DATA OBFUSCATOR - LLM PROMPT PACK ================================= Generated by data-obfuscator 0.0.0.23 (released 2026-10-13) on 2026-10-13 06:54 UTC Masked dataset: obfuscated.csv | masking level 2 (clinical labels) HOW TO USE 1. Attach 'obfuscated.csv' to your LLM chat (or paste its contents) and then paste ONE of the three prompts below. 2. Share only the masked dataset and this prompt text. Never share keymap.txt or the original input file. 3. The prompts say units are not recorded in the file. Add them yourself - for example "time in hours, DV in ng/mL, AMT in mg" - plus the drug class or indication if you are comfortable disclosing it. 4. The facts in the prompts were computed from the masked file; check them if something looks off. DATASET FACTS (computed from the masked file) Records / columns : 180 / 22 Columns : ID, USUBJID, STUDYID, SITEID, ARM, TIME, TAD, AMT, DV, EVID, MDV, CMT, WT, AGE, SEX, RACE, DATE, INVESTIGATOR, COMMENT, COUNTRY, EMAIL, SUBJINIT Subjects : 20 (column ID) Observations : 80 analysable of 160 observation records (DV column DV) Dosing : 20 dosing records across 1 distinct AMT value (50) TIME range : 0 to 24 (units not recorded) Obs per subject : min 0, median 4, max 8 Compartments : CMT 1 (20 dose / 0 observation records) and CMT 2 (0 dose / 160 observation records) EVID values : 0, 1 Treatment groups : 2 treatment groups in ARM (ARM_A and ARM_B; labels are masked and arbitrary) Covariates : WT, AGE, SEX, RACE, COUNTRY Non-numeric columns : USUBJID, STUDYID, SITEID, ARM, RACE, DATE, INVESTIGATOR, COUNTRY (need DROP in a NONMEM $INPUT) Missing DV : 80 observation record(s) have no DV value (80 of them with MDV not equal to 1) Approximate size of the masked dataset in tokens (estimates; see tokens.txt): - GPT-4 / GPT-3.5 (cl100k_base): ~8905 - GPT-4o / GPT-4.1 / o-series / GPT-5 (o200k_base): ~8903 - Claude 3 and later (Anthropic): ~8906 - Gemini (Google): ~10184 - Llama 3 / 3.1 / 3.3 (Meta): ~8905 - Qwen 2 / 2.5 / 3 (Alibaba): ~12534 - Mistral 7B / Mixtral / Llama 2 (32k vocab): ~13826 1. ANALYZE THE DATA ------------------- Act as a senior pharmacometrician and clinical pharmacologist. The attached file 'obfuscated.csv' is a masked NONMEM-style dataset with 180 records and 22 columns (ID, USUBJID, STUDYID, SITEID, ARM, TIME, TAD, AMT, DV, EVID, MDV, CMT, WT, AGE, SEX, RACE, DATE, INVESTIGATOR, COMMENT, COUNTRY, EMAIL, SUBJINIT) from 20 subjects, containing 80 analysable observations in DV (observed range 0.098 to 0.976, units not recorded in the file) and 20 dosing records across 1 distinct AMT value (50); compartments are used as follows: CMT 1 (20 dose / 0 observation records) and CMT 2 (0 dose / 160 observation records); there are 2 treatment groups in ARM (ARM_A and ARM_B; labels are masked and arbitrary). Subject, study and site identifiers, categorical labels and dates have been recoded or shifted, so treat every label as arbitrary and do not try to re-identify anything. I will state the units for time, dose and the dependent variable; if I have not, tell me what you assume. First, confirm how you read the structure (what each column means, the record types implied by EVID, MDV and CMT, and the time, dose and concentration units you infer) and list anything ambiguous for me to confirm instead of guessing. Next, describe the study design and data completeness (dosing and sampling schedule, observations per subject, missing or censored values, subjects per treatment group) and summarise the distributions of WT, AGE, SEX, RACE, and COUNTRY. Then explore the DV-time profiles on linear and semi-logarithmic scales (individual profiles, and geometric mean or median by nominal time, dose-normalised and split by dose or group), and carry out a non-compartmental analysis (Cmax, Tmax, AUC, terminal half-life, apparent clearance and volume where the data allow); describe between-subject variability and any visible relationship between exposure and WT, AGE, SEX, RACE, and COUNTRY. Finish with a recommended structural, statistical and covariate strategy for population modelling, with your reasoning, the main risks in this dataset and the next analyses you would run. Quote exact numbers from the file, give reproducible R or Python code for every calculation, and clearly separate what the data show from what you are assuming. 2. CREATE A NONMEM CONTROL STREAM --------------------------------- Act as an expert NONMEM modeller. Using the attached masked dataset 'obfuscated.csv' (20 subjects, 80 analysable observations in DV (observed range 0.098 to 0.976, units not recorded in the file), and 20 dosing records across 1 distinct AMT value (50)) write a complete, runnable NONMEM 7.5 control stream for a population pharmacokinetic model, followed by a short explanation of each block. $INPUT must follow the file's exact column order: ID USUBJID=DROP STUDYID=DROP SITEID=DROP ARM=DROP TIME TAD AMT DV EVID MDV CMT WT AGE SEX RACE=DROP DATE=DROP INVESTIGATOR=DROP COMMENT COUNTRY=DROP EMAIL SUBJINIT (the non-numeric columns USUBJID, STUDYID, SITEID, ARM, RACE, DATE, INVESTIGATOR, and COUNTRY are marked =DROP because NONMEM cannot read text), and $DATA must read 'obfuscated.csv' with IGNORE=@ to skip the header row (the file is comma-delimited). Handle the record structure actually present: doses go into CMT 1 and observations are in CMT 2, which is typical of a depot (first-order absorption) to central-compartment structure, but confirm the route; in addition respect MDV for records that must not enter the likelihood. Select ADVAN and TRANS to match, justify the number of compartments and say when you would test a more complex model; parameterise in clearance and volume (plus absorption parameters if the data call for them) with log-normal inter-individual variability on ID-level random effects, a combined or proportional residual error model on DV, and initial estimates that you derive from the observed data (show the arithmetic); apply allometric scaling with WT (exponents 0.75 on clearances and 1 on volumes, centred on a sensible reference weight); include clearly labelled, switchable covariate relationships to test for AGE and SEX; RACE and COUNTRY are stored as text and therefore dropped from $INPUT, so say how I should recode them to numbers if I want to test them. Use $ESTIMATION METHOD=1 INTERACTION (or SAEM followed by importance sampling if you judge it more robust and tell me why), add $COVARIANCE, and a $TABLE that outputs ID TIME DV PRED IPRED CWRES and the other items needed for goodness-of-fit plots. List every assumption I must confirm before running (compartment numbering, units, LLOQ and handling of censored data, route of administration), do not invent data that are not in the file, and point out anything in the dataset that would make this control stream fail or mislead. 3. PERFORM QUALITY CONTROL CHECKS --------------------------------- Act as a pharmacometric data programmer carrying out an independent quality-control review of the attached masked NONMEM-style dataset 'obfuscated.csv' (180 records, 22 columns, 20 subjects; 80 analysable observations in DV (observed range 0.098 to 0.976, units not recorded in the file); 20 dosing records across 1 distinct AMT value (50)). Check each of the following and report the count and the exact ID/TIME of every offending record. (1) Structure: consistent number of fields per row, a single header row, the missing-value convention, and non-numeric entries in columns that should be numeric (currently text in USUBJID, STUDYID, SITEID, ARM, RACE, DATE, INVESTIGATOR, and COUNTRY). (2) Uniqueness and ordering: duplicate ID/TIME/EVID/CMT records, TIME decreasing within a subject, and ID values that are not contiguous. (3) Event logic: EVID, MDV, AMT and DV must agree (dosing records need a positive AMT and no DV; observation records need EVID=0 with a DV and MDV=0; MDV=1 wherever DV is missing); my own quick scan already found 80 observation record(s) with no DV value, 80 of which have MDV not equal to 1, so please quantify and explain that first. (4) Subject-level completeness: subjects with no dose, no observations, or observations before the first dose, and each subject's observation count against the median of the group. (5) Compartments: that CMT is valid and used consistently for dosing versus observation records (CMT 1 (20 dose / 0 observation records) and CMT 2 (0 dose / 160 observation records)). (6) TAD: recompute time-after-dose from TIME and the most recent dose and compare it with the TAD column. (7) Dependent variable: DV values that are zero, negative, implausibly large or inconsistent with neighbouring samples (observed 0.098 to 0.976), plus how below-limit-of-quantification values are coded. (8) Doses: whether the AMT values (50) are consistent with the treatment groups and with the dosing history. (9) Covariates: WT, AGE, SEX, RACE, and COUNTRY - check physiological plausibility, unexpected missing values, whether each is constant within a subject unless it is meant to vary with time, and the coding of categorical values. (10) Time translation: rebuild the elapsed time since each subject's first dose from DATE and compare it with TIME (allow rounding); report wrong units such as days instead of hours, dates out of order, mixed date formats, a missing day rollover after midnight, and any record whose date and TIME disagree. Return a prioritised findings table (severity, check, number of records, example rows, suggested fix), then a PASS or FAIL checklist for every numbered check, then reproducible R or Python code that performs the checks. Do not change, impute or delete data silently, and ask me about anything ambiguous instead of assuming.