Exploring Association Between Individuals’ Stature and Type 2 Diabetes Status: Propensity Score Analysis

In this article, relationship between respondents’ height and occurrence of diabetes has been investigated. This study uses Bangladesh Demographic and Health Survey (BDHS) 2011 data collected from an observational study. Considering height (tall/normal/short) based on percentiles separately for men and women, logistic regression model was fitted to the propensity score (PS)-adjusted weighted data. No significant relationship between respondents’ height and diabetes was observed. We also found that the occurrence of diabetes significantly varies with respect to sex, education level, wealth index, body mass index (BMI), and region/division. As, in general, women are shorter than men by nature, we strongly argue that height categories should be defined separately whenever estimation of the effect of height on some response is of interest.

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Are behavioural approaches feasible and effective in the treatment of type 2 diabetes? A propensity score analysis vs. prescriptive diet

Nutrition Metabolism and Cardiovascular Diseases ◽

10.1016/j.numecd.2008.06.004 ◽

2009 ◽

Vol 19 (5) ◽

pp. 313-320 ◽

Cited By ~ 13

Author(s):

G. Forlani ◽

C. Lorusso ◽

S. Moscatiello ◽

V. Ridolfi ◽

N. Melchionda ◽

...

Keyword(s):

Type 2 Diabetes ◽

Propensity Score ◽

Propensity Score Analysis ◽

Score Analysis

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Home Health Agency Profit Orientation and Risk for Hospitalization: A Propensity Score Analysis of Population Weighted Data

Home Health Care Services Quarterly ◽

10.1080/01621420802320025 ◽

2008 ◽

Vol 27 (3) ◽

pp. 240-257 ◽

Cited By ~ 4

Author(s):

Laura M. Smith ◽

Kate L. Lapane ◽

Mary L. Fennell ◽

Edward A. Miller ◽

Vincent Mor

Keyword(s):

Propensity Score ◽

Propensity Score Analysis ◽

Health Agency ◽

Home Health ◽

Home Health Agency ◽

Score Analysis ◽

Weighted Data

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Propensity Score Analysis with Survey Weighted Data

Journal of Causal Inference ◽

10.1515/jci-2014-0039 ◽

2015 ◽

Vol 3 (2) ◽

pp. 237-249 ◽

Cited By ~ 50

Author(s):

Greg Ridgeway ◽

Stephanie Ann Kovalchik ◽

Beth Ann Griffin ◽

Mohammed U. Kabeto

Keyword(s):

Propensity Score ◽

Treatment Effects ◽

Propensity Score Analysis ◽

Real Data ◽

Common Method ◽

Sampling Weights ◽

Robust Approach ◽

Score Analysis ◽

Weighted Data ◽

Survey Designs

AbstractPropensity score analysis (PSA) is a common method for estimating treatment effects, but researchers dealing with data from survey designs are generally not properly accounting for the sampling weights in their analyses. Moreover, recommendations given in the few existing methodological articles on this subject are susceptible to bias. We show in this article through derivation, simulation, and a real data example that using sampling weights in the propensity score estimation stage and the outcome model stage results in an estimator that is robust to a variety of conditions that lead to bias for estimators currently recommended in the statistical literature. We highly recommend researchers use the more robust approach described here. This article provides much needed rigorous statistical guidance for researchers working with survey designs involving sampling weights and using PSAs.

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Are Type 2 Diabetes Patients Who Self-Monitor Blood Glucose Special? The Role of Confounders in the Observational ROSSO Study

Journal of Diabetes Science and Technology ◽

10.1177/193229680900300633 ◽

2009 ◽

Vol 3 (6) ◽

pp. 1507-1515 ◽

Cited By ~ 2

Author(s):

Hubert Kolb ◽

Stephan Martin ◽

Volker Lodwig ◽

Lutz Heinemann ◽

Werner A. Scherbaum ◽

...

Keyword(s):

Type 2 Diabetes ◽

Blood Glucose ◽

Propensity Score ◽

Propensity Score Analysis ◽

Matched Pair ◽

Term Outcome ◽

Long Term Outcome ◽

Score Analysis

Background: In the German multicenter, retrospective cohort study (ROSSO), those patients with type 2 diabetes who performed self-monitoring of blood glucose (SMBG) had a better long-term clinical outcome. We analyzed whether confounders accounted for the lower rate of clinical events in the SMBG cohort. Methods: ROSSO followed 3268 persons from diagnosis of type 2 diabetes for a mean of 6.5 years. Data were retrieved from patient files of randomly contacted primary care practices. Results: In total, more than 60 potential confounders were documented, including nondisease-associated parameters such as patient's health insurance, marital status, habitation, and characteristics of diabetes centers. There were only modest differences for these parameters between groups with versus without SMBG, and multiple adjustments did not weaken the association of SMBG use with better outcome (odds ratio 0.65, 95% confidence interval 0.53–0.81, p < .001). This was also true for subgroups of patients defined by type of antidiabetes treatment. Propensity score analysis confirmed the association of SMBG use with outcome. Using key baseline parameters, 813 matching pairs of patients were identified. The analysis again showed a better long-term outcome in the SMBG group (hazard ratio 0.67 p = .004). Conclusion: An influence of nonrecognized confounders on better outcome in the SMBG group is rendered improbable by similar results obtained with adjustments for disease-associated or disease-independent parameters, by the analysis of patient subgroups, by propensity score analysis and by performing a matched-pair analysis. The higher flexibility in pharmacological antidiabetes treatment regimens in the SMBG cohort suggests a different attitude of treating physicians and patients in association with SMBG.

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