Industry-Sponsored N-of-1 Trials in Orphan Drug Development

N-of-1 clinical trial is often mentioned as one of the approaches to address the challenging issues in orphan drug development. at one point, FDA rolled out more guidance on 'N of 1' gene therapies including the guidance on "Individualized Antisense Oligonucleotide Drug Products for Severely Debilitating or Life-Threatening Diseases". AHRQ had a publication "Design and Implementation of N-of-1 Trials: A User’s Guide". Recent "baby KJ's story" was touted by the FDA as a success story for individualized treatment for a N-of-1 disease condition "FDA Cell & Gene Therapy Roundtable: Putting Every Patient Within Reach of Innovation". Nature magazine had an article "A framework for N-of-1 trials of individualized gene-targeted therapies for genetic diseases"

We would like to check the reality to see how many drugs or biological products were approved by the US FDA based on the evidence from N-of-1 clinical trials. 

We identified a few ultra-rare disease cases in the past decade where industry or institutional sponsors conducted single-patient (“N-of-1”) trials of experimental therapies. Notably, no drug or biologic approved in the last 10 years appears to have relied on N-of-1 clinical trial data for its FDA approval. The only FDA approval historically based in part on N-of-1 evidence was danazol for hereditary angioedema in the 1980s. Below we summarize key examples of N-of-1 trials involving drugs/biologics and rare indications:

Drug/ Biologic	Orphan Indication	Sponsor (Company/Institution)	N-of-1 Trial Design & Results	Regulatory Outcome
Milasen (custom ASO)	CLN7 Batten disease (Infantile neuronal ceroid lipofuscinosis)	Boston Children’s Hospital (Timothy Yu’s lab)	Single-patient, customized antisense oligonucleotide. FDA cleared an IND for Mila in late 2017, and she received serial intrathecal doses. Her seizure frequency fell dramatically (from ~30/day to <10/day) and her neurologic decline stabilized during treatment.	Investigational (IND only; no FDA marketing approval)
Custom ASO for A-T (no trade name)	Ataxia-Telangiectasia	Boston Children’s Hospital / A-T Children’s Project (Dr. Yu)	Personalized splice-modulating ASO for one AT patient. FDA granted an IND in 2020 for an N-of-1 trial. The child began receiving the ASO (via intrathecal delivery) later in 2020; detailed clinical outcomes have not yet been published.	Investigational (IND only; ongoing treatment)
CRD‑TMH‑001 (CRISPR-based gene therapy)	Duchenne muscular dystrophy (specific exon 1/promoter mutation)	Cure Rare Disease (nonprofit biotech)	Single-patient CRISPR/Cas9 gene-editing therapy. FDA cleared an IND in August 2022 for a one-person trial. The only enrolled patient (the sponsor’s founder’s brother) was dosed but later died (cause under review). As a result, efficacy remains unknown.	Investigational (IND only; trial suspended)
Danazol	Hereditary angioedema	Original developer (Searle/Pfizer)	Historical example: A multi-crossover N-of-1 trial (9 patients, 47 treatment periods) was reported in 1976. This small trial of danazol demonstrated reversal of HAE symptoms and formed part of the FDA approval record.	Approved (for HAE; orphan indication) https://www.fda.gov/media/87621/download section 5.2.4

Each case is discussed in more detail below.

Milasen for Batten Disease (CLN7)

• Drug & Sponsor: Milasen is a one-off antisense oligonucleotide (ASO) developed at Boston Children’s Hospital by Dr. Timothy Yu’s team. It was designed for an 8‑year-old patient (“Mila”) with a unique retrotransposon mutation in the CLN7 gene causing Batten disease.
• N-of-1 Design & Results: After rapid sequencing and preclinical work, the FDA granted an IND to trial milasen in late 2017. Mila then received intrathecal milasen injections every 3 months under an N-of-1 protocol. Over ~9 months of treatment, Mila’s seizure frequency fell from ~30/day to under 10/day, and her neurological decline notably stabilized. No significant adverse effects were reported, and her condition improved in several clinical measures. These results were published in the New England Journal of Medicine (2019).
• Regulatory Outcome: Milasen remains an investigational therapy given under an IND. It was not submitted for FDA marketing approval (and Mila later died in early 2021). Thus, no FDA approval was sought or granted for milasen.

Customized ASO for Ataxia-Telangiectasia

• Drug & Sponsor: A patient-specific splice-switching ASO (unnamed) was developed by Dr. Yu’s group (supported by the A-T Children’s Project) for a young girl with ataxia-telangiectasia (AT). The ASO was tailored to correct her particular ATM gene splicing defect.
• N-of-1 Design & Results: The FDA granted IND approval for this one-off ASO treatment in 2020actionforat.org. The affected child began receiving the ASO via intrathecal infusion that year, under a single-patient trial protocol. (To our knowledge, no detailed efficacy data from this N-of-1 trial have yet been published.)
• Regulatory Outcome: This custom AT therapy is in the investigational stage (IND only). No FDA marketing approval has been sought or obtained.

CRD‑TMH‑001: CRISPR Therapy for Duchenne Muscular Dystrophy

• Drug & Sponsor: CRD‑TMH‑001 is a one-time, custom CRISPR/Cas9 gene-editing therapy developed by Cure Rare Disease (a nonprofit “n=1” biotech) to target a specific exon 1/promoter mutation in the DMD gene. Cure Rare Disease’s founder (motivated by his brother’s illness) sponsored the program.
• N-of-1 Design & Results: In mid-2022, the FDA cleared a first-in-human IND for CRD-TMH-001. The trial was explicitly a single-patient study: one eligible DMD patient (age 27, the sponsor’s brother) was treated. The therapy was infused intrathecally as planned. Tragically, the patient died several months later; a causality review is ongoing. To date, no efficacy data (or definitive safety findings) from this N-of-1 trial have been published.
• Regulatory Outcome: CRD-TMH-001 remains purely investigational under the IND. No further patients have been treated and no FDA approval has been pursued.

Historical FDA Approval: Danazol for Hereditary Angioedema

• Drug & Indication: Danazol is a synthetic steroid used to prevent attacks of hereditary angioedema (HAE), a rare genetic edema disorder.
• N-of-1 Trial Evidence: In 1976, a classic FDA review noted that the approval of danazol for HAE was supported in part by an N-of-1 multiple-crossover trial of nine patients (47 treatment periods). In that study, each subject alternated between danazol and placebo in random sequence, demonstrating clear reversal of clinical and biochemical abnormalities on danazol. This case was cited in section 5.2.4 of the FDA's Good Review Practice: Clinical Review of Investigational New Drug Applications
• Regulatory Outcome: Danazol was approved for HAE prevention (an orphan indication) and remains an approved drug (marketed as DANOCRINE). This case shows that the FDA has historically accepted single-subject crossover data in rare diseases. However, it is an older example; no similar approvals based on N-of-1 data have emerged in the past decade. The N-of-1 study of Danazol in HAE was published in New England Journal of Medicine in 1976 "Treatment of Hereditary Angioedema with Danazol — Reversal of Clinical and Biochemical Abnormalities"

Summary

In summary, we found only a handful of “N-of-1” clinical trials over the past 10 years involving investigational drugs for very rare diseases. These include two custom antisense oligonucleotides (Mila’s milasen for Batten disease, and an ASO for one AT patient) and one CRISPR gene therapy for Duchenne MD. All were “approved” only under individual INDs and have shown promising signals but remain experimental; none has led to FDA marketing approval. Apart from the historical example of danazol (approved via N-of-1 data in the 1970s), our searches of FDA databases, ClinicalTrials.gov, and published literature did not identify any new orphan drugs approved based on N-of-1 trial evidence. Thus, while N-of-1 trials are an emerging tool for ultra-rare diseases, they have not yet changed the landscape of FDA approvals in the last decade.

Steroid Tapering Design in Action - Failed

In a previous post, I discussed steroid tapering design clinical trials. In these trials, the primary efficacy endpoint is the reduction in steroid dose. Efficacy is demonstrated if patients receiving the experimental treatment are able to reduce their steroid dose significantly more than those in the placebo group.

Steroids remain highly effective for many conditions, particularly those involving chronic inflammation or an overactive immune system. However, long-term or high-dose steroid use is associated with numerous side effects. Therefore, it is highly desirable to develop therapies that allow for steroid dose reduction—or even complete steroid sparing—while maintaining disease control.

One of these diseases is sarcoidosis or lung sarcoidosis. Sarcoidosis is an inflammatory disease in which the immune system overreacts, causing groups of cells to form clusters of inflamed tissue called "granulomas" in one or more organs of the body. The most affected organ is lung. Sarcoidosis symptoms can be treated using corticosteroids, or prednisone, which turn down the immune system's activity to reduce inflammation.

A sponsor, aTyr Pharma, conducted a phase 3, confirmatory trial to investigate if their experimental drug efzofitimod is effective in treating the pulmonary sarcoidosis. The study "Efficacy and Safety of Intravenous Efzofitimod in Patients With Pulmonary Sarcoidosis" was designed as steroid tapering with the primary efficacy endpoint being "Change from baseline in mean daily oral corticosteroid (OCS) dose at Week 48".

This morning, the sponsor issued a press release announcing Topline Results from Phase 3 EFZO-FIT™ Study of Efzofitimod in Pulmonary Sarcoidosis. Study did not meet primary endpoint in change from baseline in mean daily oral corticosteroid (OCS) dose at week 48, although clinical benefit for efzofitimod observed across multiple study parameters.

The change from baseline in mean daily OCS dose reduced to an average of 2.79 mg for the high dose of efzofitimod versus 3.52 mg for placebo, resulting in an insignificant treatment difference. The sponsor blames the unexpected placebo response.

aTyr flunks phase 3 lung disease trial, deflating stock, but still plans talks with FDA

The steroid tapering design offers important clinical advantages by directly addressing the need to reduce long-term steroid exposure, a major source of morbidity in many chronic inflammatory and immune-mediated diseases. The steroid tapering design and the change from baseline in steroid dose is accepted by the regulatory agencies. It provides a patient-centered and easily quantifiable endpoint that demonstrates whether an investigational treatment can maintain disease control while allowing for a lower steroid dose. 

However, this design also has limitations. Variability in tapering schedules, risk of disease flares, and ethical concerns regarding aggressive tapering in placebo groups can complicate interpretation. In addition, results may be less generalizable to steroid-naïve patients, and regulatory acceptance of steroid-sparing endpoints as stand-alone primary outcomes remains uncertain. Careful planning and standardization are therefore essential to balance the scientific value with patient safety and trial credibility.

ICH E21 "Inclusion of Pregnant and Breastfeeding Individuals in Clinical Trials"

US FDA has just endorsed the draft guideline of ICH E21 "Inclusion of Pregnant and Breastfeeding Individuals in Clinical Trials". E21 Training Material can also be found at ich.org. 

The ICH E21 guideline aims to provide recommendations for the appropriate inclusion and retention of pregnant and breastfeeding individuals in clinical trials. This is crucial because historically, these populations have been largely excluded, leading to a significant lack of data on the safety and efficacy of medicinal products during pregnancy and lactation.

For clinical trialists, the guideline emphasizes:

• Proactive Planning: Sponsors should consider strategies to generate data on medicinal product use in pregnancy and breastfeeding from early stages of development, including nonclinical studies through post-approval.

• Risk-Benefit Evaluation: A thorough assessment of potential risks and benefits for both the pregnant or breastfeeding individual and the fetus/breastfed infant is essential.

• Ethical Considerations and Informed Consent: The guideline stresses the importance of obtaining truly informed consent, ensuring clear communication of potential risks and benefits, and avoiding any undue influence or coercion. Efforts should be made to reduce the burden of study procedures on these participants.

• Study Design and Implementation: It provides practical guidance for designing clinical trial protocols, including considerations for data collection, safety monitoring, and follow-up for both mother and child. This also includes pharmacokinetic (PK) and dosing considerations, as physiological changes during pregnancy can impact drug pharmacokinetics.

• Addressing Unmet Medical Need: The inclusion of pregnant and breastfeeding individuals is particularly important for conditions where there is a high unmet medical need for treatment in pregnancy or while breastfeeding, though the guideline's scope is not limited to these scenarios.

• Data Generation: The overarching objective is to facilitate the generation of robust clinical data that allows for evidence-based decision-making on the safe and effective use of medicinal products by these individuals and their healthcare providers.

The ICH E21 guideline is a significant step towards improving the evidence base for prescribing medications during pregnancy and lactation, ultimately aiming for better patient outcomes for these underserved populations.

Just several weeks ago, I wrote a blog post on this same topic "Managing Pregnancies in Clinical Trials: Regulatory Guidance and Best Practices", which offered a comprehensive overview of the historical exclusion of pregnant women from clinical trials, the reasons behind this, and the evolving regulatory landscape advocating for their inclusion. The article emphasized a balanced approach, highlighting FDA's shift towards diversity in trials and the ethical imperative to gather data on drug use in pregnancy to ensure evidence-based care. It delved into practical aspects like informed consent, pre-screening procedures, managing pregnancies discovered during trials (including stopping drug, unblinding, and re-consenting), and the importance of detailed data collection and specialized safety monitoring. The blog post also strongly argued for the ethical benefits of inclusion, asserting that systematic exclusion leads to greater harm by forcing healthcare providers and patients to make decisions without adequate evidence. 

The newly endorsed ICH E21 guideline directly aligns with various FDA guidance described in the blog post and significantly reinforces the "balanced approach" and the ethical arguments. 

• ICH E21 provides a more harmonized, international standard for how this inclusion should be achieved proactively throughout drug development. 
• ICH E21 offers detailed recommendations on study design, ethical considerations, pharmacokinetic and dosing adjustments, safety monitoring, and data collection specifics for pregnant and breastfeeding individuals, directly addressing the "when and how" question you pose at the end of your article. 
• ICH E21 reiterates the need for IRBs with maternal-fetal expertise, suggesting consultation with specialists for study design and safety monitoring. 
• ICH E21 highlights the critical importance of robust informed consent and ongoing safety monitoring for both the mother and the child. 
• ICH E21 essentially provides the detailed, internationally agreed-upon framework, moving the industry further towards systematic and responsible inclusion rather than historical exclusion.

Rank Analysis of Covariance (RANK ANCOVA) versus Aligned Rank Stratified Wilcoxon Test

In clinical trials where outcome measures violate the assumptions required for parametric statistical methods such as analysis of covariance (ANCOVA), non-parametric approaches are often employed. The classical two-sample Wilcoxon rank-sum test (also known as the Mann–Whitney U test) can be used in such cases; however, this method does not allow for adjustment of covariates.

Randomized clinical trials often adjust for baseline covariates (FDA 2021 guidance "Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products") to improve precision. When the outcome is non‐normal or ordinal, rank‐based methods offer alternatives to parametric ANCOVA. Two popular approaches are: (1) Rank ANCOVA – an ANCOVA on rank‐transformed data (the “rank‐transformation ANCOVA” of Conover and Iman), and (2) Aligned‐Rank Stratified Wilcoxon – a Wilcoxon rank‐sum test applied to responses “aligned” by covariate effects (e.g. Hodges‐Lehmann or van Elteren style). Below we compare their theory, practical use, pros/cons, and SAS implementations.

Theoretical Properties

• Rank ANCOVA (Rank‐Transform ANCOVA): 

Replace the outcome Y by its overall ranks and fit a usual linear ANCOVA model (treatment + covariates). Under the null it tests whether the treatment coefficient in the rank‐scale model is zero. In effect, the null is “no difference in the adjusted rank‐distributions” between groups. This approach assumes a linear additive model for covariate effects (on the rank‐scale) with homogeneous slopes across treatments. Conover and Iman showed the rank‐transform inherits the robustness and power properties of rank tests in regression. However it is not fully model‐free: violations of slope homogeneity or covariate–treatment interactions can invalidate the test. The test statistic is simply the usual F (or t) from ANCOVA on ranks. Rank ANCOVA was proposed by Dana Quade (1967) "Rank Analysis of Covariance" in JASA and was popularized after the book by Stokes, Davis, and Koch "Categorical Data Analysis Using SAS" where there was a dedicated chapter to discuss the RANK ANCOVA.

• Aligned‐Rank Stratified Wilcoxon: 

Agligns outcomes within each randomization stratum by subtracting the stratum’s Hodges–Lehmann shift (a median-based location estimate) and then applies a Wilcoxon rank-sum test to the pooled aligned data. Effectively performs an un-stratified Wilcoxon test on aligned values. Yields a Hodges–Lehmann estimate of the overall median shift (with confidence interval) as the treatment effect.

 This method is fundamentally a rank‐sum test controlling for strata or covariates. In a stratified design (or ANCOVA context), one assumes a model Yij=μ+βi+τjY_{ij}=\mu +\beta_i+\tau_j where βi\beta_i are strata/covariate effects and τj\tau_j are treatment effects. The null hypothesis is τ1=τ2=0\tau_1=\tau_2=0 (no treatment shift). To remove (align) the strata effects, one subtracts a location statistic (stratum mean/median/Hodges–Lehmann) from each YijY_{ij}, yielding “aligned” responses. These aligned values (now centered by stratum) are pooled and ranked, and a Wilcoxon test is performed ignoring strata. Equivalently, with a STRATA factor one can perform a Van Elteren (stratified Wilcoxon) test. The resulting null distribution is distribution‐free (asymptotically normal, exact via permutations), requiring only that within each stratum the treated and control distributions differ by a location shift. In summary, Rank ANCOVA assumes a linear rank‐model for covariate adjustment, whereas the Aligned‐Rank Wilcoxon assumes only an additive strata effect (with no specific distribution form) and tests a location‐shift null in a stratified rank framework.

Practical Applications

• Outcome types: Both methods suit continuous or ordinal outcomes that violate parametric assumptions. Rank ANCOVA can handle multiple continuous covariates straightforwardly (since one simply adds them to the ANCOVA on ranks). Aligned‐rank tests naturally handle categorical strata (e.g. randomization factors, center, or baseline strata). If the covariate is continuous (e.g. baseline measure), one may either form strata (e.g. quantiles) or perform alignment via regression residuals before ranking.

• Robustness: Both are robust to outliers and non‐normality because they use ranks. Conover and Iman noted that rank‐transformed regression is robust and powerful even under heavy‐tailed or skewed data. The aligned‐rank approach is fully distribution‐free under its null hypothesis, and by aligning removes nuisance location effects (e.g. site or block means). In practice, Ye and Lai (2023) found that both a covariate‐adjusted rank‐sum (rank ANCOVA) and an aligned‐rank test yielded narrower confidence intervals and maintained type I error across clinical trials, compared to unadjusted tests.

• Small samples: Neither method “magically” solves small‐N issues. Rank ANCOVA relies on large‐sample ANCOVA (normal‐theory) approximations on ranks. In contrast, the aligned‐rank/Wilcoxon method can use exact computations (via PROC NPAR1WAY) if samples are very small. However, note that alignment can behave erratically in very small samples according to some studies. In moderate samples both methods are generally acceptable. Both methods lose some efficiency if there are many ties or if covariate effects are very non‐linear.

Advantages and Disadvantages

• Rank ANCOVA 

– Advantages: It is conceptually simple (just rank the data and run standard ANCOVA). It fully utilizes continuous covariates in a regression framework, and retains much of the power of ANOVA tests while being robust to non‐normal errors. If the linear model is correct (in the rank‐scale), the test is valid and can easily test interactions. Simulations suggest rank‐based ANCOVA is often powerful when parametric assumptions fail. Adjusting for covariates in this way typically yields more precise estimates (narrower CIs) than unadjusted ranks.

– Disadvantages: Its interpretation is subtle: one is testing effects on the ranks of the outcome, not its mean or median in original units. Thus the “treatment effect” corresponds to a location shift in the ranked distribution. Experts caution that rank‐transform ANCOVA is not a direct test of medians in the original scale, and may give misleading inference if reporting medians. It also implicitly ranks the covariates (if one ranks them too) which can distort relationships. If the homogeneity of slopes assumption is violated, the rank‐ANCOVA test can be invalid. In summary, it is not fully nonparametric – it still relies on the linear model structure (albeit on ranks) and on large‐sample approximations.

• Aligned Rank Wilcoxon 

– Advantages: This method is fully nonparametric under a location‐shift null, and can produce an easily interpreted Hodges–Lehmann median shift estimate with confidence limits (e.g. via ALIGN=STRATA(HL) in SAS). It inherently accounts for stratification/baseline effects by alignment, so it naturally handles block effects or randomization strata (van Elteren’s approach). It does not require ranking the covariate itself – only the outcome after centering. In practice it has robust Type I error even under heteroscedasticity or skewness, so long as the alignment model is reasonable. SAS’s PROC NPAR1WAY provides this test directly (see below).

– Disadvantages: One must specify the strata or alignment model in advance. If there is only one continuous covariate, one needs to decide how to align (e.g. subtract predicted baseline effect or stratum median). The choice of alignment statistic (median vs mean vs HL) can affect results slightly. If strata are very small, the within‐stratum rankings may be unstable. Unlike rank‐ANCOVA, this approach cannot easily incorporate arbitrary continuous covariates without discretization or pre‐alignment. In very small samples, alignment procedures can suffer from erratic Type I behavior (especially if alignment assumptions are misspecified). Finally, it is less familiar to many practitioners and thus may be harder to explain.

SAS Implementation

• Rank ANCOVA: SAS does not have a dedicated “rank‐ANCOVA” proc. A common workaround is to rank the data (using PROC RANK) and then run a standard ANCOVA (PROC GLM or PROC REG) on the ranked outcome. For example:

proc rank data=trial out=ranked ties=mean;

  var Y; ranks Y_rank;

run;

proc glm data=ranked;

  class Trt;

  model Y_rank = Trt Baseline;

run;

This fits a linear model to the ranks. Alternatively, one can implement the Hettmansperger–McKean aligned‐rank procedure by first regressing Y on the covariate (e.g. with PROC REG or PROC ROBUSTREG) and then ranking the residuals to test the treatment effect. In short, one must manually rank or residualize and then use standard SAS procs. (There is no built‐in PROC RANKANCOVA or similar in SAS.) 

The book "Categorical Data Analysis Using SAS®, Third Edition" by Stokes, Davis, and Koch contains the sample SAS codes indicating three steps in performing the Rank ANCOVA. 

proc rank nplus1 ties=mean out=ranks;

   by center; 

   var before after;

run;

proc reg noprint; 

   by center;

   model after=before;

   output out=residual r=resid;

run;

proc freq;

   tables center*group*resid / noprint cmh2; 

run; 

 

• Aligned Rank Stratified Wilcoxon: SAS’s PROC NPAR1WAY supports both stratified Wilcoxon and aligned‐rank tests. For a Van Elteren stratified Wilcoxon, use the STRATA statement without alignment. For example:

proc npar1way data=trial wilcoxon;

  class Trt;

  strata Center;  /* stratification variable */

  var Change;

run;

This computes the stratified Wilcoxon (van Elteren) test and provides both one‐sided and two‐sided p-values. To perform an aligned‐rank test, use the ALIGN=STRATA option (with STRATA) in PROC NPAR1WAY. For example:

proc npar1way data=trial wilcoxon align=strata(hl);

class Trt;

strata Center;

var Change;

run;

This subtracts the stratum median (or Hodges–Lehmann shift if (HL) is specified) from each response before ranking, then conducts the Wilcoxon test. The output will include the Hodges‐Lehmann estimate and CI for the location shift. Thus, PROC NPAR1WAY with the STRATA and ALIGN=STRATA options directly implements the aligned‐rank stratified Wilcoxon test. (By default, RANKS=STRATUM is used and weights are by stratum, yielding van Elteren.)

Notice that for both RANK ANCOVA and Aligned Rank Stratified Wilcoxon test, only p-value will be obtained. The treatment difference (so called the difference in medians or location shift in medians) needs to be calculated using Hodges-Lehman estimator.  

Side-by-Side Comparison of RANK ANCOVA and Aligned Rank Stratified Wilcoxon

The table below summarizes these points:

	Rank ANCOVA (Rank‐transform ANCOVA)	Aligned‐Rank Stratified Wilcoxon
Null hypothesis	H₀: no treatment effect on the ranked outcome (treatment coeff=0 in rank‐scale model).	H₀: no treatment effect (no location shift) in stratified model (τ1=τ2=0\tau_1=\tau_2=0).
Model / Assumptions	Linear model on ranks: assume covariate effects are additive and linear on the rank‐scale, with equal slopes across groups. (No distributional form assumed beyond this.)	Additive strata model: Y=μ+βi+τjY=\mu+\beta_i+\tau_j. Assume observations are exchangeable within strata after alignment. Does not assume specific distribution shape.
Test statistic	ANCOVA F or t on the ranked outcome (i.e. usual parametric test applied to ranks).	Wilcoxon rank‐sum on aligned data. (Equivalently, stratified Wilcoxon/Van Elteren statistic.)
Distribution	Uses large‐sample normal/chi‐square approximations (from GLM on ranks). No exact test available in SAS for this.	Asymptotic normal (z) or exact (via permutation) available. SAS PROC NPAR1WAY can compute exact Wilcoxon p-values within strata.
Outcome type	Continuous or ordinal outcomes. Can include multiple continuous covariates in model.	Continuous or ordinal outcomes. Requires (or creates) strata: typically categorical covariates (e.g. randomization strata) or aligned by regression.
Robustness / Outliers	Not fully nonparametric. Robust to outliers and non‐normality (rank‐based). However, if covariate–treatment interactions exist or slope equality fails, Type I error can inflate.	Fully nonparametric. Robust to outliers (rank‐based) and handles non‐normal/heteroscedastic data well. Alignment removes nuisance location shifts.
Small sample	Relies on asymptotic ANCOVA on ranks; no built‐in exact test. May be liberal if sample is very small or distribution very discrete.	PROC NPAR1WAY can use exact Wilcoxon (by strata) for small N. Alignment in tiny samples may have less stable Type I.
Advantages	Easy to implement via standard ANCOVA tools. Uses full continuous covariate information. Retains high power under model correctness. Covariate adjustment usually reduces variance (narrower CIs).	Nonparametric (distribution‐free) test. Directly yields Hodges–Lehmann shift estimate and CI. Naturally incorporates stratification/blocks (van Elteren). Valid under mild assumptions.
Disadvantages	Tests on ranks, not raw scale, so interpretation of effect size is not straightforward. Not a test of medians in original units. Can mislead if model assumptions fail. No simple SAS proc – must manually rank or regress.	Must predefine strata or alignment model. Less flexible for multiple continuous covariates (usually one strata factor). Alignment choice (median vs mean) can affect results. In small samples, alignment may behave poorly.
SAS implementation	No single procedure. Typically use PROC RANK to create ranked Y, then PROC GLM (or PROC REG) with covariates on ranks. Alternatively, regress Y on covariate (PROC REG/ROBUSTREG), rank the residuals, and test group difference. (All manual steps.)	Use PROC NPAR1WAY. For stratified Wilcoxon: STRATA statement (no ALIGN) yields Van Elteren test. For aligned‐rank: add ALIGN=STRATA (and optionally (HL) or (MEAN) option) in PROC statement. E.g.  proc npar1way data=… wilcoxon align=strata(hl);  class Trt;  strata Covar;  var Y; run;.

Some Studies Where the Rank ANCOVA or Aligned Rank Stratified Wilcoxon Were Used:

• Hoeper et al (2023) "Phase 3 Trial of Sotatercept for Treatment of Pulmonary Arterial Hypertension" and details in the attached protocol and SAP - Aligned Rank Stratified Wilcoxon was used. 
• Statistical Analysis Plan for "A Phase 2b, Dose-Ranging, Randomized, Double-Blind, Placebo Controlled, Multicenter Study of Rodatristat ethyl in Patients with Pulmonary Arterial Hypertension" - Aligned Rank Stratified Wilcoxon was used. 
• Van Dyck et al (2022) "Lecanemab in Early Alzheimer’s Disease" and details in the attached protocol and SAP - rank analysis of covariance was used for sensitivity analysis to assess the robustness of the primary efficacy analysis using MMRM
• NDA 22,535/0045 Pirfenidone in the treatment of IPF - Rank ANCOVA was used
• NDA 207533/ O-1 ARISTADA for Treatment of Schizophrenia - Rank ANCOVA was used