Evaluating the Impact of a HIV Low-Risk Express Care Task-Shifting Program: A Case Study of the Targeted Learning Roadmap

Abstract In conducting studies on an exposure of interest, a systematic roadmap should be applied for translating causal questions into statistical analyses and interpreting the results. In this paper we describe an application of one such roadmap applied to estimating the joint effect of both time to availability of a nurse-based triage system (low risk express care (LREC)) and individual enrollment in the program among HIV patients in East Africa. Our study population is comprised of 16,513 subjects found eligible for this task-shifting program within 15 clinics in Kenya between 2006 and 2009, with each clinic starting the LREC program between 2007 and 2008. After discretizing follow-up into 90-day time intervals, we targeted the population mean counterfactual outcome (i. e. counterfactual probability of either dying or being lost to follow up) at up to 450 days after initial LREC eligibility under three fixed treatment interventions. These were (i) under no program availability during the entire follow-up, (ii) under immediate program availability at initial eligibility, but non-enrollment during the entire follow-up, and (iii) under immediate program availability and enrollment at initial eligibility. We further estimated the controlled direct effect of immediate program availability compared to no program availability, under a hypothetical intervention to prevent individual enrollment in the program. Targeted minimum loss-based estimation was used to estimate the mean outcome, while Super Learning was implemented to estimate the required nuisance parameters. Analyses were conducted with the ltmle R package; analysis code is available at an online repository as an R package. Results showed that at 450 days, the probability of in-care survival for subjects with immediate availability and enrollment was 0.93 (95 % CI: 0.91, 0.95) and 0.87 (95 % CI: 0.86, 0.87) for subjects with immediate availability never enrolling. For subjects without LREC availability, it was 0.91 (95 % CI: 0.90, 0.92). Immediate program availability without individual enrollment, compared to no program availability, was estimated to slightly albeit significantly decrease survival by 4 % (95 % CI 0.03,0.06, p < 0.01). Immediately availability and enrollment resulted in a 7 % higher in-care survival compared to immediate availability with non-enrollment after 450 days (95 % CI –0.08,–0.05, p < 0.01). The results are consistent with a fairly small impact of both availability and enrollment in the LREC program on in-care survival.

[1]  Mark J. van der Laan,et al.  ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data , 2017 .

[2]  K. Harper CROI 2017 highlights. , 2017, AIDS.

[3]  C. Yiannoutsos,et al.  Retention in Care and Patient-Reported Reasons for Undocumented Transfer or Stopping Care Among HIV-Infected Patients on Antiretroviral Therapy in Eastern Africa: Application of a Sampling-Based Approach. , 2016, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[4]  J. Lundgren,et al.  Why START? Reflections that led to the conduct of this large long‐term strategic HIV trial , 2015, HIV medicine.

[5]  C. Yiannoutsos,et al.  Estimation of mortality among HIV-infected people on antiretroviral treatment in East Africa: a sampling based approach in an observational, multisite, cohort study. , 2015, The lancet. HIV.

[6]  Maya L Petersen,et al.  Commentary: Applying a Causal Road Map in Settings with Time-dependent Confounding , 2014, Epidemiology.

[7]  C. Yiannoutsos,et al.  Delayed switch of antiretroviral therapy after virologic failure associated with elevated mortality among HIV-infected adults in Africa , 2014, AIDS.

[8]  M. J. van der Laan,et al.  Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models , 2014, Journal of causal inference.

[9]  M. J. van der Laan,et al.  Causal Models and Learning from Data: Integrating Causal Modeling and Statistical Estimation , 2014, Epidemiology.

[10]  Mark J van der Laan,et al.  Modeling the impact of hepatitis C viral clearance on end‐stage liver disease in an HIV co‐infected cohort with targeted maximum likelihood estimation , 2014, Biometrics.

[11]  Catherine M Crespi,et al.  Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package , 2014, Journal of causal inference.

[12]  M. J. van der Laan,et al.  Targeted Minimum Loss-Based Estimation of Causal Effects in Right-Censored Survival Data with Time-Dependent Covariates: Warfarin, Stroke, and Death in Atrial Fibrillation , 2013 .

[13]  Michael Rayment,et al.  Prevention of HIV-1 infection with early antiretroviral therapy , 2012, Journal of Family Planning and Reproductive Health Care.

[14]  C. Yiannoutsos,et al.  A causal framework for understanding the effect of losses to follow-up on epidemiologic analyses in clinic-based cohorts: the case of HIV-infected patients on antiretroviral therapy in Africa. , 2012, American journal of epidemiology.

[15]  Kristin E. Porter,et al.  Diagnosing and responding to violations in the positivity assumption , 2012, Statistical methods in medical research.

[16]  Tyler J VanderWeele,et al.  On causal inference in the presence of interference , 2012, Statistical methods in medical research.

[17]  A. Fauci,et al.  Thirty Years of HIV and AIDS: Future Challenges and Opportunities , 2011, Annals of Internal Medicine.

[18]  M. J. Laan,et al.  Targeted Minimum Loss Based Estimation of an Intervention Specific Mean Outcome , 2011 .

[19]  R. Colebunders,et al.  ART in low-resource settings: how to do more with less , 2010, The Lancet.

[20]  E. Vittinghoff,et al.  Decreases in Community Viral Load Are Accompanied by Reductions in New HIV Infections in San Francisco , 2010, PloS one.

[21]  S. Cole,et al.  Invited commentary: positivity in practice. , 2010, American journal of epidemiology.

[22]  B. Agins,et al.  Retaining HIV-infected patients in care: Where are we? Where do we go from here? , 2010, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[23]  Mark J. van der Laan,et al.  Asymptotic Theory for Cross-validated Targeted Maximum Likelihood Estimation , 2010 .

[24]  J. Robins,et al.  Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. , 2009, International journal of epidemiology.

[25]  Ross J. Harris,et al.  Mortality of HIV-infected patients starting potent antiretroviral therapy: comparison with the general population in nine industrialized countries. , 2009, International journal of epidemiology.

[26]  Matthias Egger,et al.  Sexual transmission of HIV according to viral load and antiretroviral therapy: systematic review and meta-analysis , 2009, AIDS.

[27]  Readings in Targeted Maximum Likelihood Estimation , 2009 .

[28]  M. G. Pittau,et al.  A weakly informative default prior distribution for logistic and other regression models , 2008, 0901.4011.

[29]  M. Robins James,et al.  Estimation of the causal effects of time-varying exposures , 2008 .

[30]  M. Suarez‐Almazor,et al.  Retention in care: a challenge to survival with HIV infection. , 2007, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[31]  M. J. van der Laan,et al.  Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .

[32]  B. Chi,et al.  Rapid scale-up of antiretroviral therapy at primary care sites in Zambia: feasibility and early outcomes. , 2006, JAMA.

[33]  A. Tsiatis Semiparametric Theory and Missing Data , 2006 .

[34]  J. Robins,et al.  Estimating causal effects from epidemiological data , 2006, Journal of Epidemiology and Community Health.

[35]  M. Laga,et al.  The real challenges for scaling up ART in sub-Saharan Africa. , 2006, AIDS.

[36]  A. V. D. Vaart,et al.  Oracle inequalities for multi-fold cross validation , 2006 .

[37]  J. Robins,et al.  Doubly Robust Estimation in Missing Data and Causal Inference Models , 2005, Biometrics.

[38]  R. Frankowski,et al.  Patients referred to an urban HIV clinic frequently fail to establish care: factors predicting failure , 2005, AIDS care.

[39]  M. J. van der Laan,et al.  Analysis of longitudinal marginal structural models. , 2004, Biostatistics.

[40]  M. Davidian,et al.  Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology. , 2004, American journal of epidemiology.

[41]  James M. Robins,et al.  Association, Causation, And Marginal Structural Models , 1999, Synthese.

[42]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[43]  K. Freedberg,et al.  Discontinuation From HIV Medical Care: Squandering Treatment Opportunities , 2010, Journal of health care for the poor and underserved.

[44]  James M. Robins,et al.  Unified Methods for Censored Longitudinal Data and Causality , 2003 .

[45]  Andrea Rotnitzky,et al.  Inverse probability weighted estimation in survival analysis , 2003 .

[46]  Somnath Datta,et al.  Marginal Analyses of Multistage Data , 2003, Advances in Survival Analysis.

[47]  S. Dudoit,et al.  Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples , 2003 .

[48]  J. Friedman Stochastic gradient boosting , 2002 .

[49]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[50]  Somnath Datta,et al.  The Kaplan–Meier Estimator as an Inverse-Probability-of-Censoring Weighted Average , 2001, The American statistician.

[51]  J. Robins,et al.  Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.

[52]  James M. Robins,et al.  On Profile Likelihood: Comment , 2000 .

[53]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[54]  Stefan Sperlich,et al.  Generalized Additive Models , 2014 .

[55]  James M. Robins,et al.  Marginal Structural Models versus Structural nested Models as Tools for Causal inference , 2000 .

[56]  D. Rubin,et al.  Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Commentary. Authors' reply , 1999 .

[57]  P. Kissinger,et al.  Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. , 1998, The New England journal of medicine.

[58]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[59]  G. Satten,et al.  Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. , 1998, The New England journal of medicine.

[60]  E A Emini,et al.  Treatment with indinavir, zidovudine, and lamivudine in adults with human immunodeficiency virus infection and prior antiretroviral therapy. , 1997, The New England journal of medicine.

[61]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[62]  R. Tibshirani,et al.  Generalized additive models for medical research , 1995, Statistical methods in medical research.

[63]  D. Cohen,et al.  Compliance with public sector HIV medical care. , 1995, Journal of the National Medical Association.

[64]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[65]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[66]  J. Robins,et al.  Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers , 1992 .

[67]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[68]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[69]  J. Robins A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. , 1987, Journal of chronic diseases.

[70]  J. Robins A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[71]  J. V. Ryzin,et al.  Regression Analysis with Randomly Right-Censored Data , 1981 .

[72]  D. Horvitz,et al.  A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .