Even modest prediction accuracy of genomic models can have large clinical utility

Causal models and causal modelling in obesity: foundations, methods and evidence

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Making External Validation Valid for Molecular Classifier Development

PURPOSE

Accurate assessment of a molecular classifier that guides patient care is of paramount importance in precision oncology. Recent years have seen an increasing use of external validation for such assessment. However, little is known about how it is affected by ubiquitous unwanted variations in test data because of disparate experimental handling and by the use of data normalization for alleviating such variations.

METHODS

In this paper, we studied these issues using two microarray data sets for the same set of tumor samples and additional data simulated by resampling under various levels of signal-to-noise ratio and different designs for array-to-sample allocation.

RESULTS

We showed that (1) unwanted variations can lead to biased classifier assessment and (2) data normalization mitigates the bias to varying extents depending on the specific method used. In particular, frozen normalization methods for test data outperform their conventional forms in terms of both reducing the bias in accuracy estimation and increasing robustness to handling effects. We make available our benchmarking tool as an R package on GitHub for performing such evaluation on additional methods for normalization and classification.

CONCLUSION

Our findings thus highlight the importance of proper test-data normalization for valid assessment by external validation and call for caution on the choice of normalization method for molecular classifier development.

Comparing the Ability of Two Comprehensive Clinical Staging Systems to Predict Mortality: EOSS and CMDS

OBJECTIVE

Differences in discriminative and predictive ability for all-cause mortality of two clinical staging systems, the Edmonton Obesity Staging System (EOSS) and Cardiometabolic Disease Staging (CMDS), were estimated.

METHODS

Data for nonpregnant persons aged 40 to 75 years were extracted from the National Health and Nutrition Examination Survey. Predictive and discriminative ability was assessed using pseudo-R² and C-statistics. Median years of life lost were also computed for each score.

RESULTS

Differences in out-of-sample estimates of pseudo-R² and C-statistics (EOSS model as reference) were 0.02 (95% CI: 0.01-0.04) (Kent pseudo-R² ), 0.03 (0.01-0.04) (Royston pseudo-R² ), and 0.02 (0.01-0.02) (C-statistics). The median years of life lost for EOSS scores 2 and 3 (low to high risk) for a reference person were 1.19 and 6.76 years. Those for CMDS scores 1, 2, 3, and 4 (low to high risk) were 1.53, 2.90, 3.91, and 8.51 years. Consistent results from the in-sample estimates were observed.

CONCLUSIONS

CMDS had statistically significantly greater predictive and discriminative ability than EOSS for persons aged 40 to 75. While the clinical relevance of these differences is unknown, CMDS may have greater clinical utility given that it uses fewer items to risk stratify. The clinical relevance and utility need to be studied further.

Comparing the Convergent and Concurrent Validity of the Dynamic Gait Index with the Berg Balance Scale in People with Multiple Sclerosis

Background: Recent clinical guidelines for adults with neurological disabilities suggest the need to assess measures of static and dynamic balance using the Berg Balance Scale (BBS) and Dynamic Gait Index (DGI) as core outcome measures. Given that the BBS measures both static and dynamic balance, it was unclear as to whether either of these instruments was superior in terms of its convergent and concurrent validity, and whether there was value in complementing the BBS with the DGI. Objective: The objective was to evaluate the concurrent and convergent validity of the BBS and DGI by comparing the performance of these two functional balance tests in people with multiple sclerosis (MS). Methods: Baseline cross-sectional data on 75 people with MS were collected for use in this study from 14 physical therapy clinics participating in a large pragmatic cluster-randomized trial. Convergent validity estimates between the DGI and BBS were examined by comparing the partial Spearman correlations of each test to objective lower extremity functional measures (Timed Up and Go (TUG), Six-Minute Walk Test (6MWT), Timed 25-Foot Walk (T25FW) test) and the self-reported outcomes of physical functioning and general health using the 36-Item Short Form Health Survey (SF-36). Concurrent validity was assessed by applying logistic regression with gait disability as the binary outcome (Patient Determined Disease Steps (PDDS) as the criterion measure). The predictive ability of two models, a reduced/parsimonious model including the BBS only and a second model including both the BBS and DGI, were compared using the adjusted coefficient of determinations. Results: Both the DGI and BBS were strongly correlated with lower extremity measures overall as well as across the two PDSS strata with correlations. In PDDS ≤ 2, the difference in the convergence of BBS with TUG and DGI with TUG was −0.123 (95% CI: −0.280, −0.012). While this finding was statistically significant at a type 1 error rate of 0.05, it was not significant (Hommel’s adjusted p-value = 0.465) after accounting for multiple testing corrections to control for the family-wise error rate. The BBS–SF-36 physical functioning correlation was at least moderate and significant overall and across both PDDS strata. However, the DGI–physical functioning score did not have a statistically significant correlation within PDDS ≤ 2. None of the differences in convergent and concurrent validity between the BBS and DGI were significant. The additional variation in 6MWT explained by the DGI when added to a model with the BBS was 7.78% (95% CI: 0.6%, 15%). Conclusions: These exploratory analyses on data collected in pragmatic real-world settings suggest that neither of these measures of balance is profoundly superior to the other in terms of its concurrent and convergent validity. The DGI may not have any utility for people with PDDS ≤ 2, especially if the focus is on mobility, but may be useful if the goal is to provide insight on lower extremity endurance. Further research leveraging longitudinal data from pragmatic trials and quasi-experimental designs may provide more information about the clinical usefulness of the DGI in terms of its predictive validity when compared to the BBS.

GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity

Muller et al. [1] have provided a strong critique of the Genome-Wide Association Studies (GWAS) of body-mass index (BMI), arguing that the GWAS approach for the study of BMI is flawed, and has provided us with few biological insights. They suggest that what is needed instead is a new start, involving GWAS for more complex energy balance related traits. In this invited counter-point, we highlight the substantial advances that have occurred in the obesity field, directly stimulated by the GWAS of BMI. We agree that GWAS for BMI is not perfect, but consider that the best route forward for additional discoveries will likely be to expand the search for common and rare variants linked to BMI and other easily obtained measures of obesity, rather than attempting to perform new, much smaller GWAS for energy balance traits that are complex and expensive to measure. For GWAS in general, we emphasise that the power from increasing the sample size of a crude but easily measured phenotype outweighs the benefits of better phenotyping.