Nevin Manimala

Biom J. 2021 Apr 19. doi: 10.1002/bimj.202000312. Online ahead of print.

ABSTRACT

For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of the disease. However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying-coefficient regression. However, this is challenging when there is a pre-specified group structure among variables. We propose a novel variable selection method for a varying-coefficient regression with such structured variables and provide a publicly available R package svreg for implementation of our method. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease severity.

PMID:33871905 | DOI:10.1002/bimj.202000312

Stress Health. 2021 Mar 26. doi: 10.1002/smi.3045. Online ahead of print.

ABSTRACT

Education actively helps us develop our well-being and health, but postgraduate students are at high risk of depression. The prevalence of depression symptoms varies from 6.2% to 84.7% among them, and its changes throughout the years remains unclear. The present study aimed to estimate the real prevalence of depression symptoms among postgraduate students and the changes from 1980 to 2020. Thirty-seven primary studies with 41 independent reports were included in the meta-analysis (none reports were in high-quality, three were medium-to-high quality, 20 were low-to-medium quality, and 18 were low-quality), involving 27,717 postgraduate students. The pooled prevalence of overall, mild, moderate, and severe depression symptoms was 34% (95% CI: 28-40, I² = 98.6%), 27% (95% CI: 22-32, I² = 85.8%), 13% (95% CI: 8-21, I² = 97.3%), and 8% (95% CI: 6-11, I² = 81.0%), respectively. Overall, the prevalence of depression symptoms remained relatively constant through the years following 1980 (overall: β = -0.12, 95% CI: [-0.39, 0.15], p = 0.39; mild: β = 0.24, 95% CI: [-0.02, 0.51], p = 0.07; moderate: β = -0.24, 95% CI: [-0.75, 0.26], p = 0.34; severe: β = 0.13, 95% CI: [-0.25, 0.51], p = 0.50). Doctoral students experienced more depressive symptoms than did master’s students (43% vs. 27%; Q = 2.23, df = 1, p = 0.13), and studies utilising non-random sampling methods reported a higher prevalence of mild depression and lower moderate depression symptoms than those that used random sampling (overall: 34% vs. 29%; Q = 0.45, df = 1, p = 0.50; mild: 29% vs. 21%; Q = 1.69, df = 1, p = 0.19; moderate: 16% vs. 25%; Q = 1.79, df = 1, p = 0.18; severe: 8% vs. 9%; Q = 0.13, df = 1, p = 0.72) despite these differences was not statistically significant. The prevalence of depression symptoms was moderated by the measurements and the quality of primary studies. More than one-third of postgraduates reported depression symptoms, which indicates the susceptibility to mental health risk among postgraduates. School administrators, teachers, and students should take joint actions to prevent mental disorders of postgraduates from increasing in severity.

PMID:33871902 | DOI:10.1002/smi.3045

Biom J. 2021 Apr 19. doi: 10.1002/bimj.202000240. Online ahead of print.

ABSTRACT

We propose a mathematical model based on probability theory to optimize COVID-19 testing by a multistep batch testing approach with variable batch sizes. This model and simulation tool dramatically increase the efficiency and efficacy of the tests in a large population at a low cost, particularly when the infection rate is low. The proposed method combines statistical modeling with numerical methods to solve nonlinear equations and obtain optimal batch sizes at each step of tests, with the flexibility to incorporate geographic and demographic information. In theory, this method substantially improves the false positive rate and positive predictive value as well. We also conducted a Monte Carlo simulation to verify this theory. Our simulation results show that our method significantly reduces the false negative rate. More accurate assessment can be made if the dilution effect or other practical factors are taken into consideration. The proposed method will be particularly useful for the early detection of infectious diseases and prevention of future pandemics. The proposed work will have broader impacts on medical testing for contagious diseases in general.

PMID:33871898 | DOI:10.1002/bimj.202000240

Methods Mol Biol. 2021;2249:281-305. doi: 10.1007/978-1-0716-1138-8_16.

ABSTRACT

Performing well-powered, randomized, controlled trials is of fundamental importance in clinical research. The goal of sample size calculations is to assure that statistical power is sufficiently high when the probability of falsely rejecting a true null hypothesis (type I error) is kept acceptably small. This chapter overviews the fundamental of sample size calculation for standard types of outcomes for 2-group studies. It also considers (1) the problem of determining the size of the treatment effect that a study should be designed to detect, (2) modifications to sample size calculations to account for loss to follow-up and nonadherence, (3) options that can be used when initial calculations indicate that the feasible sample size is insufficient to provide adequate power, (4) implications of using multiple primary end points. In addition, a discussion of cluster randomized trials is provided. Sample size estimates for longitudinal cohort studies must take account of confounding by baseline factors.

PMID:33871850 | DOI:10.1007/978-1-0716-1138-8_16

Methods Mol Biol. 2021;2249:261-280. doi: 10.1007/978-1-0716-1138-8_15.

ABSTRACT

Biomarkers are characteristics that are measured as indicators of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarkers may serve a number of important uses, particularly in diagnosis and prognosis of disease, and as surrogates for clinical outcomes of disease (i.e., outcomes that measure how patient survives, functions, or feels). Establishing the validity of a given biomarker for a specific role requires the conduct of carefully designed clinical studies in which the biomarker and the outcome of interest are measured independently. The design and analysis of such studies is discussed. Surrogate outcomes in clinical trials consist of events or biomarkers intended to reflect important clinical outcomes. Surrogate outcomes may offer advantages in providing statistically robust estimates of treatment effects with smaller sample sizes. However, to be useful, surrogate outcomes have to be validated to ensure that the effect of therapy on them truly reflects the effect of therapy on the important clinical outcomes of interest.

PMID:33871849 | DOI:10.1007/978-1-0716-1138-8_15

Methods Mol Biol. 2021;2249:229-245. doi: 10.1007/978-1-0716-1138-8_13.

ABSTRACT

The study of patient-reported outcomes, now common in clinical research, had its origins in social and scientific developments during the latter twentieth century. Patient-reported outcomes comprise functional and health status, health-related quality of life, and quality of life. The terms overlap and are used inconsistently, and these terms should be distinguished from expressions of preference regarding health states. Regulatory standards from the USA and European Union provide some guidance regarding reporting of patient-reported outcomes. Determining that patient-reported outcomes measurement is important depends in part on the balance between subjective and objective outcomes of the health problem under study. Instrument selection depends to a large extent on practical considerations. A number of instruments can be identified that are frequently used in particular clinical situations. The domain coverage of commonly used generic short forms varies substantially. Individualized measurement of quality of life is possible, but resource intensive. Focus groups are useful, not only for scale development but also to confirm the appropriateness of existing instruments.Under classical test theory, validity and reliability are the critical characteristics of tests. Under item response theory, validity remains central, but the focus moves from the reliability of scales to the relative levels of traits in individuals and items’ relative difficulty. Plans for clinical studies should include an explicit model of the relationship of patient-reported outcomes to other parameters, as well as define the magnitude of difference in patient-reported outcomes that will be considered important. It is particularly important to minimize missing patient-reported outcome data; to a limited extent, a variety of statistical techniques can mitigate the consequences of missing data.

PMID:33871847 | DOI:10.1007/978-1-0716-1138-8_13

Methods Mol Biol. 2021;2249:213-227. doi: 10.1007/978-1-0716-1138-8_12.

ABSTRACT

When analyzing the results of a trial, the primary outcome variable must be kept in clear focus. In the analysis plan, consideration must be given to comparing the characteristics of the subjects, taking account of differences in these characteristics, intention-to-treat analysis, interim analyses and stopping rules, mortality comparisons, composite outcomes, research design including run-in periods, factorial, stratified and crossover designs, number needed to treat, power issues, multivariate modeling, subgroup analysis, competing risks, and hypothesis-generating analyses.

PMID:33871846 | DOI:10.1007/978-1-0716-1138-8_12

Methods Mol Biol. 2021;2249:103-124. doi: 10.1007/978-1-0716-1138-8_7.

ABSTRACT

Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors’ impact upon outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precisions around the point estimates (Confidence Intervals).

PMID:33871841 | DOI:10.1007/978-1-0716-1138-8_7

Methods Mol Biol. 2021;2249:1-16. doi: 10.1007/978-1-0716-1138-8_1.

ABSTRACT

Clinical epidemiology is the science of human disease investigation with a focus on diagnosis, prognosis, and treatment. The generation of a reasonable question requires definition of patients, interventions, controls, and outcomes. The goal of research design is to minimize error, ensure adequate samples, measure input and output variables appropriately, consider external and internal validities, limit bias, and address clinical as well as statistical relevance. The hierarchy of evidence for clinical decision-making places randomized controlled trials (RCT) or systematic review of good-quality RCTs at the top of the evidence pyramid. Prognostic and etiologic questions are best addressed with longitudinal cohort studies.

PMID:33871835 | DOI:10.1007/978-1-0716-1138-8_1

Spine Deform. 2021 Apr 19. doi: 10.1007/s43390-020-00283-2. Online ahead of print.

ABSTRACT

OBJECTIVES: To study factors causing postoperative change of PI after surgical correction of ASD and to assess the effect of this variability on postoperative PI-LL mismatch.

BACKGROUND: PI is used as an individual constant to define lumbar lordosis (LL) correction goal (PI-LL < 10). Postoperative changes of PI were shown but with opposite vectors. The impact of the PI variability on the postoperative PI-LL has not been studied.

METHODS: The medical and radiographic data analyzed for patients who underwent long posterior instrumented spinal fusion. Inclusion criteria are age, ≥ 20 years old; ASD due to degenerative disk disease (DDD) or scoliosis (DS); ≥ 3 levels fused; and 2-year follow-up or revision. Studied parameters are LL (L1-S1), PI, sacral slope (SS), pelvic tilt (PT), and PI-LL. Measurement error and postoperative changes were defined. Statistical analysis includes ANOVA, correlation, regression, and risk assessment by odds ratio; P ≤ 0.05 considered statistically significant.

RESULTS: Eighty patients were included: mean age, 62.4 years-old (SD, 11.1); female, 63.7%; mean body mass index (BMI), 27.1 (SD, 5.6). Distribution of patients by follow-ups includes preoperative 100%; postoperative (1-3 weeks), 100%; 11-13 months. 90%; 22-26 months, 58%; and revision: 24%. Pre- versus postoperative PI (∆PI) changed both positively and negatively and the absolute value of change|∆PI| exceeded measurement error (P ≤ 0.05) reaching as high as 31°, and progressed with time; R² dropped from 0.73 to 0.45 (P < 0.001); ∆PI depended on disproportional changes of SS and PT, preoperative PI, and change of LL. Obesity, DS, and absence of sacroiliac fixation increased |∆PI|. The risk of LL insufficient correction (PI-LL > 10°) associated with a |∆PI|> 6°, P = 0.05. Sacroiliac fixation diminished PI variability only during the first postoperative year.

CONCLUSION: Preoperative variability and postoperative instability of PI diminish the applicability of the PI-LL < 10° goal to plan correction of LL. An alternative method is offered.

LEVEL OF EVIDENCE: IV.

PMID:33871832 | DOI:10.1007/s43390-020-00283-2