hinna0818/ukbanalytica_v2/ukbsci-regression

Name: ukbsci-regression
Author: hinna0818

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hinna0818/ukbanalytica_v2
inst/skills/UKBAnalytica_skills/ukbsci-regression/SKILL.md
Updated 20 Sep 2026
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Fit linear, logistic, Cox, GLM, negative-binomial, GAM, Fine-Gray competing-risk, lagged Cox, trend, PH-diagnostic, and restricted cubic spline models on UK Biobank Research Analysis Platform (RAP) cohorts built with UKBAnalytica. Wraps runmulti_lm, runmulti_logit, runmulti_cox, runmulti_glm, runmulti_negbin, runmulti_gam, run_regression, runmulti_cox_lag, runmulti_cox_zph, runmulti_competing, runmulti_trend, ukb_cox_diagnostics, run_rcs, and plot_rcs. Use this skill when the user asks to fit any regression model on the cohort, run batch regressions across multiple exposures, fit count/rate outcomes with Poisson/quasi-Poisson/Gamma GLM, fit negative-binomial for overdispersed counts, screen for non-linearity with GAM, check proportional-hazards assumptions, run a lagged sensitivity analysis, perform competing-risks Fine-Gray modeling, test a dose-response trend, or draw an exposure-response RCS curve. Triggers: UKB regression, Cox model, logistic regression, GLM, Poisson, negative binomial, GAM, batch regression, proportional hazards, competing risks, Fine-Gray, p_trend, restricted cubic spline, RCS, 非线性, UKB 回归, Cox 模型, 广义线性模型, 广义加性模型, /ukbsci-regression. Hard rule: local agents must not read or inspect real UKB RAP participant-level data; generate scripts for RAP execution and interpret aggregate outputs only.

Install

npx vskill@latest install hinna0818/ukbanalytica_v2/ukbsci-regression

SKILL.md

ukbsci-regression — UK Biobank batch regression

0. RAP guardrails

Strict local-agent boundary: this skill is for script generation, workflow planning, package guidance, and interpretation of aggregate outputs. The agent must not read, inspect, summarize, or process real UK Biobank RAP participant-level data, including de-identified row-level tables, raw RAP fields, exact dates, per-row predictions, row-level SHAP matrices, screenshots, tracebacks, or logs containing row-level values. Generate scripts for the user to run inside RAP; only aggregate results or rendered figures may be shared back with the agent. See ../references/agent-privacy-boundary.md.

Input: a cohort data.table produced by ukbsci-cohort (or any preprocessed UKB analysis dataset) that already lives in RAP project storage. Output: aggregate coefficient tables (estimate / CI / p-value), which may be shared with the local agent.

Forbidden: writing per-participant predicted values / residuals / influence diagnostics to local disk. Keep those in /mnt/project/... if you need them for QC.

1. When to load

Fit one or several univariate / multivariable Cox, logistic, linear, GLM,

negative-binomial, or GAM models on a UKB cohort.

Batch-run regression across many exposures at once (each gets its own model).
Count or rate outcomes with Poisson, quasi-Poisson, Gamma, or other GLM families.
Overdispersed count outcomes (negative-binomial as a principled alternative to

quasi-Poisson).

Screen for non-linear dose-response associations with GAM splines.
Fit and visualize restricted cubic spline exposure-response curves.
Check the proportional-hazards assumption for Cox models.
Run a lagged sensitivity analysis (drop events in the first N years).
Fine-Gray competing-risks modeling.
Linear-trend ("p-trend") testing across ordered exposure categories.

2. When NOT to load

Building the cohort or survival dataset → ukbsci-cohort.
Subgroup × interaction tests → ukbsci-subgroup-sensitivity.
IPTW / propensity-adjusted regression → ukbsci-propensity.
Multiple-imputation pooling → ukbsci-imputation.
KM curves / cumulative incidence / time-dependent ROC → ukbsci-survival.
Forest plots of the output → ukbsci-plot.

3. Prerequisites

library(UKBAnalytica)
library(data.table)
library(survival)         # Cox
 
stopifnot(data.table::is.data.table(cohort))
# For Cox: outcome columns from build_survival_dataset()
stopifnot(all(c("outcome_status", "outcome_surv_time") %in% colnames(cohort)))

For competing-risks models the user needs a separate competing-event indicator (e.g. non-cardiovascular death dates), or a single status column encoded as 0 = censored, 1 = primary event, 2 = competing event.

4. Pipeline (4 phases)

Phase 1 — Pick the right model family

Question	Function	Outcome
Continuous outcome	`runmulti_lm()`	numeric (e.g. SBP, BMI, biomarker)
Binary outcome at fixed time	`runmulti_logit()`	0/1
Time-to-event with censoring	`runmulti_cox()`	`outcome_status` + `outcome_surv_time`
Count / rate outcome (Poisson, Gamma…)	`runmulti_glm()`	any GLM family
Overdispersed count outcome	`runmulti_negbin()`	non-negative integer
Non-linear dose-response screening	`runmulti_gam()`	any smooth GAM family
Time-to-event + competing risk	`runmulti_competing()`	status with `{0,1,2}` coding
Cox with PH diagnostics	`runmulti_cox_zph()`	same as Cox
Cox with early-event exclusion	`runmulti_cox_lag()`	same as Cox
Ordered exposure trend	`runmulti_trend()`	depends on `model_type`
Restricted cubic spline	`run_rcs()` + `plot_rcs()`	Cox, logistic, or linear

Unified interface: run_regression(type = c("lm","logit","cox","glm","negbin","gam")) wraps all six base model families — use it when the model type is determined programmatically or you want a single consistent call site.

Phase 2 — Choose exposures & covariates

The *_multi_* family runs one model per `main_var` but shares the same covariate set. Build the covariate set from the analysis plan, e.g.

covars <- c("age", "sex", "bmi", "smoking_status",
            "Hypertension_history", "Diabetes_history")
exposures <- c("sbp", "ldl_cholesterol", "hba1c", "crp")

Phase 3 — Fit

# Linear: continuous outcome, one model per exposure
lm_res <- runmulti_lm(
  data       = cohort,
  main_var   = exposures,
  covariates = covars,
  outcome    = "carotid_imt"
)
 
# Logistic: binary outcome at baseline
logit_res <- runmulti_logit(
  data       = cohort,
  main_var   = exposures,
  covariates = covars,
  outcome    = "Hypertension_history"
)
 
# Cox: time-to-event
cox_res <- runmulti_cox(
  data       = cohort,
  main_var   = exposures,
  covariates = covars,
  endpoint   = c("outcome_surv_time", "outcome_status")
)
 
# GLM: Poisson for count / rate outcome (IRR = exp(beta))
poisson_res <- runmulti_glm(
  data       = cohort,
  main_var   = exposures,
  family     = "poisson",
  outcome    = "hospitalisation_count",
  covariates = covars
)
# exp(poisson_res$beta) gives the incidence rate ratio
# quasi-Poisson uses Wald CI automatically: family = "quasipoisson"
# Gamma with log link for positive-continuous biomarkers:
#   family = stats::Gamma(link = "log")
 
# Negative-binomial: overdispersed count outcome
negbin_res <- runmulti_negbin(
  data       = cohort,
  main_var   = exposures,
  outcome    = "hospitalisation_count",
  covariates = covars
)
# Output: IRR (= exp(beta)), lower95, upper95, pvalue, theta (dispersion), n
 
# GAM: non-linearity screening
gam_res <- runmulti_gam(
  data       = cohort,
  main_var   = exposures,
  outcome    = "carotid_imt",
  covariates = covars
)
# edf ≈ 1 → near-linear; edf > 1 → non-linear association
# smooth = FALSE for a parametric linear term inside GAM framework
 
# Save aggregate results
fwrite(cox_res,     "/mnt/project/<area>/04-results/02-cox_main.csv")
fwrite(negbin_res,  "/mnt/project/<area>/04-results/02-negbin_main.csv")

Restricted cubic spline exposure-response

Use RCS when the user asks for an interpretable nonlinear exposure-response curve, especially for continuous environmental, biomarker, or omics exposures. For quick screening across many exposures, runmulti_gam() is lighter; for a publication-ready single-exposure curve, use run_rcs() and plot_rcs().

rcs_fit <- run_rcs(
  data       = cohort,
  exposure   = "pm25",
  covariates = covars,
  model_type = "cox",
  endpoint   = c("outcome_surv_time", "outcome_status"),
  knots      = 4,
  ref_value  = median(cohort$pm25, na.rm = TRUE),
  backend    = "ns"
)
 
rcs_plot <- plot_rcs(
  rcs_fit,
  xlab = "PM2.5",
  ylab = "HR (95% CI)",
  show_p = TRUE,
  distribution = "rug"
)
ggplot2::ggsave(
  "/mnt/project/<area>/04-results/02-rcs_pm25.pdf",
  rcs_plot, width = 5.2, height = 4.2
)

Interpretation: p_overall tests whether the exposure is associated with the outcome; p_nonlinear tests whether the spline improves over a linear term. Do not export prediction grids with participant IDs; aggregate curve data and figures are safe to share.

Unified interface

# run_regression() — single call site for all 6 families
cox_res2 <- run_regression(
  data       = cohort,
  main_var   = exposures,
  type       = "cox",
  endpoint   = c("outcome_surv_time", "outcome_status"),
  covariates = covars
)
 
run_regression(data = cohort, main_var = exposures,
               type = "glm", family = "poisson",
               outcome = "hospitalisation_count", covariates = covars)
 
run_regression(data = cohort, main_var = exposures,
               type = "gam", outcome = "carotid_imt",
               smooth = TRUE, covariates = covars)

Phase 4 — Diagnostics & extensions

4a. PH diagnostics (Schoenfeld residual test)

zph <- runmulti_cox_zph(
  data       = cohort,
  main_var   = exposures,
  covariates = covars,
  endpoint   = c("outcome_surv_time", "outcome_status"),
  transform  = "km",
  alpha      = 0.05
)
zph[, .(variable, HR, lower95, upper95, pvalue,
        zph_pvalue, ph_violation)]

A TRUE in ph_violation means the exposure may have a time-varying effect; consider stratifying the offending covariate or adding a tt() term in a follow-up model.

4b. Lagged sensitivity

Drops participants whose event occurred within lag_years of baseline — guards against reverse causation.

lag_res <- runmulti_cox_lag(
  data      = cohort,
  main_var  = exposures,
  covariates = covars,
  endpoint   = c("outcome_surv_time", "outcome_status"),
  lag_years  = c(0, 1, 2, 5),
  verbose    = TRUE
)
fwrite(lag_res, "/mnt/project/<area>/04-results/04-cox_lag.csv")

The output is long: one row per (lag_years, variable, level) triple.

4c. Competing risks (Fine-Gray subdistribution hazard)

Two coding modes:

# Single-column mode: status_col has {0 = censored, 1 = primary, 2 = competing}
fg1 <- runmulti_competing(
  data           = cohort,
  main_var       = exposures,
  covariates     = covars,
  time_col       = "outcome_surv_time",
  event_col      = "outcome_status_full",
  event_value    = 1,
  compete_value  = 2
)
 
# Dual-column mode: separate columns for primary and competing events
fg2 <- runmulti_competing(
  data        = cohort,
  main_var    = exposures,
  covariates  = covars,
  time_col    = "outcome_surv_time",
  event_col   = "primary_event",
  compete_col = "competing_event"
)

Output: subdistribution hazard ratios (SHR) instead of HR.

4d. Trend across ordered exposure categories

trend_res <- runmulti_trend(
  data            = cohort,
  main_var        = c("ldl_cholesterol_q", "sbp_q"),  # quartile factors
  covariates      = covars,
  model_type      = "cox",
  endpoint        = c("outcome_surv_time", "outcome_status"),
  ref_level       = "Q1",
  score_method    = "integer",                        # 1, 2, 3, 4
  include_level_estimates = TRUE
)
trend_res[, .(variable, level, estimate, lower95, upper95,
              pvalue, p_trend, trend_estimate)]

Output: per-level estimates plus an overall p_trend per exposure.

4e. Bespoke PH diagnostics for a single fitted Cox object

fit <- survival::coxph(
  survival::Surv(outcome_surv_time, outcome_status) ~ sbp + age + sex,
  data = cohort
)
diag <- ukb_cox_diagnostics(fit, transform = "km", alpha = 0.05)
diag$table
diag$global_pvalue

5. Common pitfalls

This is the start of the file. Read the full SKILL.md on GitHub.

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Source

SKILL.md in the repositoryCheckinginst/skills/UKBAnalytica_skills/ukbsci-regression/SKILL.md

Popularity

Repository stars: 35
7-day trend score: 18

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