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Adjusted logistic propensity weighting methods for population inference using nonprobability volunteer-based

Lingxiao Wang1, Richard Valliant1,2, Yan Li1

  • 1The Joint Program in Survey Methodology, University of Maryland, College Park, Maryland, USA.

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|July 5, 2021
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Summary

This study introduces an adjusted logistic propensity weighting (ALP) method for more accurate population inferences from nonprobability samples. The ALP method provides unbiased estimators, improving health research with volunteer-based data.

Keywords:
finite population inferencenonprobability samplepropensity score weightingsurvey samplingvariance estimation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Population Health

Background:

  • Nonprobability volunteer-based samples are common in epidemiologic studies due to practical constraints.
  • These samples limit the ability to make accurate finite population (FP) inferences because they lack representativeness.
  • Existing inverse propensity score weighting methods struggle with accurate participation rate estimation.

Purpose of the Study:

  • To develop a novel method for estimating participation rates in nonprobability samples.
  • To enable more reliable population-level inferences from volunteer-based studies.
  • To provide an efficient and implementable weighting strategy for epidemiologic research.

Main Methods:

  • Proposed an adjusted logistic propensity weighting (ALP) method to estimate nonprobability sample unit participation rates.
  • Incorporated scaling of survey sample weights to enhance estimator efficiency.
  • Developed Taylor linearization variance estimators to account for all sources of variability in FP mean estimation.

Main Results:

  • The ALP method produces approximately unbiased estimators for population quantities, irrespective of the nonprobability sample rate.
  • The method is easily implemented using available software.
  • Empirical evaluation using NHANES III and NHIS data demonstrated the method's utility in estimating mortality rates.

Conclusions:

  • The adjusted logistic propensity weighting (ALP) method offers a robust solution for drawing population inferences from nonprobability samples.
  • ALP provides a practical and statistically sound approach to address sampling biases in volunteer-based health research.
  • This method enhances the utility of readily available nonprobability survey data for public health surveillance and analysis.