Table 2.

Geometric mean values of hazard ratios associated with predictors of initial SSTI over 100 simulations in univariate and multivariate Cox regression models without interaction terms

Factor	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11
CD4 count <200 cells/ml	3·02	3·15				2·51	2·92	4·44	3·67
HIV viral load <1000 copies/ml	0·51		0·35			0·45	0·45			0·34	0·41
TMP-SMX prescription					2·57		0·84		0·84		2·03
TMP-SMX use	0·69			1·39				0·47		1·57

TMP-SMX, Trimethoprim-sulfamethoxazole; SSTI, skin and soft tissue infection.

Results of various Cox regression modelling strategies applied to our simulated data set. Model 1, in which we know and adjust for whether TMP-SMX is actually used as well as CD4 count and HIV viral load, is unbiased and thus us the standard to which subsequent models can be compared for evidence of bias. Model 2, in which only CD4 count is included in the model, yields a slightly upward biased estimate for the effect of CD4 count. Model 3, in which we only account for HIV viral load, overestimates the effect of virological suppression on SSTI risk. Models 4 and 5, in which we only account for TMP-SMX prescription and actual receipt without accounting for CD4 and HIV viral load, are highly biased and suggest TMP-SMX increases the risk of SSTI. Model 6, in which we account for CD4 count and HIV viral load but not TMP-SMX use, underestimates the immunological effect of low CD4 count on SSTI risk. Model 7, where we do not have knowledge of TMP-SMX adherence, underestimates the protective effect of TMP-SMX and yields slightly biased estimates for the effect of CD4 and HIV viral load as well. Models 8 and 9, where we only account for CD4 count and TMP-SMX use, overestimate the effect of CD4 count. Models 10 and 11, where we account for HIV viral load and TMP-SMX prophylaxis but not CD4 count, erroneously suggest that TMP-SMX use increases SSTI risk.