Bespoke instrumental variables with non-ideal reference populations.

Recently, a bespoke instrumental variable method was proposed, which, under certain assumptions, can eliminate bias due to unmeasured confounding when estimating the causal exposure effect among the exposed. This method uses data from both the study population of interest, and a reference population in which the exposure is completely absent. In this paper, we extend the bespoke instrumental variable method to allow for a non-ideal reference population that may include exposed subjects. Such an extension is particularly important in randomized trials with nonadherence, where even subjects in the control arm may have access to the treatment under investigation. We further scrutinize the assumptions underlying the bespoke instrumental method, and caution the reader about the potential non-robustness of the method to these assumptions.

Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

The use, adherence, and evaluation of interactive text-messaging among women admitted to prevention of mother-to-child transmission of HIV care in Kenya (WelTel PMTCT).

BACKGROUND: To improve future mobile health (mHealth) interventions in resource-limited settings, knowledge of participants' adherence to interactive interventions is needed, but previous studies are limited. We aimed to investigate how women in prevention of mother-to-child transmission of HIV (PMTCT) care in Kenya used, adhered to, and evaluated an interactive text-messaging intervention. METHODS: We conducted a cohort study nested within the WelTel PMTCT trial among 299 pregnant women living with HIV aged ≥ 18 years. They received weekly text messages from their first antenatal care visit until 24 months postpartum asking "How are you?". They were instructed to text within 48 h stating that they were "okay" or had a "problem". Healthcare workers phoned non-responders and problem-responders to manage any issue. We used multivariable-adjusted logistic and negative binomial regression to estimate adjusted odds ratios (aORs), rate ratios (aRRs) and 95% confidence intervals (CIs) to assess associations between baseline characteristics and text responses. Perceptions of the intervention were evaluated through interviewer-administered follow-up questionnaires at 24 months postpartum. RESULTS: The 299 participants sent 15,183 (48%) okay-responses and 438 (1%) problem-responses. There were 16,017 (51%) instances of non-response. The proportion of non-responses increased with time and exceeded 50% around 14 months from enrolment. Most reported problems were health related (84%). Having secondary education was associated with reporting a problem (aOR:1.88; 95%CI: 1.08-3.27) compared to having primary education or less. Younger age (18-24 years) was associated with responding to < 50% of messages (aOR:2.20; 95%CI: 1.03-4.72), compared to being 35-44 years. Women with higher than secondary education were less likely (aOR:0.28; 95%CI: 0.13-0.64), to respond to < 50% of messages compared to women with primary education or less. Women who had disclosed their HIV status had a lower rate of non-response (aRR:0.77; 95%CI: 0.60-0.97). In interviews with 176 women, 167 (95%) agreed or strongly agreed that the intervention had been helpful, mainly by improving access to and communication with their healthcare providers (43%). CONCLUSION: In this observational study, women of younger age, lower education, and who had not disclosed their HIV status were less likely to adhere to interactive text-messaging. The majority of those still enrolled at the end of the intervention reported that text-messaging had been helpful, mainly by improving access to healthcare providers. Future mHealth interventions aiming to improve PMTCT care need to be targeted to attract the attention of women with lower education and younger age.

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation.

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g $$ g $$ -formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains 'unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.