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1.
Ann Intern Med ; 177(2): 165-176, 2024 02.
Artigo em Inglês | MEDLINE | ID: mdl-38190711

RESUMO

BACKGROUND: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. OBJECTIVE: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. DESIGN: Comparative effectiveness research accounting for underreported vaccination in 3 study cohorts: adolescents (12 to 20 years) during the Delta phase and children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. SETTING: A national collaboration of pediatric health systems (PEDSnet). PARTICIPANTS: 77 392 adolescents (45 007 vaccinated) during the Delta phase and 111 539 children (50 398 vaccinated) and 56 080 adolescents (21 180 vaccinated) during the Omicron phase. INTERVENTION: First dose of the BNT162b2 vaccine versus no receipt of COVID-19 vaccine. MEASUREMENTS: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100, with confounders balanced via propensity score stratification. RESULTS: During the Delta period, the estimated effectiveness of the BNT162b2 vaccine was 98.4% (95% CI, 98.1% to 98.7%) against documented infection among adolescents, with no statistically significant waning after receipt of the first dose. An analysis of cardiac complications did not suggest a statistically significant difference between vaccinated and unvaccinated groups. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (CI, 72.2% to 76.2%). Higher levels of effectiveness were seen against moderate or severe COVID-19 (75.5% [CI, 69.0% to 81.0%]) and ICU admission with COVID-19 (84.9% [CI, 64.8% to 93.5%]). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (CI, 83.8% to 87.1%), with 84.8% (CI, 77.3% to 89.9%) against moderate or severe COVID-19, and 91.5% (CI, 69.5% to 97.6%) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined 4 months after the first dose and then stabilized. The analysis showed a lower risk for cardiac complications in the vaccinated group during the Omicron variant period. LIMITATION: Observational study design and potentially undocumented infection. CONCLUSION: This study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. PRIMARY FUNDING SOURCE: National Institutes of Health.


Assuntos
Vacina BNT162 , COVID-19 , Estados Unidos , Humanos , Adolescente , Criança , Vacinas contra COVID-19 , COVID-19/prevenção & controle , Pesquisa Comparativa da Efetividade , Hospitalização
2.
Epidemiology ; 35(1): 16-22, 2024 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-38032801

RESUMO

Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and postexposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, for example, if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits nonadditive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called universal difference-in-differences. We describe and illustrate both fully parametric and more robust semiparametric universal difference-in-differences estimators in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil. A supplementary digital video is available at: http://links.lww.com/EDE/C90.


Assuntos
Infecção por Zika virus , Zika virus , Humanos , Fatores de Confusão Epidemiológicos , Causalidade , Viés , Razão de Chances , Surtos de Doenças , Infecção por Zika virus/epidemiologia , Modelos Estatísticos
3.
Biometrics ; 80(2)2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38646999

RESUMO

Negative control variables are sometimes used in nonexperimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome that is influenced by unobserved confounders of the exposure effects on the outcome in view, but is not causally impacted by the exposure. Tchetgen Tchetgen (2013) introduced the Control Outcome Calibration Approach (COCA) as a formal NCO counterfactual method to detect and correct for residual confounding bias. For identification, COCA treats the NCO as an error-prone proxy of the treatment-free counterfactual outcome of interest, and involves regressing the NCO on the treatment-free counterfactual, together with a rank-preserving structural model, which assumes a constant individual-level causal effect. In this work, we establish nonparametric COCA identification for the average causal effect for the treated, without requiring rank-preservation, therefore accommodating unrestricted effect heterogeneity across units. This nonparametric identification result has important practical implications, as it provides single-proxy confounding control, in contrast to recently proposed proximal causal inference, which relies for identification on a pair of confounding proxies. For COCA estimation we propose 3 separate strategies: (i) an extended propensity score approach, (ii) an outcome bridge function approach, and (iii) a doubly-robust approach. Finally, we illustrate the proposed methods in an application evaluating the causal impact of a Zika virus outbreak on birth rate in Brazil.


Assuntos
Pontuação de Propensão , Humanos , Fatores de Confusão Epidemiológicos , Infecção por Zika virus/epidemiologia , Causalidade , Modelos Estatísticos , Viés , Brasil/epidemiologia , Simulação por Computador , Feminino , Gravidez
4.
Biometrics ; 80(2)2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38819307

RESUMO

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.


Assuntos
Simulação por Computador , Modelos Estatísticos , Vacinas Pneumocócicas , Humanos , Vacinas Pneumocócicas/uso terapêutico , Vacinas Pneumocócicas/administração & dosagem , Resultado do Tratamento , Biometria/métodos , Interpretação Estatística de Dados
5.
J R Stat Soc Series B Stat Methodol ; 86(2): 487-511, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38618143

RESUMO

Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption about ψ-network dependence. Finally, we provide a consistent variance estimator.

6.
Am J Epidemiol ; 192(10): 1772-1780, 2023 10 10.
Artigo em Inglês | MEDLINE | ID: mdl-37338999

RESUMO

Randomized trials offer a powerful strategy for estimating the effect of a treatment on an outcome. However, interpretation of trial results can be complicated when study subjects do not take the treatment to which they were assigned; this is referred to as nonadherence. Prior authors have described instrumental variable approaches to analyze trial data with nonadherence; under their approaches, the initial assignment to treatment is used as an instrument. However, their approaches require the assumption that initial assignment to treatment has no direct effect on the outcome except via the actual treatment received (i.e., the exclusion restriction), which may be implausible. We propose an approach to identification of a causal effect of treatment in a trial with 1-sided nonadherence without assuming exclusion restriction. The proposed approach leverages the study subjects initially assigned to control status as an unexposed reference population; we then employ a bespoke instrumental variable analysis, where the key assumption is "partial exchangeability" of the association between a covariate and an outcome in the treatment and control arms. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical application.


Assuntos
Ensaios Clínicos como Assunto , Cooperação do Paciente , Humanos , Causalidade
7.
Epidemiology ; 34(2): 167-174, 2023 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-36722798

RESUMO

Difference-in-differences (DID) analyses are used in a variety of research areas as a strategy for estimating the causal effect of a policy, program, intervention, or environmental hazard (hereafter, treatment). The approach offers a strategy for estimating the causal effect of a treatment using observational (i.e., nonrandomized) data in which outcomes on each study unit have been measured both before and after treatment. To identify a causal effect, a DID analysis relies on an assumption that confounding of the treatment effect in the pretreatment period is equivalent to confounding of the treatment effect in the post treatment period. We propose an alternative approach that can yield identification of causal effects under different identifying conditions than those usually required for DID. The proposed approach, which we refer to as generalized DID, has the potential to be used in routine policy evaluation across many disciplines, as it essentially combines two popular quasiexperimental designs, leveraging their strengths while relaxing their usual assumptions. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical example based on Card and Krueger's landmark study of the impact of an increase in minimum wage in New Jersey on employment.


Assuntos
Emprego , Renda , Humanos , New Jersey , Políticas
8.
Biometrics ; 79(3): 1597-1609, 2023 09.
Artigo em Inglês | MEDLINE | ID: mdl-35665918

RESUMO

Treatment switching in a randomized controlled trial occurs when a patient in one treatment arm switches to another arm during follow-up. This can occur at the point of disease progression, whereby patients in the control arm may be offered the experimental treatment. It is widely known that failure to account for treatment switching can seriously bias the estimated treatment causal effect. In this paper, we aim to account for the potential impact of treatment switching in a reanalysis evaluating the treatment effect of nucleoside reverse transcriptase inhibitors (NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in participants receiving a new antiretroviral regimen that either included or omitted NRTIs in the optimized treatment that includes or omits NRTIs trial. We propose an estimator of a treatment causal effect for a censored time to event outcome under a structural cumulative survival model that leverages randomization as an instrumental variable to account for selective treatment switching. We establish that the proposed estimator is uniformly consistent and asymptotically Gaussian, with a consistent variance estimator and confidence intervals given, whose finite-sample performance is evaluated via extensive simulations. An R package 'ivsacim' implementing all proposed methods is freely available on R CRAN. Results indicate that adding NRTIs versus omitting NRTIs to a new optimized treatment regime may increase the risk for a safety outcome.


Assuntos
Infecções por HIV , Troca de Tratamento , Humanos , Infecções por HIV/tratamento farmacológico , Resultado do Tratamento
9.
Biometrics ; 79(2): 539-550, 2023 06.
Artigo em Inglês | MEDLINE | ID: mdl-36377509

RESUMO

Cox's proportional hazards model is one of the most popular statistical models to evaluate associations of exposure with a censored failure time outcome. When confounding factors are not fully observed, the exposure hazard ratio estimated using a Cox model is subject to unmeasured confounding bias. To address this, we propose a novel approach for the identification and estimation of the causal hazard ratio in the presence of unmeasured confounding factors. Our approach is based on a binary instrumental variable, and an additional no-interaction assumption in a first-stage regression of the treatment on the IV and unmeasured confounders. We propose, to the best of our knowledge, the first consistent estimator of the (population) causal hazard ratio within an instrumental variable framework. A version of our estimator admits a closed-form representation. We derive the asymptotic distribution of our estimator and provide a consistent estimator for its asymptotic variance. Our approach is illustrated via simulation studies and a data application.


Assuntos
Modelos Estatísticos , Modelos de Riscos Proporcionais , Simulação por Computador , Causalidade , Viés
10.
Biometrics ; 79(3): 2208-2219, 2023 09.
Artigo em Inglês | MEDLINE | ID: mdl-35950778

RESUMO

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.


Assuntos
Análise da Randomização Mendeliana , Análise da Randomização Mendeliana/métodos , Causalidade , Simulação por Computador , Viés
11.
Biometrics ; 79(4): 3203-3214, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-37488709

RESUMO

We introduce an itemwise modeling approach called "self-censoring" for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full-data distribution. We also provide a novel and flexible global sensitivity analysis procedure anchored at the self-censoring. We evaluate the performance of the proposed methods with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.


Assuntos
Modelos Estatísticos , Mães , Recém-Nascido , Feminino , Humanos
12.
Biometrics ; 79(2): 564-568, 2023 06.
Artigo em Inglês | MEDLINE | ID: mdl-36448265

RESUMO

In this paper, we respond to comments on our paper, "Instrumental variable estimation of the causal hazard ratio."


Assuntos
Modelos de Riscos Proporcionais , Causalidade
13.
J R Stat Soc Series B Stat Methodol ; 85(3): 913-935, 2023 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-37521168

RESUMO

We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for n-estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires the existence of a function as a solution to a representer equation that connects the shadow variable to the mean functional. Solutions to the representer equation may not be unique, which presents substantial challenges for non-parametric estimation, and standard theories for non-parametric sieve estimators are not applicable here. We construct a consistent estimator of the solution set and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator of an appropriately chosen solution. The estimator is asymptotically normal, locally efficient and attains the semi-parametric efficiency bound under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing.

14.
J R Stat Soc Series B Stat Methodol ; 85(5): 1680-1705, 2023 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-38312527

RESUMO

Predicting sets of outcomes-instead of unique outcomes-is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown covariate shift-a prevalent issue in practice-poses a serious unsolved challenge. In this article, we show that prediction sets with finite-sample coverage guarantee are uninformative and propose a novel flexible distribution-free method, PredSet-1Step, to efficiently construct prediction sets with an asymptotic coverage guarantee under unknown covariate shift. We formally show that our method is asymptotically probably approximately correct, having well-calibrated coverage error with high confidence for large samples. We illustrate that it achieves nominal coverage in a number of experiments and a data set concerning HIV risk prediction in a South African cohort study. Our theory hinges on a new bound for the convergence rate of the coverage of Wald confidence intervals based on general asymptotically linear estimators.

15.
Stat Probab Lett ; 1982023 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38405420

RESUMO

We consider identification and inference about a counterfactual outcome mean when there is unmeasured confounding using tools from proximal causal inference. Proximal causal inference requires existence of solutions to at least one of two integral equations. We motivate the existence of solutions to the integral equations from proximal causal inference by demonstrating that, assuming the existence of a solution to one of the integral equations, n-estimability of a mean functional of that solution requires the existence of a solution to the other integral equation. Solutions to the integral equations may not be unique, which complicates estimation and inference. We construct a consistent estimator for the solution set for one of the integral equations and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator for a uniquely defined solution. A debiased estimator is shown to be root-n consistent, regular, and semiparametrically locally efficient under additional regularity conditions.

16.
Am J Epidemiol ; 191(5): 939-947, 2022 03 24.
Artigo em Inglês | MEDLINE | ID: mdl-34907434

RESUMO

Suppose that an investigator is interested in quantifying an exposure-disease causal association in a setting where the exposure, disease, and some potential confounders of the association of interest have been measured. However, there remains concern about residual confounding of the association of interest by unmeasured confounders. We propose an approach to account for residual bias due to unmeasured confounders. The proposed approach uses a measured confounder to derive a "bespoke" instrumental variable that is tailored to the study population and is used to control for bias due to residual confounding. The approach may provide a useful tool for assessing and accounting for bias due to residual confounding. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical example concerning mortality among Japanese atomic bomb survivors.


Assuntos
Projetos de Pesquisa , Viés , Causalidade , Fatores de Confusão Epidemiológicos , Humanos
17.
Am J Epidemiol ; 191(11): 1954-1961, 2022 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-35916388

RESUMO

A covariate-adjusted estimate of an exposure-outcome association may be biased if the exposure variable suffers measurement error. We propose an approach to correct for exposure measurement error in a covariate-adjusted estimate of the association between a continuous exposure variable and outcome of interest. Our proposed approach requires data for a reference population in which the exposure was a priori set to some known level (e.g., 0, and is therefore unexposed); however, our approach does not require an exposure validation study or replicate measures of exposure, which are typically needed when addressing bias due to exposure measurement error. A key condition for this method, which we refer to as "partial population exchangeability," requires that the association between a measured covariate and outcome in the reference population equals the association between that covariate and outcome in the target population in the absence of exposure. We illustrate the approach using simulations and an example.


Assuntos
Projetos de Pesquisa , Humanos , Viés
18.
Am J Epidemiol ; 191(2): 349-359, 2022 01 24.
Artigo em Inglês | MEDLINE | ID: mdl-34668974

RESUMO

Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability-weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates.


Assuntos
Causalidade , Modelos Estruturais , Modelos Teóricos , Viés , Simulação por Computador , Interpretação Estatística de Dados , Depressão/epidemiologia , Depressão/etiologia , Humanos , Fatores de Risco , Acidente Vascular Cerebral/psicologia
19.
N Engl J Med ; 381(3): 230-242, 2019 07 18.
Artigo em Inglês | MEDLINE | ID: mdl-31314967

RESUMO

BACKGROUND: The feasibility of reducing the population-level incidence of human immunodeficiency virus (HIV) infection by increasing community coverage of antiretroviral therapy (ART) and male circumcision is unknown. METHODS: We conducted a pair-matched, community-randomized trial in 30 rural or periurban communities in Botswana from 2013 to 2018. Participants in 15 villages in the intervention group received HIV testing and counseling, linkage to care, ART (started at a higher CD4 count than in standard care), and increased access to male circumcision services. The standard-care group also consisted of 15 villages. Universal ART became available in both groups in mid-2016. We enrolled a random sample of participants from approximately 20% of households in each community and measured the incidence of HIV infection through testing performed approximately once per year. The prespecified primary analysis was a permutation test of HIV incidence ratios. Pair-stratified Cox models were used to calculate 95% confidence intervals. RESULTS: Of 12,610 enrollees (81% of eligible household members), 29% were HIV-positive. Of the 8974 HIV-negative persons (4487 per group), 95% were retested for HIV infection over a median of 29 months. A total of 57 participants in the intervention group and 90 participants in the standard-care group acquired HIV infection (annualized HIV incidence, 0.59% and 0.92%, respectively). The unadjusted HIV incidence ratio in the intervention group as compared with the standard-care group was 0.69 (P = 0.09) by permutation test (95% confidence interval [CI], 0.46 to 0.90 by pair-stratified Cox model). An end-of-trial survey in six communities (three per group) showed a significantly greater increase in the percentage of HIV-positive participants with an HIV-1 RNA level of 400 copies per milliliter or less in the intervention group (18 percentage points, from 70% to 88%) than in the standard-care group (8 percentage points, from 75% to 83%) (relative risk, 1.12; 95% CI, 1.09 to 1.16). The percentage of men who underwent circumcision increased by 10 percentage points in the intervention group and 2 percentage points in the standard-care group (relative risk, 1.26; 95% CI, 1.17 to 1.35). CONCLUSIONS: Expanded HIV testing, linkage to care, and ART coverage were associated with increased population viral suppression. (Funded by the President's Emergency Plan for AIDS Relief and others; Ya Tsie ClinicalTrials.gov number, NCT01965470.).


Assuntos
Antirretrovirais/uso terapêutico , Circuncisão Masculina , Infecções por HIV/diagnóstico , Infecções por HIV/tratamento farmacológico , Programas de Rastreamento , Adolescente , Adulto , Botsuana/epidemiologia , Circuncisão Masculina/estatística & dados numéricos , Feminino , Infecções por HIV/epidemiologia , Infecções por HIV/prevenção & controle , Humanos , Incidência , Masculino , Administração Massiva de Medicamentos , Pessoa de Meia-Idade , Modelos de Riscos Proporcionais , População Rural , Fatores Socioeconômicos , Carga Viral , Adulto Jovem
20.
Biometrics ; 78(2): 668-678, 2022 06.
Artigo em Inglês | MEDLINE | ID: mdl-33914905

RESUMO

A prominent threat to causal inference about peer effects in social science studies is the presence of homophily bias , that is, social influence between friends and families is entangled with common characteristics or underlying similarities that form close connections. Analysis of social study data has suggested that certain health conditions such as obesity and psychological states including happiness and loneliness can spread between friends and relatives. However, such analyses of peer effects or contagion effects have come under criticism because homophily bias may compromise the causal statement. We develop a regression-based approach which leverages a negative control exposure for identification and estimation of contagion effects on additive or multiplicative scales, in the presence of homophily bias. We apply our methods to evaluate the peer effect of obesity in Framingham Offspring Study.


Assuntos
Amigos , Grupo Associado , Viés , Amigos/psicologia , Humanos , Obesidade
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