Article

Estimating Model-Adjusted Risks, Risk Differences, and Risk Ratios From Complex Survey Data

Statistics and Epidemiology Unit, RTI International, Research Triangle Park, North Carolina 27709-2194, USA.
American journal of epidemiology (Impact Factor: 5.23). 03/2010; 171(5):618-23. DOI: 10.1093/aje/kwp440
Source: PubMed

ABSTRACT

There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead
of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc.,
Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors
show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression
models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically
involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments.
Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions
in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction
terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to
obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate
these concepts.

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    • "Missing values for education and number of chronic conditions were treated as not missing completely at random in the weighted frequency calculation. To evaluate the association between survey year and receipt of preventive services, adjusted prevalence of receipt and marginal prevalence ratio (PR) and the 95% confidence intervals (CI) (Bieler et al., 2010) were calculated for each insurance type using multivariable logistic regression controlling for age, gender, race/ethnicity , education, marital status, region, residence, and number of chronic conditions . In order to assess if the association varied by socioeconomic status (Damiani et al., 2011; Hoeck et al., 2014; Sambamoorthi and McAlpine, 2003) and health status, we further conducted stratified analyses by family income and the number of chronic conditions, particularly for adults aged 18–64 years with any private insurance. "
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    • "Tests for trend are reported as Wald F values with DF=(1, >100); DF (denominator) = number of PSUs with relevant observations (1268) minus number of survey strata (2). Predicted marginal risk ratios (model-adjusted risk ratios) were reported from logistic regression instead of odds ratios [35,36]. Risk ratios and odds ratios are close for uncommon outcomes but for more common outcomes odds ratios are more extreme than risk ratios. "
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    ABSTRACT: Abstract Background: Criteria for the diagnosis of alcohol abuse or dependence in DSM-IV or ICD-10 do not include measures of alcohol consumption. However the Alcohol Use Disorders Identification Test (AUDIT) contains three consumption questions (AUDIT-C) plus seven problem questions. The AUDIT-C has often been used as a short screening questionnaire. Here drinking patterns in the past year are analysed, and the AUDIT-C and other combinations of those three questions are related to alcohol problems or diagnoses in the same period. Methods: The 2003-2004 New Zealand Mental Health Survey (N=12,992), a nationally representative survey, included the AUDIT and the Composite International Diagnostic Interview (CIDI) 3.0. Latent class analyses were used to discover patterns of alcohol consumption (AUDIT-C) and patterns of alcohol problems. Cross-tabulations, Receiver Operating Characteristic Curves and logistic regression were used to relate consumption to problems and diagnoses. Results: Analyses indicated that drinking frequency (Q1) was an ineffective screening question. Amount consumed per drinking day (Q2) plus frequency of per-occasion heavy drinking (Q3) was as good as or better than the AUDIT-C, with Q3 alone nearly as good. For a given consumption score, males were only slightly more likely than females to experience negative consequences from their drinking whereas age differences were more substantial. For both sexes and all age groups, a reasonable sensitivity of around 80% was achieved with often rather low specificity for detection of any drinking problems (specificities 57- 81%). However there was higher specificity for detection of multiple problems, or diagnosis (specificities 72-85%). Conclusion: Usual drinking frequency is a poor screening indicator of past year alcohol problems and alcohol diagnoses, and does not improve on frequency of heavy per-occasion drinking, or that question plus usual quantity consumed. Retention of the usual drinking frequency question in the AUDIT-C must be based on considerations apart from its value in screening.
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    • "prevalence of needs improvement – health risk CRF combined with unhealthy MSF). To investigate potential for internal selection bias in CRF, MSF, or urban vs. rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49]. Missings in BMI and WC were not evaluated due to very few missing values in the working sample (BMI: n = 0; WC: n = 9). "
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