scholarly journals A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

2020 ◽  
Vol 8 (1) ◽  
pp. 318-327
Author(s):  
Mohammad M Islam ◽  
Erik L Heiny
Keyword(s):  
Longitudinal Data ◽  
Statistical Method ◽  
Small Sample ◽  
Sampling Distribution ◽  
Outcome Variable ◽  
Policy Intervention ◽  
Segmented Regression ◽  
Nonparametric Bootstrap ◽  
Intervention Effect ◽  
Robust Statistical Method

Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality of the outcome variable, a large sample size, no autocorrelation in the observations, and a linear trend over time. Also, segmented regression is very sensitive to outliers. In a small sample study, if the outcome variable does not follow a Gaussian distribution, then using segmented regression to estimate the intervention effect leads to incorrect inferences. To address the small sample problem and non-normality in the outcome variable, including outliers, we describe and develop a robust statistical method to estimate the policy intervention effect in a series of longitudinal data. A simulation study is conducted to demonstrate the effect of outliers and non-normality in the outcomes by calculating the power of the test statistics with the segmented regression and the proposed robust statistical methods. Moreover, since finding the sampling distribution of the proposed robust statistic is analytically difficult, we use a nonparametric bootstrap technique to study the properties of the sampling distribution and make statistical inferences. Simulation studies show that the proposed method has more power than the standard t-test used in segmented regression analysis under the non-normality error distribution. Finally, we use the developed technique to estimate the intervention effect of the Istanbul Declaration on illegal organ activities. The robust method detected more significant effects compared to the standard method and provided shorter confidence intervals.

Coping with longitudinal data in research on developmental psychopathology

2006 ◽  
Vol 30 (1) ◽  
pp. 20-25 ◽  
Author(s):  
David A. Cole
Keyword(s):  
Data Collection ◽  
Longitudinal Data ◽  
Developmental Psychopathology ◽  
Careful Consideration ◽  
Two Dimensions ◽  
The Other ◽  
Outcome Variable ◽  
The Third ◽  
Data Analytic ◽  
Over Time

Many outcome variables in developmental psychopathology research are highly stable over time. In conventional longitudinal data analytic approaches such as multiple regression, controlling for prior levels of the outcome variable often yields little (if any) reliable variance in the dependent variable for putative predictors to explain. Three strategies for coping with this problem are described. One involves focusing on developmental periods of transition, in which the outcome of interest may be less stable. A second is to give careful consideration to the amount of time allowed to elapse between waves of data collection. The third is to consider trait-state-occasion models that partition the outcome variable into two dimensions: one entirely stable and trait-like, the other less stable and subject to occasion-specific fluctuations.

scholarly journals Nonparametric inference for interventional effects with multiple mediators

2021 ◽  
Vol 9 (1) ◽  
pp. 172-189
Author(s):  
David Benkeser ◽  
Jialu Ran
Keyword(s):  
Interval Estimation ◽  
Small Sample ◽  
Parametric Models ◽  
Nonparametric Bootstrap ◽  
Mediation Effects ◽  
Specific Effects ◽  
Convergence Results ◽  
Large Sample Theory ◽  
Scientific Goal ◽  
Statistical Learning Techniques

Abstract Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation facilitated via the nonparametric bootstrap. We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques to be considered. In particular, we establish weak convergence results that facilitate the construction of closed-form confidence intervals and hypothesis tests and prove multiple robustness properties of the proposed estimators. Simulations show that inference based on large-sample theory has adequate small-sample performance. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.

scholarly journals A Comparison of Mixture Modeling Approaches in Latent Class Models With External Variables Under Small Samples

2017 ◽  
Vol 78 (6) ◽  
pp. 925-951 ◽  
Author(s):  
Unkyung No ◽  
Sehee Hong
Keyword(s):  
Sample Size ◽  
Latent Class ◽  
Latent Class Model ◽  
Mixture Modeling ◽  
Small Sample ◽  
Outcome Variable ◽  
Parameter Estimates ◽  
Class Model ◽  
Modeling Approaches ◽  
Distal Outcome

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class model with three predictor variables and a latent class model with one distal outcome variable. For the simulation, data were generated under the conditions of different sample sizes (100, 200, 300, 500, 1,000), entropy (0.6, 0.7, 0.8, 0.9), and the variance of a distal outcome (homoscedasticity, heteroscedasticity). For evaluation criteria, parameter estimates bias, standard error bias, mean squared error, and coverage were used. Results demonstrate that the three-step approaches produced more stable and better estimations than the other approaches even with a small sample size of 100. This research differs from previous studies in the sense that various models were used to compare the approaches and smaller sample size conditions were used. Furthermore, the results supporting the superiority of the three-step approaches even in poorly manipulated conditions indicate the advantage of these approaches.

scholarly journals RESILIENT: A robust statistical method for estimating multiple VOC sources from limited field measurements

Heliyon ◽  
2020 ◽  
Vol 6 (10) ◽  
pp. e05296
Author(s):  
Anand Kakarla ◽  
Asif Qureshi ◽  
Shashidhar Thatikonda ◽  
Swades De ◽  
Soumya Jana
Keyword(s):  
Statistical Method ◽  
Field Measurements ◽  
Robust Statistical Method

A new robust statistical method for treatment planning systems validation using experimental designs

2016 ◽  
Vol 32 ◽  
pp. 202
Author(s):  
S. Dufreneix ◽  
K. Briand ◽  
C. Di Bartolo ◽  
C. Legrand ◽  
M. Bremaud ◽  
...  
Keyword(s):  
Statistical Method ◽  
Treatment Planning ◽  
Experimental Designs ◽  
Planning Systems ◽  
Treatment Planning Systems ◽  
Robust Statistical Method

Small sample GEE estimation of regression parameters for longitudinal data

2014 ◽  
Vol 33 (22) ◽  
pp. 3869-3881 ◽  
Author(s):  
Sudhir Paul ◽  
Xuemao Zhang
Keyword(s):  
Longitudinal Data ◽  
Small Sample ◽  
Regression Parameters

scholarly journals Vitamin D2 and D3 Supplementation Effect on Serum 25(OH)D Levels: A Meta-Analysis

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1782-1782
Author(s):  
Meline Chakalian ◽  
Joyce Cao ◽  
Jiang Hu ◽  
Casey Vanous ◽  
Simon Sum
Keyword(s):  
Vitamin D ◽  
Meta Analysis ◽  
Small Sample ◽  
Relative Potency ◽  
Health Concern ◽  
Outcome Variable ◽  
Vitamin D2 ◽  
Baseline Serum ◽  
Meta Regression ◽  
The Impact

Abstract Objectives Vitamin D insufficiency is a global health concern that affects nearly 50% of the population worldwide. Growing demand for vegan/vegetarian products has aroused interest in the plant-sourced D2 form for use in dietary supplements. However, vitamin D2’s ability to raise serum 25(OH)D levels in relation to D3 among existing scientific literature is inconclusive. This study sought to compare vitamin D2 to D3 in increasing serum 25(OH)D levels in order to better understand the relative potency and dosage required to address vitamin D insufficiencies. Methods PubMed and Embase databases were searched through July of 2018. Randomized controlled trials comparing D2 and D3 supplementation of equivalent dosages and the resulting increase in serum 25(OH)D levels in adults were eligible for this meta-analysis. A meta regression was conducted to compare the impact of both vitamin D forms on serum 25(OH)D levels. The outcome variable evaluated was the serum 25(OH)D levels. Results Nine RCTs (n = 628) with vitamin D dose ranging from 10 mcg per day to 1250 mcg per week, and an intervention duration from 2 to 16 weeks were eligible. Subjects included healthy adults as well as those with chronic kidney disease. There was substantial heterogeneity among the studies (I2 = 78.07%). The meta-regression showed vitamin D supplementation regardless of form was effective in raising serum 25(OH)D levels (P < 0.0001). The mean effect size expressed as the standardized mean difference (SMD) from baseline serum 25(OH)D levels was 1.16 [95% CI: 0.83, 1.49] for D2 and 1.52 [95% CI: 0.99, 2.04] for D3. While there was a trend of greater increase caused by D3 numerically, the difference between D2 and D3 was not statistically significant. When duration and frequency of supplementation were examined, similar trends of non-significant greater increases for D3 relative to D2 were observed. Conclusions This research shows both vitamin D2 and D3 supplementation can significantly increase serum 25(OH)D levels. Though the results did not reach statistical significance, there is a consistent trend of vitamin D3 offering additional effectiveness relative to D2. The high heterogeneity across studies and small sample size likely contributed to the non-significant results and limited the ability to identify a quantitative relative potency that can be used for a D2 dosage recommendation. Funding Sources None.

Developing a predictive model for disease status in breast cancer patients using Th1 and Th2 serum cytokine profiles

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 10078-10078 ◽  
Author(s):  
S. Khoo ◽  
T. Kao ◽  
Z. A. Dehqanzada ◽  
C. E. Storrer ◽  
K. A. Harris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Predictive Value ◽  
Small Sample ◽  
Nodal Status ◽  
Outcome Variable ◽  
Breast Cancer Patients ◽  
Serum Cytokine ◽  
Gm Csf ◽  
Cytokine Levels ◽  
Potential Biomarkers

10078 Background: Using the Luminex multiplex assay, we have reported significant correlations between serum levels of MCP-1, eotaxin, and IL-6 and disease characteristics in breast cancer (BCa) patients. We now examine the potential of utilizing a general cytokine profile to develop a statistical model to predict certain disease states in these patients. Methods: Sera from 36 BCa patients (24 node-negative, 12 node-positive, 12 normals) were analyzed using the Luminex assay for levels of 21 cytokines (IL-1α, 1b, 2, 4, 5, 6, 8, 10, 12, 13, 15, 17, IFN-γ, G-CSF, GM-CSF, TNF-α, IP-10, MIP-1α, RANTES, MCP-1, eotaxin). Logistic regression models were used to assess if a binary outcome variable (Y) can be predicted by using serum cytokine levels (X). The area under a receiver operating characteristic (ROC) curve (c) was used to assess the potential utility of a biomarker. The larger the value of c, the better the biomarker. Results: MCP-1 was found to be a possible predictor of the presence of BCa while other potential biomarkers were IL-13, MIP-1α and eotaxin. The higher the MCP-1 level, the greater the likelihood that the patient would have BCa. Similar relationships applied to the other potential biomarkers. Among BCa patients, GM-CSF seemed to be a good predictor of nodal status with lower levels of GM-CSF predicting positive nodes. Other potential biomarkers with a similar expression pattern for nodal status were MCP-1, IL-6 and IL-5. Due to small sample sizes, we were unable to examine a potential “panel” of cytokines to develop a prognostic algorithm based on serum analysis. Conclusions: MCP-1, which was previously shown to be elevated in BCa, may also have some predictive value linking the presence of disease and disease severity as measured by nodal status. Other prominent cytokines from earlier studies (MIP-1α, eotaxin, IL-6) also displayed some possible predictive value. Our results warrant studying a larger population in order to establish a unified prognostic formula for BCa based on serum cytokine levels. [Table: see text] No significant financial relationships to disclose.

Sign in / Sign up

Export Citation Format

Share Document