hinna0818/ukbanalytica_v2/ukbsci-mediation

ukbsci-mediation — Causal mediation on UKB cohorts

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.

Inputs: cohort data.table with exposure, mediator(s), outcome, covariates. Outputs: aggregate effect tables + figures that may be shared with the local agent. Bootstrap CI sampling stays in RAP memory; the resulting estimates and CIs are aggregate.

1. When to load

• The user has an exposure → mediator → outcome triple and wants to

decompose total effect into direct + indirect components.

• Screening multiple candidate mediators for the strongest indirect effect.
• Reporting CDE / PNDE / TNIE / TNDE / PNIE / TE / proportion-mediated.

2. When NOT to load

• Simple confounder adjustment (no mediator) → ukbsci-regression.
• Effect-modification / subgroup tests → ukbsci-subgroup-sensitivity.
• IPTW / treatment-effect estimation → ukbsci-propensity.

3. Prerequisites

library(UKBAnalytica)
library(regmedint)         # Required — implements 4-way decomposition
library(data.table)

If regmedint is missing, run_mediation() errors out with a clear message.

4. Pipeline

Phase 1 — Specify the triple + model types

exposure   <- "sbp_above_median"          # binary or continuous
mediator   <- "ldl_cholesterol"           # continuous
outcome    <- "outcome_status"            # binary for logistic, ignored for Cox time arg
covars     <- c("age", "sex", "bmi", "smoking_status")
 
mediator_type <- "continuous"             # "continuous" or "binary"
outcome_type  <- "cox"                    # "linear", "logistic", "cox"

Decision matrix:

Phase 2 — Single mediator: run_mediation()

res <- run_mediation(
  data             = cohort,
  exposure         = exposure,
  mediator         = mediator,
  outcome          = "outcome_surv_time",            # time variable for Cox
  covariates       = covars,
  exposure_levels  = c(0, 1),
  mediator_value   = 0,                              # CDE evaluated at m = 0
  covariate_values = NULL,                           # NULL = covariate means
  mediator_type    = "continuous",
  outcome_type     = "cox",
  endpoint         = c("outcome_surv_time", "outcome_status"),
  interaction      = TRUE,                           # include E×M
  boot             = FALSE,
  boot_n           = 1000,
  conf_level       = 0.95
)
 
class(res)
# "mediation_result"
 
res$effects      # data.frame: estimate, SE, CI, p-value for CDE, PNDE, TNIE, TNDE, PNIE, TE, PM
summary(res)     # S3 summary with optional `exponentiate = TRUE`
coef(res)        # named vector of point estimates
confint(res)     # matrix [lower, upper]

Phase 3 — Multi-mediator screening

candidate_mediators <- c("ldl_cholesterol", "hba1c", "crp", "bmi")
 
multi <- run_multi_mediator(
  data           = cohort,
  exposure       = exposure,
  mediators      = candidate_mediators,
  outcome        = "outcome_surv_time",
  covariates     = covars,
  mediator_type  = "continuous",
  outcome_type   = "cox",
  endpoint       = c("outcome_surv_time", "outcome_status")
)
# data.frame: one row per mediator with TNIE/PNDE/TE/PM + SE/CI/p

Phase 4 — Visualize

p_bar  <- plot_mediation(res, type = "effects",
                         show_ci = TRUE, show_pvalue = TRUE,
                         exponentiate = FALSE,
                         title = "Mediation of SBP→AA via LDL")
p_path <- plot_mediation(res, type = "path")
p_dec  <- plot_mediation(res, type = "decomposition")
 
p_forest <- plot_mediation_forest(
  multi,
  effect_type = "tnie",
  exponentiate = FALSE,
  null_value   = 0
)
ggplot2::ggsave("/mnt/project/<area>/05-figs/Fig09-mediation_forest.pdf",
                p_forest, width = 8, height = 5)

5. Key assumptions encoded

regmedint (and therefore this skill) assumes:

1. No unmeasured exposure-outcome confounders given covariates.
2. No unmeasured mediator-outcome confounders given exposure + covariates.
3. No unmeasured exposure-mediator confounders given covariates.
4. No mediator-outcome confounder affected by exposure (cross-world

assumption for natural effects).

1. Correct functional form of mediator and outcome models.

Setting interaction = TRUE allows for an E × M interaction in the outcome model (recommended; the default).

6. Common pitfalls

1. `regmedint` not installed. Triggers a stop with explicit message —

install before continuing.

1. Survival outcomes require outcome_type = "cox" and endpoint.

Passing only outcome is for linear / logistic.

1. Binary mediator + Cox outcome. Supported but doubles model

complexity; expect convergence warnings on small subgroups.

1. Bootstrap is slow. boot = TRUE, boot_n = 1000 can take many

minutes on 200k+ cohorts. Start with boot = FALSE (delta-method CIs) and only bootstrap a confirmatory run.

1. No multiple-comparisons correction in `run_multi_mediator()`.

When screening ≥ 5 mediators, apply Benjamini-Hochberg externally before highlighting "significant" indirect effects.

1. Sensitivity analysis is not implemented. Use a method appropriate to

the fitted mediation model and report its assumptions explicitly.

1. Exponentiated estimates apply naturally to logistic / Cox outcomes

(OR / HR scale). For linear outcomes, leave exponentiate = FALSE.

7. Key functions

8. Related skills

9. References

• `references/functions.md`
• `references/examples.md`