Targeted Estimation of Marginal Absolute and Relative Associations in Case–Control Data

Pearl, Michelle; Balzer, Laura B.; Ahern, Jennifer

doi:10.1097/ede.0000000000000476

Cited by 6 publications

(5 citation statements)

References 35 publications

(36 reference statements)

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“…Case–control-weighted G-computation corrects for the case–control sampling by reweighting: cases are weighted according to the population prevalence of the outcome, and controls are weighted with (1-prevalence)/(control:case ratio). 32 , 33 The case–control-weighted G-computation formula is as follows:…”

Section: Methodsmentioning

confidence: 99%

Community-Level Risk Factors for Firearm Assault and Homicide: The Role of Local Firearm Dealers and Alcohol Outlets

Pear,

Wintemute,

Jewell

et al. 2023

Epidemiology

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Background: Identifying community characteristics associated with firearm assault could facilitate prevention. We investigated the effect of community firearm dealer and alcohol outlet densities on individual risk of firearm assault injury. Methods: In this density-sampled case–control study of Californians, January 2005 - September 2015, cases comprised all residents with a fatal or nonfatal firearm assault injury. We sampled controls monthly from the state population, 4:1 with cases. Exposures were monthly densities of county-level pawn and non-pawn firearm dealers and ZIP code-level off-premises alcohol outlets and bars and pubs (“bars/pubs”). We used case–control-weighted g-computation to estimate risk differences (RD) statewide and among younger Black men, comparing observed exposure densities to hypothetical interventions setting these densities to low. We estimated additive interactions between firearm and alcohol retailer density. Secondary analyses examined interventions targeted to high exposure density or outcome burden areas. Results: There were 67,850 cases and 268,122 controls. Observed (vs. low) densities of pawn firearm dealers and off-premises alcohol outlets were individually associated with elevated monthly risk of firearm assault per 100,000 people (RDpawn dealers: 0.06, 95% CI: 0.05, 0.08; RDoff-premises outlets: 0.01, 95% CI: 0.01, 0.03), but non-pawn firearm dealer and bar/pub density were not; models targeting only areas with the highest outcome burden were similar. Among younger Black men, estimates were larger. There was no interaction between firearm and alcohol retailer density. Conclusions: Our results are consistent with the hypothesis that limiting pawn firearm dealer and off-premises alcohol outlet densities can reduce interpersonal firearm violence.

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Section: Methodsmentioning

confidence: 99%

Community-Level Risk Factors for Firearm Assault and Homicide: The Role of Local Firearm Dealers and Alcohol Outlets

Pear,

Wintemute,

Jewell

et al. 2023

Epidemiology

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“…In this context, Targeted Maximum Likelihood Estimation (TMLE) is one estimation option. Since the mid 2000's TMLE has been implemented successfully in numerous applications, initially for the effects of treatments or exposures at a single time point [1,2,3,4,5]. In recent years, an increasing body of work has dealt with the application of TMLE in longitudinal settings [6,7,8,9,10,11,12,13,14,15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions

Schomaker

Luque-Fernandez

Leroy

et al. 2019

Statistics in Medicine

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Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, ie, it is doubly robust, and can yield valid inference when used in conjunction with machine learning. It also has the advantage of easy-to-calculate analytic standard errors in contrast to the g-formula, which requires bootstrapping. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings, and we compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies: (1) long follow-up time (30 months); (2) gradually declining sample size; (3) limited support for some intervention rules of interest; (4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods; and (5) consideration of collider bias. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: We show that (1) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (2) machine learning utilized with a small set of simple learners (if more complex ones cannot be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; and (3) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis. We provide guidance for the practical application of LTMLE.

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“…The utilized HIPSS study also collected data on other STIs like Chlamydia trachomatis, Neisseria gonorrhoea, Mycoplasma genitalium, Trichomonas vaginalis, syphilis, hepatitis B, and human papillomavirus (HPV) infection. However, we selected HIV and HSV-2 because of their relatively high prevalence compared to other STIs, and the application of TMLE for rare outcomes is still in its infancy [20]. Moreover, previous reports [1], have shown that the associations of MMC with STIs other than HIV and HSV-2 were unreliable as the study was underpowered to detect rare STI outcomes.…”

Section: Discussionmentioning

confidence: 99%

Association of Medical Male Circumcision and Sexually Transmitted Infections in a Population-Based Study: A Targeted Maximum Likelihood Estimation Approach

Amusa

Zewotir

North

et al. 2021

Preprint

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Background: Epidemiological theory and many empirical studies support the hypothesis that there is a protective effect of male circumcision against some sexually transmitted infections (STIs). However, there is a paucity of randomized control trials (RCTs) to test this hypothesis in the South African population. Due to the infeasibility of conducting RCTs, estimating marginal or average treatment effects with observational data, are of increasing interest. Using targeted maximum likelihood estimation (TMLE), a doubly robust estimation technique, we aim to provide evidence of association between medical male circumcision (MMC) and two STI outcomes.Methods: We investigated the associations between MMC and the two STI outcomes, HIV and HSV-2, using data from the HIV Incidence Provincial Surveillance System (HIPSS) study in KwaZulu-Natal, South Africa. We estimated marginal odds ratios using TMLE and compared estimates with those from propensity score full matching and inverse probability of treatment weighting (IPTW). Results: TMLE estimates suggest that MMC was associated with 46.9% lower odds of HIV (OR: 0.531; 95% CI: 0.455, 0.621) and 20.5% for HSV-2 (OR: 0.795; 95% CI: 0.694, 0.911). The propensity score analyses also provided evidence of association of MMC with lower odds of HIV and HSV-2. For full matching: HIV (OR: 0.546; 95% CI: 0.402, 0.741), and HSV-2 (OR: 0.705; 95% CI: 0.545, 0.910). For IPTW: HIV (OR: 0.541; 95% CI: 0.405, 0.722), and HSV-2 (OR: 0.694; 95% CI: 0.541, 0.889).Conclusion: Using a TMLE approach, we present further evidence of a protective effect of MMC against HIV and HSV-2 in this hyper-endemic South African setting. TMLE has the potential to enhance the evidence base for recommendations that embrace the effect of public health interventions on health or disease outcomes.

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Targeted Estimation of Marginal Absolute and Relative Associations in Case–Control Data

Cited by 6 publications

References 35 publications

Community-Level Risk Factors for Firearm Assault and Homicide: The Role of Local Firearm Dealers and Alcohol Outlets

Community-Level Risk Factors for Firearm Assault and Homicide: The Role of Local Firearm Dealers and Alcohol Outlets

Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions

Association of Medical Male Circumcision and Sexually Transmitted Infections in a Population-Based Study: A Targeted Maximum Likelihood Estimation Approach

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