MMRM in SAS: Step-by-Step

 /*-----------------------------*

 | 0) Housekeeping (optional)  |

 *-----------------------------*/

ods trace on;

options nolabel;


/*-----------------------------*

 | 1) Fit the MMRM             |

 *-----------------------------*/

proc mixed data=xxx(where=(0<vsn<=96)) order=data;

  class usubjid trtc vsn genotype;


  /* Fixed effects */

  model chg =

        trtc|vsn              /* trtc, vsn, and trtc*vsn */

        base*vsn

        b_age*vsn

        genotype*vsn

        / solution ddfm=kr;


  /* Repeated measures (UN covariance) */

  repeated vsn / subject=usubjid type=un;


  /* Precision/analytic weights (e.g., ATT) */

  weight _attwgt_;


  /* LSMEANS with visit-wise EC – Internal study differences */

  lsmeans trtc*vsn / diff cl slice=vsn;


  /* Overall (average over visits) treatment difference */

  estimate 'EC vs Internal study (average over VSN)' trtc 1 -1;


  /* Visit-specific EC – Internal study contrasts

     Ensure the CLASS order for VSN matches 24,48,72,96.

  */

  contrast 'EC – Internal study @ 24w'

           trtc 1 -1

           trtc*vsn   1 0 0 0    -1 0 0 0;


  contrast 'EC – Internal study @ 48w'

           trtc 1 -1

           trtc*vsn   0 1 0 0     0 -1 0 0;


  contrast 'EC – Internal study @ 72w'

           trtc 1 -1

           trtc*vsn   0 0 1 0     0  0 -1 0;


  contrast 'EC – Internal study @ 96w'

           trtc 1 -1

           trtc*vsn   0 0 0 1     0  0  0 -1;


  /* Global joint test across visits (multi-df Wald) */

  contrast 'Global treatment difference across visits'

           trtc 1 -1  trtc*vsn  1 0 0 0  -1 0 0 0,

           trtc 1 -1  trtc*vsn  0 1 0 0   0 -1 0 0,

           trtc 1 -1  trtc*vsn  0 0 1 0   0  0 -1 0,

           trtc 1 -1  trtc*vsn  0 0 0 1   0  0  0 -1;


  /* Capture outputs for TLFs/QC */

  ods output

    lsmeans   = lsm_vsn_trt

    diffs     = dif_vsn_trt

    covparms  = cov_un

    estimates = est_tests;

run;


ods trace off;

What each step does (and how to read it)

  1. PROC MIXED & CLASS
    Declares the repeated-measures factors. order=data ensures your visit order (e.g., 24, 48, 72, 96) is used when constructing contrasts and LSMeans.

  2. MODEL statement

    • trtc|vsn fits treatment, visit, and their interaction, so the treatment effect can differ by visit (core of MMRM).

    • base*vsn, b_age*vsn, genotype*vsn allow covariate effects to vary by visit (common in SAPs for longitudinal change).

    • ddfm=kr uses Kenward–Roger degrees of freedom (better small-sample properties).

    • solution prints estimates for all fixed effects (useful for QC, usually not reported directly).

  3. REPEATED with TYPE=UN
    Sets an unstructured covariance for within-subject residuals—maximally flexible (variance and correlation free to vary by visit pair). Appropriate when N allows, and convergence is stable.

  4. WEIGHT attwgt
    Applies precision (analytic) weights (e.g., ATT weights from propensity modeling). Larger weights imply smaller residual variance and more influence.
    Tips: ensure weights are positive; consider trimming/stabilizing extreme weights; rerun as sensitivity with trimmed weights.

  5. LSMEANS trtc*vsn / diff cl slice=vsn
    Produces visit-specific EC–Mission differences with CIs and p-values without hand-coding coefficients. This is the easiest, least error-prone way to get per-visit effects.

  6. ESTIMATE 'EC vs Mission'
    Gives an average treatment difference across visits under the model; often de-emphasized when the interaction is present (interpret with care).

  7. CONTRAST (per visit)
    Explicitly reconstructs EC–Mission at a given visit using the cell-means coding for trtc and trtc*vsn. Functionally matches the SLICE=VSN results; useful for an auditable, fixed set of inferential statements.

  8. Global joint test (multi-df)
    The comma-separated CONTRAST forms a Wald test that all visit-specific EC–Mission differences are zero simultaneously. Report the F/χ², df, and p-value as your global longitudinal treatment test.

  9. ODS OUTPUT

    • lsm_vsn_trt: LSMeans for each (trtc, vsn) cell (good for figures).

    • dif_vsn_trt: Per-visit differences with SE, t, CI, p (table/forest).

    • cov_un: the estimated UN covariance (check plausibility, convergence).

    • est_tests: rows for ESTIMATE/CONTRAST (per-visit and global tests).

Interpretation + QC checklist

  • Primary readouts: table from dif_vsn_trt (EC–Mission at 24/48/72/96 weeks) and the Global test from est_tests.

  • Convergence & fit: in the PROC MIXED log and cov_un; if UN struggles, consider CSH or ARH(1) as sensitivity.

  • Missing data: MMRM assumes MAR given included covariates. If dropout is meaningful, plan MI/tipping-point sensitivity.

  • Level order safety: Ensure VSN levels are in the intended order (use order=data and pre-sort your dataset).

  • Weights: Inspect distribution; consider stabilized ATT, trimming (e.g., 1st–99th percentile).

  • Multiplicity: Report both per-visit results and the global joint test to support the overall claim.

Comments

Post a Comment

Popular posts from this blog

Use FDA Suggestions on Missing Data and Sensitivity Analyses

  The U.S. FDA provides guidance and expectations on how to handle missing data in clinical trials, especially in the context of estimands and sensitivity analyses . Below is a detailed summary based on key regulatory documents and practices. 📘 1. Key FDA Guidance Documents A. FDA (2019): "Estimands and Sensitivity Analyses in Clinical Trials" Focus: Aligning analysis with the trial objective and handling intercurrent events , like treatment discontinuation or dropout. Introduces the estimand framework from ICH E9(R1). Emphasizes sensitivity analysis under plausible MNAR assumptions. 🔗 Link: FDA Estimand Guidance (PDF) 📌 2. FDA Expectations at a Glance Topic FDA View Primary analysis Can assume MAR (e.g., MMRM) if justified Sensitivity analysis Must explore MNAR scenarios (not just MAR) Multiple imputation Acceptable if properly implemented Pattern-mixture models Encouraged as part of sensitivity analyses Reference-based imputation (CIR, J2R) Inc...
Read more

Understanding Binding vs. Non-Binding Futility Analysis in Clinical Trials

Image
  1.What is the difference between binding futility and non-binding futility analysis ? In clinical trials,   futility analysis   is a type of interim analysis used to determine whether it is unlikely that a study will achieve its objectives if it continues as planned. This helps avoid wasting resources and exposing participants to ineffective treatments. Futility analyses can be   binding   or   non-binding , and the distinction is important for trial design and interpretation. 🔹 Binding Futility Analysis Definition : If the futility boundary is crossed, the trial  must be stopped . Implication : It is part of the formal decision-making process and is enforced by the protocol or statistical analysis plan. Use Case : Often used when ethical or resource concerns demand early termination if the treatment is clearly not effective. 🔹 Non-Binding Futility Analysis Definition : If the futility boundary is crossed, the trial  may continue ...
Read more

Not Estimable: Why the Median Time-to-Event Is Sometimes Out of Reach ?

 In clinical trials and survival analyses, statements like: “ The median DoR was not estimable with a median duration of follow-up of 26.6 months ,” or “ The median PFS and OS were not estimable ” typically mean that insufficient events (e.g., disease progression, death, or end of response) have occurred at the time of data cutoff to accurately estimate the median using Kaplan-Meier survival analysis . Here’s a breakdown of the reasoning: 🔍 What Does "Not Estimable" Mean? In the context of Kaplan-Meier curves , median is the time at which 50% of patients have experienced the event (e.g., progression, death, end of response). If fewer than 50% of patients have had the event by the data cutoff, then the median cannot be computed because the curve hasn’t dropped to 50%. So the median is “not estimable” (NE) or not reached . 📌 Common Scenarios for NE (Not Estimable): Durable responses or long survival : If a large proportion of patients are st...
Read more