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Naive Hypothesis Testing for Case Series Analysis with Time-Varying Exposure Onset Measurement Error: Inference for Infection-Cardiovascular Risk in Patients on Dialysis




The case series method is useful in studying the relationship between time-varying exposures, such as infections, and acute events observed during the observation periods of individuals. It provides estimates of the relative incidences of events in risk periods (e.g., 30-day period after infections) relative to the baseline periods. When the times of exposure onsets are not known precisely, application of the case series model ignoring exposure onset measurement error leads to biased estimates. Bias-correction is necessary in order to understand the true directions and effect sizes associated with exposure risk periods, although uncorrected estimators have smaller variance. Thus, inference via hypothesis testing based on uncorrected test statistics, if valid, is potentially more powerful. Furthermore, the tests can be implemented in standard software and do not require additional auxiliary data. In this work, we examine the validity and power of naive hypothesis testing, based on applying the case series analysis to the imprecise data without correcting for the error. Based on simulation studies and theoretical calculations, we determine the validity and relative power of common hypothesis tests of interest in case series analysis. In particular, we illustrate that the tests for the global null hypothesis, the overall null hypotheses associated with all risk periods or all age effects are valid. However, tests of individual risk period parameters are not generally valid. Practical guidelines are provided and illustrated with data from patients on dialysis.