3: Stratified contrasts

library(ratesci)

Confidence intervals for stratified comparisons of independent binomial or Poisson rates

Stratified analysis might be required for combining results from multiple studies in a meta-analysis, or adjusting clinical trial results for factors used in a stratified randomisation. Associated tests against a null hypothesis of common effects over strata (homogeneity) can also be useful for examining subgroup effects in a clinical trial. The illustration below uses data from a meta-analysis of 9 trials studying the effectiveness of graduated compression stockings for prevention of postoperative deep vein thrombosis (DVT) (from Roderick et al. 2005):

data(compress, package = "ratesci")
strat_rd <- scoreci(x1 = compress$event.gcs, 
                   n1 = compress$n.gcs, 
                   x2 = compress$event.control, 
                   n2 = compress$n.control, 
                   contrast = "RD", 
                   stratified = TRUE)
strat_rd$estimates
#>       lower    est   upper level  p1hat p2hat  p1mle p2mle
#> [1,] -0.163 -0.124 -0.0866  0.95 0.0858  0.21 0.0936 0.218

strat_rd$pval
#>      chisq pval2sided theta0 scorenull pval_left pval_right
#> [1,]  42.2   8.25e-11      0      -6.5  4.12e-11          1

The Qtest object provides a heterogeneity test and related quantities, including an assessment of qualitative heterogeneity (indicating whether treatment effects are in opposite directions in different strata). All of which are derived using the score statistic and its estimated variance, for a consistent approach - see (Laud 2017 Supplementary Appendix S4).

strat_rd$Qtest
#>            Q         Q_df     pval_het           I2         tau2           Qc 
#>     14.41529      8.00000      0.07156     44.50337      0.00176      0.00000 
#> pval_qualhet 
#>      0.99609

It is important to note that heterogeneity tests often have low power, so it is recommended to inspect the stratum-specific estimates as well(Greenland 1982). The stratdata object provides estimates and intervals for each stratum, along with details of stratum weights and contributions to the Q-statistic (these may be further examined using the hetplot argument).

strat_rd$stratdata
#>       x1j n1j x2j n2j p1hatj p2hatj wt_fixed wtpct_fixed wtpct_rand   theta_j
#>  [1,]  15  97  37 103 0.1546 0.3592    49.95       15.62      15.62 -2.04e-01
#>  [2,]   0   8   5  10 0.0000 0.5000     4.44        1.39       1.39 -5.00e-01
#>  [3,]  11  50  23  48 0.2200 0.4792    24.49        7.66       7.66 -2.59e-01
#>  [4,]   4 110  16 110 0.0364 0.1455    55.00       17.19      17.19 -1.10e-01
#>  [5,]   7  65   7  32 0.1077 0.2188    21.44        6.70       6.70 -1.13e-01
#>  [6,]   8  25   8  25 0.3200 0.3200    12.50        3.91       3.91 -2.98e-08
#>  [7,]   5 126  17 126 0.0397 0.1349    63.00       19.69      19.69 -9.57e-02
#>  [8,]   0 104   4  92 0.0000 0.0435    48.82       15.26      15.26 -4.51e-02
#>  [9,]   7  80  16  81 0.0875 0.1975    40.25       12.58      12.58 -1.10e-01
#>       lower_j  upper_j     V_j Stheta_j    Q_j
#>  [1,]  -0.321 -0.08495 0.00369  -0.0804 1.7506
#>  [2,]  -0.787 -0.10319 0.04111  -0.3758 3.4351
#>  [3,]  -0.435 -0.07103 0.00914  -0.1350 1.9917
#>  [4,]  -0.190 -0.03530 0.00151   0.0151 0.1517
#>  [5,]  -0.290  0.04065 0.00704   0.0132 0.0246
#>  [6,]  -0.260  0.25979 0.01763   0.1242 0.8753
#>  [7,]  -0.169 -0.02700 0.00132   0.0290 0.6349
#>  [8,]  -0.101 -0.00470 0.00119   0.0807 5.4843
#>  [9,]  -0.221 -0.00172 0.00300   0.0142 0.0671

Random effects

In this instance, there is weak evidence ($p = 0.07$) that the treatment effect (on the RD scale) varies across strata. (Estimation on the RR contrast scale gives more consistency across strata.) For the sake of illustration, a random effects analysis of RD would be obtained as follows, giving a wider interval that incorporates the stratum variability. The random = TRUE option invokes the t-distribution asymptotic score (TDAS) method (Laud 2017), which modifies the asymptotic score methodology using formulae from the Hartung-Knapp-Sidik-Jonkman (HKSJ) random effects meta-analysis method (with superior performance compared to the DerSimonian-Laird method).

strat_rd_rand <- scoreci(x1 = compress$event.gcs, 
                   n1 = compress$n.gcs, 
                   x2 = compress$event.control, 
                   n2 = compress$n.control, 
                   contrast = "RD", 
                   stratified = TRUE,
                   random = TRUE,
                   prediction = TRUE)
strat_rd_rand$estimates
#>       lower    est upper level  p1hat p2hat  p1mle p2mle
#> [1,] -0.188 -0.124 -0.06  0.95 0.0858  0.21 0.0936 0.218

Note that the TDAS method does not include a skewness correction, so when random = TRUE, the skew argument only affects the stratdata output element.

A prediction interval for the treatment effect in a new study (Higgins, Thompson, and Spiegelhalter 2008) can be obtained using prediction = TRUE:

strat_rd_rand$prediction
#>       lower  upper
#> [1,] -0.244 0.0158

References

Greenland, Sander. 1982. “Interpretation and Estimation of Summary Ratios Under Heterogeneity.” Statistics in Medicine 1 (3): 217–27. https://doi.org/10.1002/sim.4780010304.

Higgins, Julian P. T., Simon G. Thompson, and David J. Spiegelhalter. 2008. “A Re-Evaluation of Random-Effects Meta-Analysis.” Journal of the Royal Statistical Society Series A: Statistics in Society 172 (1): 137–59. https://doi.org/10.1111/j.1467-985x.2008.00552.x.

Laud, Peter J. 2017. “Equal-Tailed Confidence Intervals for Comparison of Rates.” Pharmaceutical Statistics 16 (5): 334–48. https://doi.org/10.1002/pst.1813.

Roderick, P, G Ferris, K Wilson, H Halls, D Jackson, R Collins, and C Baigent. 2005. “Towards Evidence-Based Guidelines for the Prevention of Venous Thromboembolism: Systematic Reviews of Mechanical Methods, Oral Anticoagulation, Dextran and Regional Anaesthesia as Thromboprophylaxis.” Health Technology Assessment 9 (49). https://doi.org/10.3310/hta9490.