Estimation of average treatment effect with incompletely observed longitudinal data: application to a smoking cessation study

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.

A tutorial on sensitivity analyses in clinical trials: the what, why, when and how

BACKGROUND

Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials. They are a critical way to assess the impact, effect or influence of key assumptions or variations--such as different methods of analysis, definitions of outcomes, protocol deviations, missing data, and outliers--on the overall conclusions of a study.The current paper is the second in a series of tutorial-type manuscripts intended to discuss and clarify aspects related to key methodological issues in the design and analysis of clinical trials.

DISCUSSION

In this paper we will provide a detailed exploration of the key aspects of sensitivity analyses including: 1) what sensitivity analyses are, why they are needed, and how often they are used in practice; 2) the different types of sensitivity analyses that one can do, with examples from the literature; 3) some frequently asked questions about sensitivity analyses; and 4) some suggestions on how to report the results of sensitivity analyses in clinical trials.

SUMMARY

When reporting on a clinical trial, we recommend including planned or posthoc sensitivity analyses, the corresponding rationale and results along with the discussion of the consequences of these analyses on the overall findings of the study.

All-cause and cause-specific mortality associated with diabetes in prevalent hemodialysis patients

Background

Diabetes is the most common risk factor for end-stage renal disease (ESRD) and has been associated with increased risk of death. In order to better understand the influence of diabetes on outcomes in hemodialysis, we examine the risk of death of diabetic participants in the HEMODIALYSIS (HEMO) study.

Methods

In the HEMO study, 823 (44.6%) participants were classified as diabetic. Using the Schoenfeld residual test, we found that diabetes violated the proportional hazards assumption. Based on this result, we fit two non-proportional hazard models: Cox’s time varying covariate model (Cox-TVC) that allows the hazard for diabetes to change linearly with time and Gray’s time-varying coefficient model.

Results

Using the Cox-TVC, the hazard ratio (HR) for diabetes increased with each year of follow up (p = 0.02) for all cause mortality. Using Gray’s model, the HR for diabetes ranged from 1.41 to 2.21 (p <0.01). The HR for diabetes using Gray’s model exhibited a different pattern, being relatively stable at 1.5 for the first 3 years in the study and increasing afterwards.

Conclusion

Risk of death associated with diabetes in ESRD increases over time and suggests that an increasing risk of death among diabetes may be underappreciated when using conventional survival models.

Depressive mood, suicide ideation and anxiety in smokers who do and smokers who do not manage to stop smoking after a target quit day

AIMS

The effect of successful and unsuccessful smoking cessation on depressive mood, anxiety- and suicide-related outcomes is unclear. The aim of this secondary analysis was to explore the relationship between abstinence status and these outcomes.

DESIGN

Cohort of adult smokers attempting to stop smoking. Smoking status was assessed by a daily diary; depressed mood, anxiety and suicidal tendencies by the Hamilton Depression Rating Scale (HDRS). The association of complete and point-prevalence abstinence with the HDRS variables was assessed using multi-level linear regression models.

SETTING

Randomized trial of sertraline versus placebo for smoking cessation with weekly behavioural support provided in a clinic.

PARTICIPANTS

A total of 133 adult smokers with past major depression.

FINDINGS

Pre-quit mood scores did not predict smoking status post-quit day. Both continuous and point-prevalence abstainers had significantly lower total HDRS, suicide and anxiety scores, adjusted for all potential confounders, during the period following quit day than did non-abstainers who experienced a significant mood deterioration. There was a significant effect of sertraline on post-quit HDRS scores but not on abstinence.

CONCLUSIONS

Contrary to expectation, smoking abstinence among smokers with a history of major depression did not lead to increase in depression, anxiety or suicide ideation; however, failed quit attempts did. Persisting with a quit attempt while unable to achieve abstinence may be associated with mood deterioration.