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S J Kuritz

Publications and source records attributed to S J Kuritz.

5 recordsLinked to original sources
PubMed1988

Summary attributable risk estimation from unmatched case-control data.

We propose an alternative method to obtain summary estimators, variances and confidence intervals for attributable risk measures. This method combines weighted exposure prevalences for cases and controls across strata formed by the cross-classification of relevant covariates to form estimates of attributable risk among the exposed and attributable risk in the target population. The major benefit of this approach over those previously proposed in the literature is that it operates on data summed across strata rather than on estimation of statistics within each stratum. This alternative method for attributable risk measures utilizes the Mantel-Haenszel estimate of an average odds ratio, and can be implemented using the matrix procedure in SAS. This method is appropriate even when the within-stratum sample sizes are too small for other methods to be valid. Simulation results indicate that this method is superior to others with respect to bias and coverage probability for confidence intervals.

Alcohol Drinking↗
PubMed1988

Attributable risk estimation from matched case-control data.

A methodology is proposed for obtaining summary estimators, variances, and confidence intervals for attributable risk measures from data obtained through a case-control study design where one or more controls have been matched to each case. The sampling design for obtaining these data is conceptualized as a simple random sample of cases being equivalent to a simple random sample of matched sets. By combining information across the strata determined by the matched sets, this approach provides all of the benefits associated with the Mantel-Haenszel procedure for the estimators of attributable risk among the exposed and population attributable risk. Asymptotic variances are derived under the assumption that the frequencies of the unique response patterns follow the multinomial distribution. Simulation results indicate that these methods fare very well with respect to bias and coverage probability.

Analysis of Variance↗
PubMed1987

Attributable risk ratio estimation from matched-pairs case-control data.

Explicit formulas are provided for estimating the attributable risk ratio among the exposed and the entire target population utilizing matched-pairs data. Large-sample standard errors and corresponding confidence intervals are provided. These estimates can be obtained from the cross-classification frequencies of matched pairs by disease and exposure status in the usual 2 X 2 table. The key to the development of these formulas lies in recognizing that attributable risk among the exposed is a direct function of the odds ratio, and population attributable risk is a direct function of the odds ratio and exposure prevalence among only the cases (assuming a rare disease). The formulas presented in this paper require only a calculator for computation. The methodology is illustrated with data from a matched-pairs case-control study of oral conjugated estrogens and endometrial cancer.

Contraceptives, Oral↗