aligns with the need for statistical methods that accurately reflect real-world clinical practice and patient experience. If extreme values arise from factors not directly related to the treatment's intended effect on the typical patient—such as rare severe adverse events or non-adherence driven by external circumstances—then their removal allows for a clearer assessment of the treatment's impact on the majority. Conversely, if extreme values are an inherent part of the treatment effect, such as severe lack of efficacy leading to patient dropout, the

trimmed mean can define an estimand for the subpopulation that did not experience these extreme negative outcomes. Both scenarios offer a more focused and potentially more interpretable clinical picture.

It is important to acknowledge the inherent trade-off between robustness and efficiency: more robust methods, including trimmed means, may sacrifice some efficiency (precision or variability) compared to optimal methods under ideal statistical assumptions. The selection of a method ultimately depends on the nature of the data and the specific goals of the analysis.

Comparison with Traditional Measures of Central Tendency (Mean, Median)

The choice among the mean, median, and trimmed mean is not merely a statistical decision but reflects a fundamental determination about the target estimand and the specific clinical question being addressed.

● Mean: The traditional arithmetic mean calculates the average of all values in a dataset. It is highly sensitive to extreme values, which can significantly distort the measure of central tendency and lead to a less representative average, especially in the presence of outliers or skewed distributions.

● Median: The median represents the middle value in an ordered dataset and is highly resistant to the influence of extreme values.3 Conceptually, the median can be viewed as an extreme form of a trimmed mean, where all but one or two central observations are effectively removed. While both the trimmed mean and the median reduce the impact of outliers, the median is generally considered more robust in certain contexts due to its reliance solely on rank order.

The trimmed mean strikes a balance between these two traditional measures. It retains more information from the dataset than the median, which discards a significant portion of the data, while simultaneously offering greater robustness to outliers than the arithmetic mean.3 This allows for a nuanced definition of "average effect" that acknowledges the presence of extreme outcomes without being unduly influenced by them (like the raw mean) or implicitly discarding them entirely (like the median). This highlights the importance of defining the estimand before selecting the statistical method, a principle strongly emphasized by ICH E9(R1).

Regulatory Context and Trial Examples

Trimmed means have been discussed in statistical literature and regulatory settings as a way to handle dropout or intercurrent events. Permutt and Li (FDA biostatisticians) first proposed using trimmed means for “symptom trials” with dropout, treating each dropout as a complete (nonnumeric) observation ranked as the worst outcome. Under this approach, all subjects who discontinue before the endpoint are implicitly assigned the worst possible values and then an equal percentage are trimmed from each arm. In effect, trimming favors treatments with fewer dropouts, since “having more completers is a beneficial effect of the drug”.

Notably, trimmed means correspond to a composite estimand in the ICH E9(R1) framework: one can assign intercurrent events (e.g. dropouts) a worst-case value and then summarize the outcome distribution by a median or trimmed mean. For instance, the ICH E9 addendum training materials explicitly cite trimmed means (alongside medians) as summary measures under a composite strategy when dropouts are scored as extreme unfavourable outcomes. A recent FDA review of glaucoma drug Rocklatan (netarsudil/latanoprost) illustrates this: the statistical reviewer computed an “adaptive trimmed mean” IOP reduction by coding patients who withdrew for lack of efficacy or adverse events as worst outcomes. The reviewer noted this analysis aligns with the composite strategy in ICH E9(R1) and can reinforce the main intent-to-treat result. 

However, we found no FDA-approved trial in which a trimmed mean was the pre‑specified primary endpoint analysis. In all examples, trimmed means have been used as sensitivity or supportive analyses rather than the main test. For example, in a uterine fibroid drug application (NDA 214846), a trimmed‐mean analysis of menstrual blood loss was reported: the trimmed mean in each arm used the 50% best-performing patients, reflecting a 50% trim. Similarly, an ophthalmology (geographic atrophy) trial review (NDA 217171) applied trimmed‐mean + multiple imputation as a sensitivity: certain dropouts were “excluded (trimmed)” in the reviewer’s analysis. In each case, the trimmed mean analysis “assumes missing data as ‘bad outcomes’” (i.e. MNAR). These examples underscore that trimmed means appear in regulatory documents as robust or conservative analyses (often labeled “completer” or MNAR analyses) rather than as the primary efficacy metric.

Key examples:

• Glaucoma (Rocklatan NDA 208259): Reviewer performed an adaptive trimmed mean IOP analysis, excluding patients who withdrew for lack of effect and assigning worst values (citing Permutt & Li as method).

• Uterine fibroids (NDA 214846): Primary efficacy (menstrual blood loss) was also examined by trimmed means (50% trim) as sensitivity. The FDA report notes the trimmed‐mean is based on the “top 50% best performers in each arm”.

• Geographic atrophy (NDA 217171): A trimmed-mean + MI analysis was done by the reviewer; dropouts due to adverse events or lack of efficacy were “excluded (trimmed)” from one scenario.

These applications align with recent methodological studies showing trimmed means estimate a unique estimand: essentially, the mean outcome in the subpopulation of patients who would have remained on treatment. This estimand privileges treatments that prevent dropout, but it relies on the strong assumption that every dropout truly has an “unfavourable” outcome. As Wang et al. note, “the trimmed mean estimates a unique estimand” and its validity “hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients”. Ocampo et al. similarly emphasize that trimmed means work well when discontinuation is strongly associated with poor outcome, but give biased estimates if data are actually missing at random.

Calculation and Assumptions

Formula: To calculate an alpha‑trimmed mean, one typically sorts the data and discards a proportion alpha/2 from each end. For example, the 50% trimmed mean drops the lowest 25% and highest 25% of values, averaging the middle 50%. In general, if alpha (0–1) is the total fraction trimmed, remove the lowest alpha/2 and highest alpha/2 observations, then compute the mean of the remaining values. (The interquartile mean is the special case alpha=0.5.) Typical choices of alpha are guided by robustness needs: small trims (e.g. alpha=0.1 for 5% each tail) mildly reduce outlier influence, while large trims (up to 0.5) exclude half the data.

Statistical assumptions: Trimmed‐mean analyses assume that any trimmed/missing values are in fact the worst outcomes. In the dropout context, this treats early withdrawal as if the patient’s true result were extremely poor. As Ocampo et al. explain, the trimmed mean approach “sets missing values as the worst observed outcome and then trims away a fraction of the distribution”. Under this MNAR assumption, the trimmed mean can provide an unbiased estimate of the treatment effect (on the subpopulation that remains). However, if outcomes are actually missing at random (MAR) without relation to extreme values, trimmed means will be biased. In simulations, trimmed means were found to fail under MAR (because the assumption of trimming “bad” outcomes is then invalid).

Interpretation: Because the trimmed mean ignores equal fractions from each arm, it essentially compares the upper quantiles of the outcome distribution. Clinically, it reflects the mean of the best-performing subset of patients. This is why Permutt and Li emphasize that their method makes no assumptions beyond randomization: it is a nonparametric “exact” test for efficacy that includes all randomized subjects (by ranking dropouts worst). Regulators caution that the trimmed-mean estimand differs from a standard ITT mean; it answers the question, “What is the mean outcome among patients who would have remained in the trial?”.

SAS Implementation Example

Trimmed means can be computed in SAS using procedures like PROC UNIVARIATE or PROC MEANS. For instance, PROC UNIVARIATE supports a trimmed= option. The snippet below computes a 10% trimmed mean (i.e. removes 10% from each tail) of a variable Y and captures the result via ODS:

/* Example: Compute a 20% trimmed mean (10% each tail) for outcome Y */
ods output TrimmedMeans=TrimmedStats;
proc univariate data=trial_data trimmed=0.10;
   var Y;
run;

proc print data=TrimmedStats noobs; 
   title "Trimmed Mean of Y";
run;

Alternatively, PROC MEANS (SAS 9.4+) also supports trimmed means. For example:

/* Using PROC MEANS with OUTPUT to get trimmed mean */
proc means data=trial_data noprint trimmed=0.10;
   var Y;
   output out=Stats (drop=_TYPE_ _FREQ_) trimmed=Y_trimmed;
run;

proc print data=Stats noobs; 
   title "Trimmed Mean of Y via PROC MEANS";
run;

These code examples illustrate that one can easily obtain trimmed‐mean estimates in SAS by specifying the trimmed percentage (e.g. 0.10 for 10%) and directing the procedure output to a dataset for further use. (In practice, one would adjust trimmed= according to the planned trim proportion.)

Using Google Gemini, a comprehensive report on using trimmed means in clinical trials was generated and can be accessed here.