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Rediscovering drug side effects: the impact of analytical assumptions on the detection of associations in EHR data.

Diaz-Garelli JF, Bernstam EV - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect.Categorization of the exposure variable improved side effect detection but negatively impacted model fit.To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX.

ABSTRACT
Large clinical datasets can be used to discover and monitor drug side effects. Many previous studies analyzed symptom data as discrete events. However, some drug side effects are inferred from continuous variables such as weight or blood pressure. These require additional assumptions for analysis. For example, we can define positive/negative thresholds and time windows within which we expect to see the side effect. In this paper, we discuss the impact of such assumptions on the ability to detect known continuous drug side effects using statistical and visualization techniques. Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect. Categorization of the exposure variable improved side effect detection but negatively impacted model fit. To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.

No MeSH data available.


Related in: MedlinePlus

The absence of windowing may mask relationships. Adding temporal window constraints impacts visualization of the effect of drug in weight over time. Left: All weight data available after prednisone prescription is shown. The data shows a downward trend, which is contrary to the expected outcome. Right: When the data are windowed within the first 90 days after prescription, a clear upward trend is detectable.
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f1-2087550: The absence of windowing may mask relationships. Adding temporal window constraints impacts visualization of the effect of drug in weight over time. Left: All weight data available after prednisone prescription is shown. The data shows a downward trend, which is contrary to the expected outcome. Right: When the data are windowed within the first 90 days after prescription, a clear upward trend is detectable.

Mentions: The raw visualization showed a downward trend when the considered data spanned over several years (Fig. 1); stratifying this graph by exposures (high-low) also yielded downward trends for both groups. Thus, this set of assumptions would lead us to conclude that prednisone led to weight loss. Setting up an upper limit to 90 days after the first prescription reversed this phenomenon (Fig. 1), revealing a slope of 0.00012%/day increase (p=0.0004). Stratifying by exposure, we found significant positive slopes for both groups (Fig 2). For high exposure, we found a slope of 0.000388%/day (p<0.0001) and low exposure revealed a slope of 0.000175%/day (p=0.0001).


Rediscovering drug side effects: the impact of analytical assumptions on the detection of associations in EHR data.

Diaz-Garelli JF, Bernstam EV - AMIA Jt Summits Transl Sci Proc (2015)

The absence of windowing may mask relationships. Adding temporal window constraints impacts visualization of the effect of drug in weight over time. Left: All weight data available after prednisone prescription is shown. The data shows a downward trend, which is contrary to the expected outcome. Right: When the data are windowed within the first 90 days after prescription, a clear upward trend is detectable.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525264&req=5

f1-2087550: The absence of windowing may mask relationships. Adding temporal window constraints impacts visualization of the effect of drug in weight over time. Left: All weight data available after prednisone prescription is shown. The data shows a downward trend, which is contrary to the expected outcome. Right: When the data are windowed within the first 90 days after prescription, a clear upward trend is detectable.
Mentions: The raw visualization showed a downward trend when the considered data spanned over several years (Fig. 1); stratifying this graph by exposures (high-low) also yielded downward trends for both groups. Thus, this set of assumptions would lead us to conclude that prednisone led to weight loss. Setting up an upper limit to 90 days after the first prescription reversed this phenomenon (Fig. 1), revealing a slope of 0.00012%/day increase (p=0.0004). Stratifying by exposure, we found significant positive slopes for both groups (Fig 2). For high exposure, we found a slope of 0.000388%/day (p<0.0001) and low exposure revealed a slope of 0.000175%/day (p=0.0001).

Bottom Line: Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect.Categorization of the exposure variable improved side effect detection but negatively impacted model fit.To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX.

ABSTRACT
Large clinical datasets can be used to discover and monitor drug side effects. Many previous studies analyzed symptom data as discrete events. However, some drug side effects are inferred from continuous variables such as weight or blood pressure. These require additional assumptions for analysis. For example, we can define positive/negative thresholds and time windows within which we expect to see the side effect. In this paper, we discuss the impact of such assumptions on the ability to detect known continuous drug side effects using statistical and visualization techniques. Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect. Categorization of the exposure variable improved side effect detection but negatively impacted model fit. To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.

No MeSH data available.


Related in: MedlinePlus