Kyle, Ryan P.

Objectives To understand the trade-offs between different statistical modeling approaches, using real world data with small sub-populations, with rare exposures and increasingly rare outcomes. In particular, to compare adjusted regression, inverse probability weighting, and matching. Methods Data for these analyses came from the RADAR (N=1,134) and combined CNICS/JHHCC (N=14,434) cohorts. We estimated prevalence ratios (PRs) for self-reported use of specific substances comparing subpopulations (SP), SP-1 vs SP-3 and SP-2 vs SP-3, where SP-1 was the largest proportion (92% in RADAR, 81% in CNICS/JHHCC), SP2 was moderate proportion (18% in CNICS/JHHCC) and SP-3 was the smallest proportion (8% in RADAR, 1% in CNICS/JHHCC) of the population. We calculated PRs using 1) unadjusted relative risk regression (RR); and adjusted estimates controlling for age, race/ethnicity, study site, and year of interview using: 2) standard adjustment in RR; 3) stabilized inverse probability of treatment weighting (IPTW); and 4) matching with up to 3 matches from SP-1 or SP-2 per SP-3 participant. Results For most substances, all methods yielded consistent estimates. There were large weights in some of the IPTW analyses and in three cases these resulted in substantially divergent estimates. For the comparison between SP-1 and SP-3, the estimate for smoking was 1.3-fold greater in the matched analysis (PR=1.33, 95% CI: 1.02-1.75) than in IPTW (PR=1.03, 95% CI: 0.78-1.37 ATE and PR=1.05, 95% CI: 0.88-1.26 ATT). Even more extreme divergence in estimates was observed for differences between SP-2 and SP-3 with respect to methamphetamine/amphetamine, (IPTW-ATE: PR=1.51, 95% CI: 0.79-2.89; IPTW-ATT: PR=2.36, 95% CI: 1.36-4.12); vs Matching: PR=2.71, 95%CI: 1.47-4.99) and cocaine (IPTW-ATE: PR=1.15, 95% CI: 0.55-2.38; IPTW-ATT: PR=1.81, 95% CI: 1.03-3.18); vs Matching: PR=1.38, 95%CI: 0.76-2.53). Conclusion The combination of a rare exposure and a rare outcome can produce challenges for commonly used confounding adjustment strategies, and it is often best to compare different modeling approaches to gain greater insight.

Background: Sodium–glucose cotransporter 2 inhibitors (SGLT2i) are nephroprotective but are associated with early eGFR decline, also termed “eGFR dip.” Despite elevated risk of kidney disease, this phenomenon is understudied among people with HIV (PWH). Methods: In a 1:1 propensity score–matched cohort study using data from 9 Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) sites, we compared new SGLT2i users with new users of other antihyperglycemic classes. We estimated adjusted hazard ratios (aHRs) for time to ≥10% and ≥30% eGFR decline using multivariable Cox proportional hazards models and analyzed eGFR change using multivariable linear mixed models. In addition, longer-term eGFR trends over 24 months were visualized using locally weighted scatterplot smoothing (LOWESS) curves. Results: Among 1554 eligible PWH, we obtained 295 matched pairs. Over 6 months, eGFR decline incidence of ≥10% and ≥30% was higher among users of SGLT2i versus other antihyperglycemic classes (58.2% vs. 37.4%; aHR: 1.79; 95% CI: 1.40%–2.28%; and 17.3% vs. 9.8%; aHR: 1.69; 95% CI: 1.05–2.73; respectively). The adjusted mean eGFR change at 6 months was −2.62 mL/min/1.73 m 2 for SGLT2i versus 0.05 mL/min/1.73 m 2 for other classes. Long-term eGFR trends revealed an expected initial decline after SGLT2i initiation, followed by stabilization and a slower subsequent decline compared with other classes. Conclusions: Acute eGFR dips were more common among PWH initiating SGLT2i relative to other antihyperglycemic classes, although overall declines were small, transient, and consistent with the general population. Further research is needed to explore the long-term effects of SGLT2i in PWH.