Mantel-Haenszel Test and Odds Ratio Meta-analysis

This R code reproduces the example above. It needs no packages and was checked with R 4.6.1. Paste it into R, or save it as a script and run it.

# Odds ratio meta-analysis (Mantel-Haenszel and exact): the StatsDirect help example
# (Armitage and Berry 1994, smoking among lung cancer patients and controls in ten
# case-control studies; the test workbook's Meta-analysis worksheet columns Smokers
# total, Smokers cancer, Control total and Control cancer) in R
smokers <- c(155, 317, 210, 711, 2646, 166, 993, 961, 2180, 519)
smokers_cancer <- c(83, 90, 129, 412, 1350, 60, 459, 499, 451, 260)
nonsmokers <- c(17, 46, 26, 163, 68, 30, 99, 75, 675, 33)
nonsmokers_cancer <- c(3, 3, 7, 32, 7, 3, 18, 19, 39, 5)
k <- length(smokers)

# Each study's fourfold table: a and b are the smokers and non-smokers with lung
# cancer, c and d the smokers and non-smokers among the controls (the workbook's
# "Control" columns are the non-smokers), so the odds ratio a d / (b c) is the odds
# of smoking among the patients relative to the controls
a <- smokers_cancer
b <- nonsmokers_cancer
c <- smokers - a
d <- nonsmokers - b
n <- a + b + c + d
tables <- array(rbind(a, b, c, d), dim = c(2, 2, k),
                dimnames = list(smoking = c("smoker", "non-smoker"),
                                group = c("lung cancer", "control"), study = 1:k))

# R's standard test: the Mantel-Haenszel estimate of the common odds ratio with a
# confidence interval that equals the report's Robins, Breslow and Greenland
# interval, and the Mantel-Haenszel chi-square with the continuity correction
# (correct = TRUE is the default)
r <- mantelhaen.test(tables)
print(r)

# The exact conditional test gives the conditional maximum likelihood estimate and
# the exact (Fisher) interval; R finds them numerically and they agree with the
# report to four significant figures. The code below finds them to a tighter
# tolerance, together with the mid-P interval and P values that R does not print
e <- mantelhaen.test(tables, exact = TRUE)
print(e)

# The report's lines to 6 places
six <- function(x) formatC(x, digits = 6, format = "f", drop0trailing = TRUE)
pv <- function(p) {
  if (p < 0.0001) "P < 0.0001" else
    paste("P =", formatC(p, digits = 4, format = "f", drop0trailing = TRUE))
}
z <- qnorm(0.975)

# The exact methods condition on every table's margins: given them, the total of
# the a cells has a distribution proportional to a product of hypergeometric terms
# times psi^a, where psi is the common odds ratio. The function returns the log of
# each term at psi = 1 over the support of the total (the terms are convolved on the
# natural scale, each table's rescaled to a maximum of 1 to keep within range)
conditional <- function(a, b, c, d) {
  exposed <- a + c
  unexposed <- b + d
  cases <- a + b
  coef <- 1
  start <- 0
  for (i in seq_along(a)) {
    x <- max(0, cases[i] - unexposed[i]):min(exposed[i], cases[i])
    p <- dhyper(x, exposed[i], unexposed[i], cases[i])
    p <- p / max(p)
    new <- numeric(length(coef) + length(p) - 1)
    for (j in seq_along(p)) {
      at <- j:(j + length(coef) - 1)
      new[at] <- new[at] + coef * p[j]
    }
    coef <- new / max(new)
    start <- start + x[1]
  }
  list(support = start + seq_along(coef) - 1, logc = log(coef))
}
# The probability of the part of the support that 'which' selects, at odds ratio psi
cond_p <- function(dist, psi, which) {
  lw <- dist$logc + dist$support * log(psi)
  w <- exp(lw - max(lw))
  sum(w[which]) / sum(w)
}
# Solve for psi on the log scale: the conditional mean equal to the observed total
# (the conditional maximum likelihood estimate), or a tail probability equal to
# alpha / 2 (an exact limit); 'half' counts half of the observed total's probability
# for the mid-P limits
cond_solve <- function(dist, target, s, side = "mean", half = FALSE) {
  f <- function(t) {
    if (side == "mean") {
      lw <- dist$logc + dist$support * t
      w <- exp(lw - max(lw))
      return(sum(dist$support * w) / sum(w) - s)
    }
    p <- if (side == "upper") cond_p(dist, exp(t), dist$support > s) else
      cond_p(dist, exp(t), dist$support < s)
    if (half) p + 0.5 * cond_p(dist, exp(t), dist$support == s) - target else
      p + cond_p(dist, exp(t), dist$support == s) - target
  }
  exp(uniroot(f, c(-30, 30), tol = 1e-10)$root)
}
exact_limits <- function(dist, s, level = 0.95, half = FALSE) {
  alpha <- (1 - level) / 2
  if (s == min(dist$support)) lower <- 0 else
    lower <- cond_solve(dist, alpha, s, "upper", half)
  if (s == max(dist$support)) upper <- Inf else
    upper <- cond_solve(dist, alpha, s, "lower", half)
  c(lower, upper)
}

# Each study's odds ratio with its exact (conditional maximum likelihood) limits,
# as fisher.test would give them, but to a tight tolerance. The report gives the odds
# ratio of a table with an empty cell after a continuity correction, and the exact
# limits of the table as it is; no table has an empty cell here
or <- a * d / (b * c)
lci <- numeric(k)
uci <- numeric(k)
for (i in 1:k) {
  lim <- exact_limits(conditional(a[i], b[i], c[i], d[i]), a[i])
  lci[i] <- lim[1]
  uci[i] <- lim[2]
}
cat("Stratum, odds ratio, 95% CI (CML)\n")
for (i in 1:k) {
  cat(i, six(or[i]), six(lci[i]), six(uci[i]), "\n")
}

# Mantel-Haenszel: the pooled odds ratio is sum(a d / n) / sum(b c / n), the
# fixed effects weights are b c / n, and the Robins-Breslow-Greenland variance of
# its log gives the interval, as mantelhaen.test printed
R <- a * d / n
S <- b * c / n
P <- (a + d) / n
Q <- (b + c) / n
or_mh <- sum(R) / sum(S)
v_rbg <- sum(P * R) / (2 * sum(R)^2) + sum(Q * R + P * S) / (2 * sum(R) * sum(S)) +
  sum(Q * S) / (2 * sum(S)^2)
wt_fixed <- 100 * S / sum(S)

# Heterogeneity: Breslow-Day compares each a cell with its expectation under the
# pooled odds ratio (the root of a quadratic), and Cochran's Q is the weighted sum
# of squared deviations of the log odds ratios from the log of the pooled odds
# ratio, weighted by the inverse of each study's logit (Woolf) variance
cases <- a + b
controls <- c + d
exposed <- a + c
unexposed <- b + d
qa <- 1 - or_mh
qb <- unexposed - cases + (exposed + cases) * or_mh
qc <- -cases * exposed * or_mh
ea <- (-qb + sqrt(qb^2 - 4 * qa * qc)) / (2 * qa)
v_ea <- 1 / (1 / ea + 1 / (cases - ea) + 1 / (exposed - ea) +
               1 / (controls - exposed + ea))
bd <- sum((a - ea)^2 / v_ea)
yi <- log(or)
v_woolf <- 1 / a + 1 / b + 1 / c + 1 / d
w <- 1 / v_woolf
q <- sum(w * (yi - log(or_mh))^2)

# DerSimonian-Laird: the moment estimate of the between studies variance (not less
# than zero), the random effects weights and the pooled odds ratio
tau2 <- max(0, (q - (k - 1)) * sum(w) / (sum(w)^2 - sum(w^2)))
w_re <- 1 / (tau2 + v_woolf)
wt_random <- 100 * w_re / sum(w_re)
or_re <- exp(sum(w_re * yi) / sum(w_re))
x2_re <- sum(w_re * yi)^2 / sum(w_re)

# The report's second table: the standardised effect is the log odds ratio, with
# its logit (Woolf) variance, and the weights are percentages
vi <- v_woolf
cat("Stratum, standardized effect, variance, % weights (fixed, random)\n")
for (i in 1:k) {
  cat(i, six(yi[i]), six(vi[i]), six(wt_fixed[i]), six(wt_random[i]), "\n")
}

mh_ci <- exp(log(or_mh) + c(-1, 1) * z * sqrt(v_rbg))
cat("Fixed effects (Mantel-Haenszel, Robins-Breslow-Greenland)\n")
cat("Pooled odds ratio = ", six(or_mh), " (95% CI = ", six(mh_ci[1]), " to ",
    six(mh_ci[2]), ")\n", sep = "")
x2 <- as.numeric(r$statistic)
cat("Chi2 (test odds ratio differs from 1) =", six(x2), " ", pv(r$p.value), "\n")

# The pooled exact analysis: the conditional maximum likelihood estimate, the exact
# Fisher and mid-P limits, and the P values at odds ratio 1 (the Fisher two sided P
# sums the probabilities of totals no more probable than the one observed)
all <- conditional(a, b, c, d)
s <- sum(a)
cml <- cond_solve(all, 0, s)
fisher <- exact_limits(all, s)
midp <- exact_limits(all, s, half = TRUE)
p_upper <- cond_p(all, 1, all$support >= s)
p_lower <- cond_p(all, 1, all$support <= s)
p_at_s <- cond_p(all, 1, all$support == s)
w1 <- exp(all$logc - max(all$logc))
p2_fisher <- sum(w1[w1 <= w1[all$support == s]]) / sum(w1)
p1_mid <- min(p_upper - p_at_s / 2, 1 - (p_upper - p_at_s / 2))
cat("Fixed effects (conditional maximum likelihood)\n")
cat("Pooled odds ratio =", six(cml), "\n")
cat("Exact Fisher 95% CI =", six(fisher[1]), "to", six(fisher[2]), "\n")
cat("Exact Fisher one sided ", pv(min(p_upper, p_lower)), ", two sided ",
    pv(p2_fisher), "\n", sep = "")
cat("Exact mid-P 95% CI =", six(midp[1]), "to", six(midp[2]), "\n")
cat("Exact mid-P one sided ", pv(p1_mid), ", two sided ", pv(min(1, 2 * p1_mid)),
    "\n", sep = "")

# I-squared is 100 (Q - df) / Q, not below zero. Its interval is the non-central
# chi-square method of Hedges and Pigott (2001) that the report uses: Q is treated
# as a non-central chi-square variable on df degrees of freedom, and each limit is
# the non-centrality parameter at which the observed Q sits at the 97.5th (lower
# limit) or the 2.5th (upper limit) percentile, found with uniroot and pchisq; a
# limit is 0 when the central distribution already puts Q below that percentile.
# A non-centrality lambda gives I-squared = lambda / (df + lambda)
df <- k - 1
isq <- max(0, 100 * (q - df) / q)
ncp_limit <- function(p) {
  if (pchisq(q, df) <= p) return(0)
  # the bracket Q + 1000 is ample: the distribution function there is near 0
  uniroot(function(l) pchisq(q, df, ncp = l) - p, c(0, q + 1000), tol = 1e-10)$root
}
lambda <- c(ncp_limit(0.975), ncp_limit(0.025))
i2_ci <- 100 * lambda / (df + lambda)
one <- function(x) formatC(x, digits = 1, format = "f", drop0trailing = TRUE)
cat("Non-combinability of studies\n")
cat("Breslow-Day = ", six(bd), "  (df = ", df, ")  ",
    pv(pchisq(bd, df, lower.tail = FALSE)), "\n", sep = "")
cat("Cochran Q = ", six(q), "  (df = ", df, ")  ",
    pv(pchisq(q, df, lower.tail = FALSE)), "\n", sep = "")
cat("Moment-based estimate of between studies variance =", six(tau2), "\n")
cat("I2 (inconsistency) = ", one(isq), "% (95% CI = ", one(i2_ci[1]), "% to ",
    one(i2_ci[2]), "%)\n", sep = "")

re_ci <- exp(log(or_re) + c(-1, 1) * z / sqrt(sum(w_re)))
cat("Random effects (DerSimonian-Laird)\n")
cat("Pooled odds ratio = ", six(or_re), " (95% CI = ", six(re_ci[1]), " to ",
    six(re_ci[2]), ")\n", sep = "")
cat("Chi2 (test odds ratio differs from 1) =", six(x2_re), " (df = 1) ",
    pv(pchisq(x2_re, 1, lower.tail = FALSE)), "\n")

# Bias indicators, from the log odds ratios and their logit standard errors.
# Begg-Mazumdar: Kendall's tau between the deviations of the log odds ratios from
# their weighted mean, standardised, and the variances; cor.test gives the exact
# two sided P because there are fewer than 50 studies and no ties. With tied
# variances the report uses tau-b and a continuity-corrected normal approximation,
# cor.test(..., exact = FALSE, continuity = TRUE). The report adds "(low power)"
# with fewer than eleven studies: the rank correlation test then has little power
se <- sqrt(v_woolf)
dev <- (yi - sum(w * yi) / sum(w)) / sqrt(v_woolf - 1 / sum(w))
begg <- cor.test(dev, v_woolf, method = "kendall")
cat("Bias indicators\n")
cat("Begg-Mazumdar: Kendall's tau =", six(begg$estimate), " ", pv(begg$p.value),
    "(low power)\n")
# Egger: the standardised effect regressed on precision, the intercept being the
# bias; the limits are at 90% (twice the usual alpha) because the test has low power
egger <- lm(I(yi / se) ~ I(1 / se))
ci <- confint(egger, level = 0.9)
cat("Egger: bias = ", six(coef(egger)[1]), " (90% CI = ", six(ci[1, 1]),
    " to ", six(ci[1, 2]), ")  ", pv(summary(egger)$coefficients[1, 4]), "\n", sep = "")
# Harbord-Egger: the same regression of each study's efficient score (a minus its
# expectation) divided by the root of its hypergeometric variance, on that root
score <- a - cases * exposed / n
v_score <- cases * controls * exposed * unexposed / (n^2 * (n - 1))
harbord <- lm(I(score / sqrt(v_score)) ~ sqrt(v_score))
ci <- confint(harbord, level = 0.9)
cat("Harbord-Egger: bias = ", six(coef(harbord)[1]), " (90% CI = ",
    six(ci[1, 1]), " to ", six(ci[1, 2]), ")  ",
    pv(summary(harbord)$coefficients[1, 4]), "\n", sep = "")

# The report's charts. First the bias assessment (funnel) plot: each log odds ratio
# against its logit standard error, the axis reversed so that the 95% cone about the
# pooled Mantel-Haenszel log odds ratio opens downwards
se_top <- max(se) * 1.05
plot(yi, se, ylim = c(se_top, 0), xlab = "Log(Odds ratio)", ylab = "Standard error",
     main = "Bias assessment plot",
     xlim = range(yi, log(or_mh) + c(-1, 1) * z * se_top))
abline(v = log(or_mh))
lines(log(or_mh) + c(-1, 0, 1) * z * se_top, c(se_top, 0, se_top))

# The L'Abbe plot: the percentage of each study's smokers who were lung cancer
# patients against the same percentage of its non-smokers, the symbol size growing
# with the study size, with the line of equality. The report adds a dashed line from
# the origin with the slope of the pooled odds ratio, which is the line of a risk
# ratio; the curve of the studies that share the pooled odds ratio is drawn here
p0 <- 100 * b / (b + d)
p1 <- 100 * a / (a + c)
plot(p0, p1, xlim = c(0, 100), ylim = c(0, 100), cex = 0.5 + 2 * sqrt(n / max(n)),
     xlab = "control percent", ylab = "experimental percent",
     main = "L'Abbe plot (symbol size represents sample size)")
abline(0, 1)
x0 <- seq(0, 1, 0.01)
lines(100 * x0, 100 * or_mh * x0 / (1 + (or_mh - 1) * x0), lty = 2)

# The two forest plots: each study's odds ratio (the symbol area proportional to
# its weight) with its exact interval on a log scale, the line of no effect at 1,
# and the pooled estimate as a diamond at the foot with a dashed line up from it
forest <- function(pooled, lower, upper, weights, main, label) {
  y <- (k + 1):2
  two <- function(x) formatC(x, digits = 2, format = "f")
  par(mar = c(5, 8, 4, 9))
  plot(NA, xlim = range(1, lci, uci, lower, upper), ylim = c(0.5, k + 1.5),
       log = "x", yaxt = "n", xlab = "odds ratio (95% confidence interval)",
       ylab = "", main = main)
  abline(v = 1)
  segments(pooled, 1, pooled, k + 1, lty = 2)
  segments(lci, y, uci, y)
  points(or, y, pch = 15, cex = 0.5 + 2 * sqrt(weights / max(weights)))
  polygon(c(lower, pooled, upper, pooled), c(1, 1.3, 1, 0.7), col = "grey")
  axis(2, at = c(y, 1), labels = c(paste("stratum", 1:k), label), las = 1,
       tick = FALSE)
  axis(4, at = c(y, 1), tick = FALSE, las = 1,
       labels = paste0(two(c(or, pooled)), " (", two(c(lci, lower)), ", ",
                       two(c(uci, upper)), ")"))
}
forest(or_mh, mh_ci[1], mh_ci[2], wt_fixed,
       "Odds ratio meta-analysis plot [fixed effects]", "combined [fixed]")
forest(or_re, re_ci[1], re_ci[2], wt_random,
       "Odds ratio meta-analysis plot [random effects]", "combined [random]")
par(mar = c(5, 4, 4, 2) + 0.1)