Assessing Health-Related Quality of Life Improvement in the Modern Anticancer Therapy Era

Recent breakthroughs in anticancer therapies such as small-molecule drugs and immunotherapies have made improvements in Health-Related Quality of Life (HRQOL) possible among cancer patients over the course of treatment. In a recent paper published in the Journal of Clinical Epidemiology, Cottone and colleagues are the first to propose the framework for assessing the change in HRQOL over time in these patients: Time to HRQOL Improvement (TTI), and Time to Sustained HRQOL Improvement (TTSI).

In the proposed framework, TTI is based on the time to the “first clinically meaningful improvement occurring in a given scale or in at least one among different scales” – for instance, a minimal important difference (MID) of 5 points on the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire – Core 30 (QLQ-C30). The authors suggest utilizing the first posttreatment score as the baseline measurement for monitoring improvements over time. “Sustained improvement” was defined as the first improvement that is not followed by a deterioration that meets or exceeds the MID.

 

The use of Kaplan-Meier curves and Cox proportional hazards is inappropriate for these outcomes, the authors argue, as it does not allow for possible competing events, such as disease progression, toxicity, or the possibility of an earlier improvement in another scale when multiple scales are used. They propose the use of the Fine-Gray model for the evaluation of TTI and TTSI and pilot it with a case study of 124 newly diagnosed chronic myeloid leukemia patients undergoing first-line treatment with nilotinib.

Time To Improvement (TTI) and Time to Sustained Improvement (TTSI) can be used to elucidate differences in HRQOL responses to treatment based on baseline characteristics. Here, the figure shows TTSI in fatigue scores based on hemoglobin level at baseline. Click to enlarge.

Using this model, the authors found that improvements in fatigue scores appeared more quickly than those in physical functioning when measuring scores from baseline (pre-treatment), but upon using first post-treatment score as the baseline, the differences between improvement rates in fatigue and physical functioning diminished. Additionally, a lower baseline hemoglobin level was associated with earlier sustained improvements in fatigue.

 

While the proposed method of evaluating TTI and TTSI has some limitations, such as lower statistical power than other ways of tracking changes in HRQOL over time, it also has notable strengths. In particular, this method can be used to elucidate differences between treatment approaches that show similar survival outcomes so that the approach with shorter TTI and TTSI can be favored.

Cottone, F., Collins, G.S., Anota, A., Sommer, K., Giesinger, J.M., Kieffer, J.M., ... & Efficace, F. (2020). Time to health-related quality of life improvement analysis was developed to enhance evaluation of modern anticancer therapies. J Clin Epidemiol 127:9-18.

Manuscript available from publisher's website here. 

Research Shorts: Use of GRADE for the Assessment of Evidence about Prognostic Factors

In addition to questions of interventions and diagnostic tests, GRADE can also be used to assess the certainty of evidence when it comes to prognostic factors. In part 28 of the Journal of Clinical Epidemiology’s GRADE series published earlier this year, Foroutan and colleagues provide guidance for applying GRADE to a body of evidence of prognostic factors.

The Purpose of Prognostic Studies

GRADE may be applied to a body of evidence, separated by individual prognostic factors instead of outcomes, for one of two reasons. The first is a non-contextualized setting, such as when the certainty of evidence surrounding prognostic factors is being evaluated for application within research planning and analysis (e.g., determining which factors are best to use when stratifying for randomization). The second is a contextualized setting, when the certainty of evidence surrounding prognostic factors is used to help inform clinical decisions.

Establishing the Certainty of Evidence

Unlike when grading the certainty of evidence of an intervention, when assessing prognostic evidence, the overall certainty for observational studies starts out as HIGH. This is because the patient population is likely to be more representative studies than in RCTs, when eligibility criteria may place artificial restrictions on the characteristics of patients. Certainty may then be rated down based on the five traditional domains:

• Risk of bias tools and instruments such as QUality In Prognosis Studies (QUIPS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) may be helpful here. When teasing out the effect of each potential factor, consider utilizing some form of multivariate analysis that accounts for dependence between several different prognostic factors.
• Inconsistency can be examined via visual tests of the variability between individual point estimates and the overlap of confidence intervals; statistical tests such as i2 are likely to be less helpful, as they can often be inflated when large studies lead to particularly narrow Cis. As always, potential explanations for any observed heterogeneity should be considered a priori.
• Imprecision will depend on whether the setting is contextualized, in which case it will depend on the relationship between the confidence interval and the previously set clinical decision threshold, or non-contextualized, in which case the threshold will most likely represent the line of no effect.
• Indirectness should be based on a comparison of the PICOs for the clinical question at hand, and those addressed in the meta-analyzed studies.
• Publication bias can be assessed via visually exploring a funnel plot or the use of appropriately applied statistical tests.

Foroutan F, Guyatt G, Zuk V, Vandvik PO, Alba AC, Mustafa R, Vernooij R et al. GRADE guidelines 28: Use of GRADE for the assessment of evidence about prognostic factors: Rating certainty in identification of groups of patients with different absolute risks. J Clin Epidemiol 121; 62-70.

Manuscript available from the publisher's website here.