FMRI Study Statistical Methods Review

I need an independent statistical-methods review of a prospective human fMRI study before participant recruitment and funding.

The scientific protocol, primary estimand, decision rules, and simulation code already exist. This is not a request for general data analysis, manuscript statistics, or a redesign of the neuroscience.

The review should focus on:

* validating a repeated-measures within-subject primary estimand and variance/standardization approach;
* assessing possible residual timing/proximity bias in a counterbalanced design;
* reviewing prespecified GO / KILL / INCONCLUSIVE decision rules at a meaningful effect boundary;
* evaluating false-negative / false-KILL operating characteristics;
* assessing whether a bounded-bias decision rule is statistically defensible when the residual bias magnitude is not empirically identifiable;
* reviewing fixed-N enrollment versus one possible prespecified interim analysis;
* reviewing effect-blinded technical QC rules to ensure they do not introduce optional stopping or outcome-driven changes;
* independently inspecting supplied Python Monte Carlo simulation code and key operating-characteristic results.

I already have a compact review packet, a focused list of statistical questions, and reproducible simulation code.

Primary deliverable:
A concise written methodological review suitable for incorporation into the prospective Statistical Analysis Plan, including:

1. any statistical defects or unresolved assumptions;
2. recommendations for the final decision framework;
3. recommendation on fixed-N versus the proposed interim design;
4. recommendation on handling residual bias and false-KILL risk;
5. confirmation or criticism of the effect-blinded QC framework.

No participant data have been collected.

Preferred qualifications:

* PhD in biostatistics, statistics, quantitative psychology, psychometrics, or a closely related field;
* strong experience with prospective experimental design;
* repeated-measures / within-subject methods;
* Monte Carlo simulation and operating-characteristic analysis;
* bias / sensitivity analysis;
* preferably sequential or group-sequential design experience;
* ability to review Python statistical simulation code;
* fMRI / neuroimaging experience is helpful but not mandatory if the statistical-methods background is strong.

Please include 1–3 concrete examples of prior work involving at least two of the following:

* simulation-based operating characteristics;
* sequential/group-sequential designs;
* sensitivity analysis for unidentified or partially identified bias;
* advanced repeated-measures experimental design.

This is a bounded review. I am not seeking ongoing statistical support or a mandatory video call. A brief follow-up discussion can be arranged only if needed to clarify the written recommendations.