Opinion ID: 2161593
Heading Depth: 2
Heading Rank: 5

Heading: Appellee's evidence

Text: Dr. Brian MacMahon was appellee's principal witness on epidemiology and statistics. He testified that in evaluating the data on which an epidemiological study is based, one must first calculate the relative risk of the substance tested. Next, one must determine the confidence interval for the study, which sets the range within which the relative risk would fall if the study were repeated many times. Finally, one must look to see if the number 1 falls within the confidence interval. If so, the results of the study are essentially meaningless; if not, the study shows a statistically significant relationship between the substance tested and the effects observed. Dr. MacMahon then reviewed a number of epidemiological studies on Bendectin. Many of them had a relative risk higher than 1, which meant that the risk of having a baby with a birth defect was higher for those mothers who took Bendectin than those who did not. Nevertheless, Dr. MacMahon believed that these studies were meaningless because the number 1 fell within the confidence interval of each of the studies. He therefore concluded that Bendectin was not responsible for the birth defects observed in the studies and, moreover, that it did not cause appellant's birth defects. Appellee relies principally on Dr. MacMahon's testimony and on similar statements by its other expert witnesses in arguing that causation cannot be established without evidence of a statistically significant association between Bendectin and birth defects. There are at least three reasons why this argument lacks merit. First, Dr. MacMahon conceded that there were in fact several studies showing statistically significant associations between Bendectin and various types of birth defects. In a study by a Dr. Cordero, for example, Dr. MacMahon noted that the relative risk for one type of limb reduction effect was statistically significant. The relative risk was 3.88, and the confidence interval did not include the number 1. Other defense witnesses also testified to such statistically significant associations. Therefore, even assuming that a study must be deemed meaningless whenever the number 1 falls within its confidence interval, the jury was made aware of several meaningful studies on which it could base a finding of causation. Second, appellant's rebuttal witness, Dr. Shanna Swan, an expert in biostatistics and epidemiology, testified that the mere fact that the number 1 falls within the confidence interval does not invalidate the study. She cited as an example an epidemiological study by a Dr. Smithells, in which the confidence interval was from 0.71 to 2.61, and the relative risk was 1.36. Dr. Swan testified that the relative risk calculation is the best estimate of the true situation based on [these] data.... A relative risk of 1.36 means that women who take Bendectin have a 36 percent higher risk of having a baby with a birth defect than those who do not. Confidence intervals, she said, are used to predict the range in which the relative risk would fall if the study were repeated several times. Thus, in the Smithells study, the data [are] consistent with the relative risk being as high as 2.61, which means that women who took Bendectin would have a 161 percent higher risk of having a baby with a birth defect than those who did not take Bendectin. Dr. Swan's testimony thus contradicted that of Dr. MacMahon on the question of whether many of the studies which he rejected could be relied upon as evidence that women exposed to Bendectin had a higher risk of giving birth to babies with birth defects. That contradiction was properly left to the jury to resolve. Finally, Dr. MacMahon conceded that statisticians must be careful to weigh other evidence and not to apply their rules rigidly and blindly. On cross-examination, he agreed with Dr. Byron Brown, whom he recognized as an authority on the subject: Q. Do you agree with [Dr. Brown], if the scientist is to go further than reporting his set of data, discussing it as fully as possible and relating it to the body of current knowledge, if he is to regard himself as a decision maker, then he must consider more than error rates? Do you agree with that? A. Yes. Q. He must weigh past evidence in with the data he has accumulated and he must weigh the penalties or costs involved in making the various types of error. Do you agree with that? A. Yes. Q. And Doctor, one of the things you have to consider, then, when you come up with your statistics is what happens if you are wrong. You have to consider that risk, don't you, as a biostatistician? A. Yes. Q. And if the studies that show a relative risk as Smithells did at 36 percent greater chance of a defect, Mitchell, 50 percent, and the other ones that we referred to were right with that relative risk and you are wrong saying they are statistically insignificant, and Bendectin is in fact a teratogen, then the risk is that children are going to be born with limb defects of mothers who take Bendectin, isn't that right? A. That's very hypothetical, but yes.