Comparison of statistical methodologies used to estimate the treatment effect on time-to-event outcomes in observational studies

The use of real-world data became more and more popular in the pharmaceutical industry. The impact of real-world evidence is now well emphasized by the regulatory authorities. Indeed, the analysis of this type of data can play a key role for treatment efficacy and safety. The aim of this work is to assess various methods and give guidance on the comparisons of drugs, mostly with respect to time-to-event data, in non-randomized studies with potentially confounding variables. For that purpose, several statistical methodologies are compared based on simulation studies. These methodologies belong to family classes of methods that are widely used for this type of problem: regression, matching, weighting and subclassification methods. The evaluation criteria used to compare methods performances are the relative bias, the mean square error, the coverage probability and the width of the confidence interval. In this paper, we consider different scenarios of dataset features in order to study the effect of the sample size, the number of covariates and the magnitude of the treatment effect on the statistical methodologies performances. These statistical analyses are conducted within a proportional hazard model framework. Furthermore, we highlight the advantage of using techniques to identify relevant covariates for time-to-event outcomes by comparing two variable selection methods under a frequentist and a Bayesian inference. Based on simulation results, recommendations on each of the family of methods are provided to guide decision making.

IMPACT OF THREE DIFFERENT MATCHING METHODS ON PATIENT SET-UP ERROR IN X-RAY VOLUMETRIC IMAGING FOR HEAD AND NECK CANCER

Impact of three different matching methods for delivery of Volumetric Modulated Arc Therapy (VMAT) in Cone-beam computed tomography (CBCT) on patient set-up error. As per institutional imaging protocol, 300 CBCT scans of 20 VMAT head and neck cancer patients treated with 60 Gy/30 fractions were chosen for the present study. Approved CT images of the plan were registered as a reference with the CBCT images on board. Grey-scale matching (GM), manual matching (MM), and bone matching (BM) between on-board CBCT and reference CT images were used to assess patient translation errors. Patient positioning verification was evaluated using the Clip-box registration in all three matching methods. Using the GM approach as a reference point, two additional matchings were rendered in offline mode using BM and MM. For analysis, random error (σ), systematic error (∑), maximum error (E) mean set-up error (M), mean displacement vector (R), matching time (Mt), and multiple comparisons using Post hoc Tukey's HSD test were performed. In MM, less random and systematic errors were found than in GM and BM with an insignificant difference (p > 0.05) Compared to BM and GM, the maximum error, mean set-up error, and displacement vector were marginally less in MM (p > 0.05). In MM, an increased Mt relative to BM and GM was observed (p > 0.05). Furthermore, an insignificant difference in set-up error was revealed in a multiple comparison test (p > 0.05). Any of the three matching methods can be used during CBCT to check patient translation errors for the delivery of the VMAT head and neck patients.

Acute health shocks and labour market outcomes: Evidence from the post crash era

We investigate the labour supply response to an acute health shock for individuals of all working ages, in the post crash era, combining coarsened exact matching and entropy balancing to preprocess data prior to undertaking parametric regression. Identification exploits uncertainty in the timing of an acute health shock, defined by the incidence of cancer, stroke, or heart attack, based on data from Understanding Society. The main finding implies a substantial increase in the baseline probability of labour market exit along with reduced hours and earnings. Younger workers display a stronger labour market attachment than older counterparts, conditional on a health shock. Impacts are stronger for women, older workers, and those who experience more severe limitations and impairments. This is shown to be robust to a broad range of approaches to estimation. Sensitivity tests based on pre-treatment outcomes and using future health shocks as a placebo treatment support our identification strategy.

Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research

OBJECTIVE

Examining covariate balance is the prescribed method for determining the degree to which propensity score methods should be successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (similar to a disease risk score), to determine which balance measures best correlate with bias in the treatment effect estimate.

STUDY DESIGN AND SETTING

The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only.

RESULTS

The absolute standardized mean difference (ASMD) in prognostic scores, the mean ASMD (in covariates), and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations with bias of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification, and the prognostic score measure performed well under a variety of scenarios.

CONCLUSION

Researchers should consider using prognostic score-based balance measures for assessing the performance of propensity score methods for reducing bias in nonexperimental studies.