Causality on longitudinal data: Stable specification search in constrained structural equation modeling

Longitudinal outcome monitoring in patients with chronic gastroduodenal symptoms investigated using the Gastric Alimetry system: study protocol

INTRODUCTION

The Gastric Alimetry platform offers a multimodal assessment of gastric function through body surface gastric mapping (BSGM) and concurrent symptom-tracking via a validated App. We aim to perform a longitudinal cohort study to examine the impact of Gastric Alimetry, and changes in clinical management on patient symptoms, quality of life and psychological health.

METHODS AND ANALYSIS

This is a prospective multicentre longitudinal observational cohort study of participants with chronic gastroduodenal symptoms. Consecutive participants undergoing Gastric Alimetry will be invited to participate. Quality of life will be assessed via EuroQol-5D and the Patient Assessment of Upper Gastrointestinal Disorders-Quality of Life score. Gastrointestinal symptoms will be assessed via the Patient Assessment of Upper Gastrointestinal Symptom Severity index, and the Gastroparesis Cardinal Symptom Index. Psychometrics will be assessed, including anxiety via the General Anxiety Disorder-7, perceived stress using the Perceived Stress Scale 4, and depression via the Patient Health Questionnaire 9. Clinical parameters including diagnoses, investigations and treatments (medication and procedures) will also be captured. Assessments will be made the week after the BSGM test, at 30 days, 90 days, 180 days and 360 days thereafter. The primary outcome is feasibility of longitudinal follow-up of a cohort that have undergone Gastric Alimetry testing; from which patients' continuum of care can be characterised. Secondary outcomes include changes in patient-reported symptoms, quality of life and psychometrics (anxiety, stress and depression). Inferential causal analyses will be performed at the within patient level to explore causal associations between treatment changes and clinical outcomes. The impact of Gastric Alimetry on clinical management will also be captured.

ETHICS AND DISSEMINATION

The protocol has been approved in Aotearoa New Zealand by the Auckland Health Research Ethics Committee. Results will be submitted for conference presentation and peer-reviewed publication.

Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research

OBJECTIVE

Alzheimer's disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline.

METHODS

An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart).

RESULTS

EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual-spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach.

CONCLUSION

The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.

Potential mechanisms of the fatigue-reducing effect of cognitive-behavioral therapy in cancer survivors: Three randomized controlled trials

OBJECTIVE

Fatigue is a common symptom among cancer survivors that can be successfully treated with cognitive-behavioral therapy (CBT). Insights into the working mechanisms of CBT are currently limited. The aim of this study was to investigate whether improvements in targeted cognitive-behavioral variables and reduced depressive symptoms mediate the fatigue-reducing effect of CBT.

METHODS

We pooled data from three randomized controlled trials that tested the efficacy of CBT to reduce severe fatigue. In all three trials, fatigue severity (checklist individual strength) decreased significantly following CBT. Assessments were conducted pre-treatment and 6 months later. Classical mediation analysis testing a pre-specified model was conducted and its results compared to those of causal discovery, an explorative data-driven approach testing all possible causal associations and retaining the most likely model.

RESULTS

Data from 250 cancer survivors (n = 129 CBT, n = 121 waitlist) were analyzed. Classical mediation analysis suggests that increased self-efficacy and decreased fatigue catastrophizing, focusing on symptoms, perceived problems with activity and depressive symptoms mediate the reduction of fatigue brought by CBT. Conversely, causal discovery and post-hoc analyses indicate that fatigue acts as mediator, not outcome, of changes in cognitions, sleep disturbance and depressive symptoms.

CONCLUSIONS

Cognitions, sleep disturbance and depressive symptoms improve during CBT. When assessed pre- and post-treatment, fatigue acts as a mediator, not outcome, of these improvements. It seems likely that the working mechanism of CBT is not a one-way causal effect but a dynamic reciprocal process. Trials integrating intermittent assessments are needed to shed light on these mechanisms and inform optimization of CBT.

Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models

Rationale & objective

Early prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD) currently use Cox models including baseline estimated glomerular filtration rate (eGFR) only. Alternative approaches include a Cox model that includes eGFR slope determined over a baseline period of time, a Cox model with time varying GFR, or a joint modeling approach. We studied if these more complex approaches may further improve ESKD prediction.

Study design

Prospective cohort.

Setting & participants

We re-used data from two CKD cohorts including patients with baseline eGFR >30ml/min per 1.73m². MASTERPLAN (N = 505; 55 ESKD events) was used as development dataset, and NephroTest (N = 1385; 72 events) for validation.

Predictors

All models included age, sex, eGFR, and albuminuria, known prognostic markers for ESKD.

Analytical approach

We trained the models on the MASTERPLAN data and determined discrimination and calibration for each model at 2 years follow-up for a prediction horizon of 2 years in the NephroTest cohort. We benchmarked the predictive performance against the Kidney Failure Risk Equation (KFRE).

Results

The C-statistics for the KFRE was 0.94 (95%CI 0.86 to 1.01). Performance was similar for the Cox model with time-varying eGFR (0.92 [0.84 to 0.97]), eGFR (0.95 [0.90 to 1.00]), and the joint model 0.91 [0.87 to 0.96]). The Cox model with eGFR slope showed the best calibration.

Conclusion

In the present studies, where the outcome was rare and follow-up data was highly complete, the joint models did not offer improvement in predictive performance over more traditional approaches such as a survival model with time-varying eGFR, or a model with eGFR slope.

Comparing causal techniques for rainfall variability analysis using causality algorithms in Iran

Causal analysis (CA) is a strong quantitative approach whose mechanisms have climatic predictions. In this study, we studied the patterns of causality (PC) on the effect of rainfall (ER) using climatic series collected from 170 stations for the period 1975-2014 in Iran. Next, we predicted the causal relationships of climatic variables using causal models, including first-generation techniques (FGT), second-generation techniques (SGT), third-generation techniques (TGT), and causal hybrid techniques (CHT). Then, we estimated the causal models using partial squares algorithms (PSA), mechanical equations modeling algorithms (MEMA) such as exploratory and confirmatory methods, and spatial variability methods such as geostatistics and spatial statistical methods. Finally, we evaluated the quality of the methods using the goodness of fit indices, including absolute fit indices (AFI), comparative fit indices (CFI), and parsimonious fit indices (PFI). The results showed that CHT algorithm more suitably predicted the climatic spatiotemporal effect variability (SEV) by extracting direct, indirect, and total effects of climatic variables. Based on the CHT algorithm, the highest and lowest effect values were observed in total effects of winter rainfall (0.98) and summer rainfall variables (0.1), respectively. The SEV ranged from 0.8 to 0.98 for the winter rainfall total effects of CHT in Iran. Using CHT, most of the predicted SEV, particularly the rainfall series, displayed SEV varying from 80% to 98% of the winter rainfall total effects to the annual rainfall in Iran. Similarly, based on the CHT, the highest and lowest SEV values were in western, eastern, and southern regions and in central regions, respectively. In addition, the SEV varied within the range of 0.6-0.74 (varying from 60% to 74% for the autumn rainfall total effects of the annual rainfall in Iran) for the autumn rainfall total effects in Iran. Finally, the SEV of this type of analytical pattern as well as designated subject of CA applications in the atmospheric science and environmental science are discussed.