Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

PURPOSE

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.

METHODS

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results.

RESULTS

Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE.

CONCLUSIONS

There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

Association of Combined Estrogen-Progestogen and Progestogen-Only Contraceptives with the Development of Cancer

Combined estrogen-progestogen contraceptives (oral contraceptives or OCs) and progestogen-only contraceptives (POCs) are synthetic steroids that bind to steroid hormone receptors, which are widespread throughout the body. They have a profound effect on cellular physiology. Combined OCs have been classified by the International Agency for Research on Cancer (IARC) as Group 1 carcinogens, but their findings have not been updated recently. In order to update the information and better understand the impact that OCs and POCs have on the risk of development of cancers, a comprehensive literature search was undertaken, focusing on more recently published papers. In agreement with the IARC, the recent literature confirms an increased risk of breast cancer and cervical cancer with the use of OCs. The recent literature also confirms the IARC conclusion that OCs decrease the risk of ovarian and endometrial cancers. However, there is little support from recent studies for the IARC conclusion that OCs decrease the risk of colorectal cancer or increase the risk of liver cancer. For liver cancer, this may be due to the recent studies having been performed in areas where hepatitis is endemic. In one large observational study, POCs also appear to increase the overall risk of developing cancer. OCs and POCs appear to increase the overall risk of cancer when carefully performed studies with the least intrinsic bias are considered.

Summary

OCs have been classified as cancer-causing agents, especially leading to increases in breast cancer and cervical cancer. A review of the recent scientific literature was performed to see whether this still appears to be the case. The recent literature supports the cancer-causing role of OCs especially for breast cancer and cervical cancer. Studies also indicate that progesterone-only contraceptives (such as implants and vaginal rings) also can cause cancer. This is especially true for breast cancer and cervical cancer.

A review of time scale fundamentals in the g-formula and insidious selection bias

PURPOSE OF REVIEW

We review recent examples of data analysis with the g-formula, a powerful tool for analyzing longitudinal data and survival analysis. Specifically, we focus on the common choices of time scale and review inferential issues that may arise.

RECENT FINDINGS

Researchers are increasingly engaged with questions that require time scales subject to left-truncation and right-censoring. The assumptions necessary for allowing right-censoring are well defined in the literature, whereas similar assumptions for left-truncation are not well defined. Policy and biologic considerations sometimes dictate that observational data must be analyzed on time scales that are subject to left-truncation, such as age.

SUMMARY

Further consideration of left-truncation is needed, especially when biologic or policy considerations dictate that age is the relevant time scale of interest. Methodologic development is needed to reduce potential for bias when left-truncation may occur.