M
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August2016

Volume 2-The Informed Prescriber

N5

HECK

o

Be careful with serious bias in epidemiologic studies
Symptoms after HPV vaccine and "frailty exclusion bias"
Actos (pioglitazone) and bladder cancer:"time-related bias" of a new type

CONTENTS （August 2016, Vol. 2, No. 5）

Editorial:
Epidemiologists and biostatisticians, be honest !
Adverse Reactions
Symptoms after HPV vaccine:
Typical "frailty exclusion bias" in Nagoya City study
Critical comments from theoretical viewpoints
Pioglitazone (brand name: Actos) and bladder cancer:
A new type "time-related bias"

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-The Informed Prescriber

Editorial

HECK

Epidemiologists and biostatisticians, be honest!
Translated from the editorial in Med Check-TIP (in Japanese) Jan 2016 ; 16 (63)When new types of substances are introducedis one of the coauthors of this epidemiologic study,

into medicine, an unfortunate result is new types ofwhich was conducted by Kaiser Permanente. In Kaiser

harm from new drugs. In Japan, we have experienced

Permanente's latest report, a new analytical method

several serious drug disasters, including thalidomidethat contains a type of time-related bias is introduced,

phocomelia/embryopathy, clioquinol-induced SMON

and the association between Actos and bladder(Subacute Myelo-Optico-Neuropathy), chloroquine

cancer is found to have disappeared.

retinopathy, HIV infection induced by unheatedblood products, CJD induced by dried dura mater,

The article in this issue clearly explains why such abias was introduced.

and hepatitis C infection induced by fibrinogen and

The epidemiologic studies that the MHLW used asother blood products. In lawsuits against government

evidence for the safety of HPV vaccine have many

and/or pharmaceutical companies that resulted fromlimitations. One mistakenly stated the prevalence

these disasters, causal associations between the drugsof autoimmune disease to be one-20th of its actualand the diseases were established and recognized.

usual prevalence. Other studies compared healthy

However, more recent drug disasters caused by

vaccinees with non-vaccinees who may have beensome products introduced since around 2000 seem tomore frail as a group (see p17 in this issue). It is

be different from the above cases in that confirmation

known in epidemiologic research that such bias isof causality is very difficult. One example is acutecalled "healthy vaccinee effects", or inversely, "frailty

respiratory injury associated with the use of Iressa

selection bias". We call this type of bias “frailty(gefitinib, an anti-cancer drug for non-small cell lungexclusion bias”. In observational studies assessing

cancer). Another is harm associated with the use of

efficacy and harm by comparing vaccinees with non-

Tamiflu: sudden death or hypoxic encephalopathy

vaccinees, this type of bias is normally taken intowith sequelae from respiratory arrest, and accidental

account.

death following abnormal behaviours. HPV vaccine

However, many epidemiologic studies have

has led to complex and intractable diseases involving

been published that do not deal with this bias

the nervous and immune systems, including

appropriately. Such studies may conclude that even a

autoimmune diseases.

harmful vaccine is “safe” or “leads to risk reduction”.

A task force headed by epidemiologist Y. Hirota,

The preliminary report on the Nagoya City study of

who was believed to have no conflict of interest with

HPV vaccine and symptoms, which was first issued in

the pharmaceutical company that developed Tamiflu,

December 2015, has some serious limitations. One isproduced a fraudulent analysis of an epidemiologic

the neglect of “frailty exclusion bias”. In this study, 88

survey on the association of Tamiflu use with

~ 90% of girls born in the years 1994 to 1996 wereabnormal behaviors. The Japanese Ministry of Health,vaccinated; it is therefore possible that frailty may

Labour and Welfare (MHLW) subsequently adopted

have accumulated in the remaining non-vaccinatedthis faulty analysis. Although Tamiflu increases

10 ~ 12% .

incidence of sudden death during influenza, many

It is not known whether the investigator who

observational studies that do not correct for time-

analysed the Nagoya City data omitted “frailty

dependent bias claim that it decreases mortality if

exclusion bias” intentionally or not. However, theused early.

conclusions stated in the preliminary report are

An epidemiologic study published in July 2015,

undoubtedly incorrect, and should not be used tofeatured in this issue of Med-Check, claims that there

reject causality. This report should not be used tois no association between the use of Actos (genericdeny the payment of compensation to the victims ofname pioglitazone) and bladder cancer. Professor

HPV vaccine. We strongly call on epidemiologists andBrian L. Strom, a world-class pharmacoepidemiologist,

biostatisticians to be honest in science.
MED CHECK - TIP August 2016 / Vol.2 No.5 ・ Page 17

Adverse Reactions
Symptoms after HPV vaccine:

typical "frailty exclusion bias" in Nagoya City study

Translation from Japanese edition of Med Check-TIP 2016: May(No 65):62-63
Abstract:
The preliminary released analysis of Nagoya City study which compared the proportion of symptoms among more than
30,000 girls with and without HPV vaccine, reported no link with the inoculation of HPV vaccine. However, the analysis seems
having serious biases including “frailty exclusion bias”, “healthy vaccinee effect” or “frailty selection bias”. We discuss thetheoretical basis of this type of bias which is frequently missed to be taken into account in epidemiologic studies not only on
the effectiveness and safety of vaccine but also on other medications.
Introduction
Nagoya City issued on December 14 2015 a preliminary

indicates that the analysis methods using age adjustment may
be inappropriate.

report on "A survey of HPV vaccination" which was conducted

In Nagoya City survey, HPV vaccine coverage (%) was up

in September 2015 [1]. It is a large scale questionnaire survey

to 85 to 90% among those born between 1994 and 1997.

to compare symptoms of the girls who were inoculated with

It is easily expected that frailty may be accumulated in the

HPV vaccine and those of the girls who were not. It was

remaining 10 to 15 %, who did not receive the vaccine: this isvaluable in that about 70,000 girls who were born between

one of the best examples of “frailty exclusion bias”.

1994 and 2000 living in Nagoya City were included in the

One of the major reasons why the proportion of the girls

survey and about 30,000 of them responded (response rate:with symptoms was the lowest in those born in 2000 (about

43.4%), including not only those who were vaccinated but

15-year-old) may be because they had the lowest coveragealso those who were not.

(15% ), and not because it was the youngest age group.

The preliminary report concluded that in a multivariate
analysis adjusted by age, none of the 24 kinds of symptoms

Frailty exclusion bias or healthy vaccinee effects

was reported significantly more frequently in those who were

If a person has fever on the day of vaccination, she

vaccinated with HPV vaccine, and denied the association

would be excluded from vaccination. Therefore, vaccinees

between HPV vaccine and serious symptoms after thehave better health than the non-vaccinated at the start ofvaccination.

comparison. Hence, even if the vaccine has no efficacy nor

As a response to our inquiry, Nagoya City explained

harmful effect, the result would show that it is effective andthat they would disclose the raw data on the web for an

safe. This is called “healthy vaccinee effect” [6,7] or “frailty

investigation by the third party, but they had never done it

selection bias” [8] in epidemiology.

yet on early May (note: data were disclosed on around 20th

We propose to call this type of bias as “frailty exclusion

June 2016). This report discusses the "frailty exclusion bias",bias” [9]. This is a type of selection bias and is usuallywhich Nagoya City study overlooked.

called “frailty selection bias”. However, it may cause
misunderstanding that frail persons are selectively vaccinated

The increase of symptoms which cannot be explained by age

while actually, they tend to be excluded from vaccination.

For instance, it was estimated that the girls with symptoms

This is also commonly called “healthy vaccinee effect”, andsuch as "difficulty in simple calculation” or "cannot walkit occurs frequently in observational studies on efficacy andnormally” among the non-vaccinees would increase forsafety of vaccines. However, this type of bias is not only

each age by 1.39 times (39%) or 1.38 times (38%) [4]. If aimportant in the assessment of vaccine but also should be

proportion of persons with the symptoms increase with age bytaken into account for medications in general [9]. An example

these ratios, what would be the consequence ? The proportion

for the latter is a case-control study claiming the efficacy ofat the age of 26 would be 30 times higher than that at therosiglitazone for reducing heart disease [10]. A proportion

age of 15. This estimation is obviously unrealistic. This simplyof patients who took rosiglitazone in the control group wasPage 18 ・ MED CHECK - TIP August 2016/ Vol.2 No.5

Figure 1: No exclusion of frailty

Figure 2: If frail people were excluded from vaccination

higher than 95%, and those who were not prescribed with

beneficial and harmful effects, odds of the frail isrosiglitazone probably had heart diseases that contraindicated(ac － ace)/(bc+ace) among the vaccinated andthe drug.

(ad+ace)/(bd － ace) among the non-vaccinated.

Without considering the “frailty exclusion bias”, we cannot
assess the harm of the HPV vaccine adequately.

Hence, the odds ratio of the frail in the vaccinatedcompared with the non-vaccinated is((ac － ace)/(bc + ace)) ／ ((ad + ace)/(bd － ace)).

Theoretical basis of the “frailty exclusion bias”

Unless "e" is 0, odds ratio of the frail in the vaccinated(1) No exclusioncompared with the non-vaccinated will always be less thanFirst, we consider the case in which no exclusion of frailty
occurs in Figure 1 under the condition that a vaccine has nobeneficial and harmful effect.

1.0 theoretically.
This is the theoretical basis of "frailty exclusion bias“,"frailty selection bias" or "healthy vaccinee effect".

Imagine that the proportion of high risk people (the frail) in
a population is “a”. People are vaccinated by the coverage "c".

Effect of the frailty exclusion bias

If the frail (people with high risk) or the healthy are equally

Under the condition in which the vaccine has no

vaccinated, and the vaccine does not cause any adverse

beneficial and harmful effects, and a proportion of the frail

effects, the odds of the frail is a/b for both the vaccinatedis 0.001 (0.1%), theoretical odds ratios of the frail in theand non-vaccinated. Hence, the odds ratio of the frail in thecase where the frail were excluded by “e” compared withvaccinated compared with the non-vaccinated is 1.0.

the case where the frail were not excluded from the non-(2) In the case with exclusion

vaccinated people is shown by percent coverage (Figure

If the frail were excluded from vaccination by the3a). In the Figure 3a, various curves for each e (0, 0.01,

proportion of “e”, and if the vaccine does not cause any

0.1, 0.3, 0.5, 0.7 and 0.9) are shown. Figure 3b is the

MED CHECK - TIP August 2016 / Vol.2 No.5 ・ Page 19

Figure 3: Theoretical effects of frailty exclusion on odds ratio compared with no exclusion(in the condition that vaccine has no benefit or harm)
A. Effect of exclusion in non-vaccinatedB. Effects of exclusion in vaccinated

A. If the percent coverage is around 15%, there is little effect of the exclusion bias among the non-vaccinated regardless of exclusion ( ↓ ).
However, the effect increases greatly when percent coverage increase up to 90% as in the study conducted by Nagoya City; the odds ratio of
the frail by exclusion compared with non-exclusion increases greatly ( → in the case when e=0.5, for example). It may be the results of the frailty
exclusion bias, which becomes more evident as the coverage increased up to 90% , and not of the effect of the age that the odds ratio of positive
symptoms of various birth year groups compared with those born in 2000 increased greatly up to 3.0 to 7.3 in the Nagoya City study.
B: The effect of the exclusion bias on the vaccinated group increases proportionally to the extent of “e” regardless of vaccination coverage.

Figure 4: Theoretical effects of frailty exclusion on odds ratio of vaccinated compared with non-vaccinated(in the condition that vaccine has no benefit or harm)
A: Odds ratio of the frail by
the coverage and exclusion

B: Extent of harm to be 1.0 ofapparent odds ratio

A. As a result, odds ratio of the frail comparing the vaccinated to the non-vaccinated decreases as the coverage (c) and/or exclusion (e) increase.
If c=0.9 and e=0.5, odds ratio may be 0.09 when a vaccine has no beneficial nor harmful effect.
B. Therefore, in order to demonstrate odds ratio of 1.0 when c=0.9 and e=0.5, the vaccine has to induce 11 times more harmful outcomes inthe vaccinated than in the non-vaccinated ( → ). In order to demonstrate apparent odds ratio of 2.0, when c=0.9 and e=0.5, the vaccine has toinduce 22 times more harmful outcomes in the vaccinated than in the non-vaccinated.

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theoretical odds ratios of the frail in the case where the frail
were excluded by “e” compared with the case where the frail
were not excluded from the vaccinated people. Figure 4 is thetheoretical odds ratios of the frail in the vaccinated groupcompared with that in the non-vaccinated group by percent
coverage and by “e”.

References
1) About Nagoya City, the enforcement of the cervical cancer vaccination
investigation,
http://www.city.nagoya.jp/kenkofukushi/page/0000076417.html2)http://www.city.nagoya.jp/kenkofukushi/cmsfiles/
contents/0000078/78732/kf271214-k.pdf
3) 2015.12.14 Mayor Takashi Kawamura, Nagoya City press conference

An adjustment by the health status before inoculation isessential

https://www.youtube.com/watch? v=X1m5wTR1Rlw&feature=youtu.be4) Nagoya City. Preliminary report of the survey on the symptoms after
HPV vaccine.

It is essential to adjust odds ratio of the harm of HPV

http://www.city.nagoya.jp/kenkofukushi/page/0000073419.html

vaccine by the health status before vaccination of the5) Satoshi Takano, medical truth of the HPV vaccine, MMJ (Mainichi

vaccinated and the non-vaccinated in order to get true
figures. However, it is not known whether the investigatorsof the Nagoya City study collected such information for the
adjustment. If not, it may be difficult to adjust the healthstatus before inoculation.
However, there are some methods to estimate better therisk of the HPV vaccine by utilizing the collected data.
Because odds ratio of individual positive symptoms in thesame stratum of birth year is not affected by age, odds ratios
and 95% confidence interval (95% CI) should be calculated by
the number of women with positive symptoms (numerators),

Medical Journal)2016:12(1): 7-11.
6) Fine PE, Chen RT. Confounding in studies of adverse reactions tovaccines. Am J Epidemiol. 1992; 136(2): 121-35.
7) von Kries R et al Sudden and unexpected deaths after the administrationof hexavalent vaccines: is there a signal? Eur J Pediatr. 2005;164:61-9.
8) Simonsen L, Taylor RJ, Viboud C, Miller MA, Jackson LA. Mortality
benefits of influenza vaccination in elderly people: an ongoing
controversy. Lancet Infect Dis. 2007 Oct; 7(10): 658-66.
9) Hama R. Do clinical trials and epidemiologic studies support the safetyof the human papillomavirus (HPV) vaccine? - A critical review, in Shawet al edd. "Controversies in Vaccine Safety", Elsevior (in press)
10) Florez H, Reaven PD, Bahn G, Moritz T, Warren S et al. VADT ResearchGroup. Rosiglitazone treatment and cardiovascular disease

number of women subjects (denominator) by symptom, byin the Veterans Affairs Diabetes Trial. Diabetes Obes Metab.

birth year, and by inoculation status.

2015;17(10):949-55.

Because the odds ratio in the stratum of women with least
coverage (15% ) of inoculation (born in 2000; about 15 years
old) may be affected the least by “frailty exclusion bias”, we
propose to adjust odds ratio of other strata of birth years thathave higher vaccine coverage.
Note that “frailty exclusion bias” will not completely
disappear by these procedures (cf. Figure 4-Aand B).
Conclusion
We strongly request that Nagoya City withdraw the
preliminary results and disclose the data so that the third
party could analyse them. We also recommend Nagoya
City to re-analyse the data appropriately by themselves assoon as possible (Note).

Note: Nagoya City withdrew the interim report anddisclosed the raw data as a PDF file on around 20th June,
2016. We are now working on the conversion of the raw
data into an excel file for further analysis.

MED CHECK - TIP August 2016 / Vol.2 No.5 ・ Page 21

Adverse Reactions

Carcinogenicity of pioglitazone (brand name: Actos)
A critical review on the recently published paper.
Translated synopsis from the Japanese edition of Med Check TIP No 63 (2016) with web supplementary materials.

Abstract:
Pioglitazone (Actos) is an anti-diabetic agent released in December, 1999 in Japan. We warn the harms of this agent since
the early stage of marketing. Bladder cancer is one of the serious adverse effects of the agent. The systematic reviews andmeta-analysis have shown statistically significant and dose dependent relation. A major new epidemiologic study (a final
analysis of Kaiser study) claiming no association has “a time related bias” of new type which we discussed in this synopsis.

Takeda Pharmaceutical has agreed to pay$2.4 billion tosettle thousands of lawsuits filed by patients and their family
members who said that Actos caused bladder cancer [1-3].

Figure 1: Risk of bladder cancer from pioglitazone use(a) Meta-analysis results [25]Two new epidemiological studies (one by Kaiser group [4]and the other from Europe [5]) reported that statisticallysignificant association was not found between Actos andbladder cancer [4,5]. Most media and academia expressed
their negative view on the association [6-9]. However, thedenial of the association seems to be erroneous as discussed
below.
Actos is an anti-diabetic agent released in December, 1999
in Japan. We have warned of harmful effects of Actos: on theheart failure, bone disorders and carcinogenesis just after the(b) Final report of Kaiser [4]marketing [10-12] and several times subsequently [14-16, 23]with the evidence on the pathogenesis [17-22] and the resultsfrom the animal carcinogenicity test [14].
A systematic review and meta-analysis results of fiverandomized controlled trials as well as a systematic review
and meta-analysis results of 13 epidemiologic surveys
including the interim-analysis of the Kaiser’s study publishedin 2011 [24] have shown statistically significant and dose
dependent relation between the risk of bladder cancer andActos use [25] (Figure 1a).
(c) Interim report of Kaiser [24]As the populations of both newly disclosed epidemiologic
studies are far smaller (less than one tenth [4] and less than
one thirtieth [5]) than the total population of those metaanalysed [25], the results of both studies do not affect theoverall results “positive association between Actos use andthe bladder cancer”.
Moreover, we found a different figure in the final reportof Kaiser study [4] (Figure 1b) compared with results of thesystematic review and meta-analysis [25] (Figure 1a). In the

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meta-analysis results, the dose dependent increase of bladderdatabase studies. They are most often a form of differential

cancer risk was observed, and even at the lowest dose, themisclassification bias and should be recognised as they canpoint odds ratio was more than 1.0 [25] (Figure 1a), but in thebe generally avoided by appropriate accounting of follow-up

final report of Kaiser study [4], odds ratio was less than 1.0,

time and exposure status in the design and analysis of such

namely 0.94 or 0.95 (Figure 1b). The results from the interim

studies.” [26]report [24] (Figure 1c) was intermediate between the metaanalysis results (Figure 1a) and the final report (Figure 1b).

Immortal time bias

We investigated the reason why such controversial

Immortal time in epidemiology refers to a period of cohort

phenomena were observed; bladder cancer risk of Actos

follow-up time during which death (or an outcome that

became lower in the final results compared with the interim

determines end of follow-up) cannot occur. It is defined inresults, and negative tendency of association was observed in

the book Modern Epidemiology (K. Rothman, S. Greenland,

the final results.

T. Lash. 3rd Edition, Lippincott Williams & Wilkins, 2008 p.

We conclude that misclassifications of exposure to Actos

106-7) [26].

yield time-related bias [26-32] in the Kaiser study and it
became more evident after the observation period wasprolonged. This time-related bias is precisely described in thenext section.

The exposure definition in the methods of Kaiser study:“Ever use” of pioglitazone and other diabetes medications
was defined as “having filled 2 prescriptions for the drug

Actos is a PPAR-γ agonist that regulates fat metabolism,

within a 6-month period”. Once a patient met the exposureand may affect immune and inflammatory reactions.

definition, he or she was considered exposed from that point

Consequently, it may have carcinogenicity [34, 35]. These

forward.

biological properties are consistent with the findings from

Figure 3 shows 12 typical individuals by the exposure stateclinical, epidemiological and animal toxicity (carcinogenicity)of pioglitazone and bladder cancer diagnosis to demonstratetests (Figure 2).

how the immortal time bias results in the final odds ratios,
although the Kaiser study conducted so that immortal timeOn the “time-related bias”

bias does not occur.

“Time-related bias in observational studies can produce

In the Figure 3, during the period c (Case 4-6) and theillusory results in favour of the treatment group andperiod d (Case 7) no event occurred. This means that these

may affect both cohort and case-control studies, mostly

periods are the immortal time: i.e. a period before Actos being

Figure 2: Actos-dose related increase of proportion of rats with bladder cancer--- from the rats carcinogenicity study for 2 years.

Dose response was statistically significant including the highest dose group. (p=0.0006). All-cause mortality was 40
to 63 % in the control and in the lower dose group of Actos (14.5 mg/kg/day or less). However, all-cause mortality
was 91.7% in the highest dose group (57 mg/kg/day). Hence the reason of relatively low incidence of mortality
may be derived from the early death caused by the diseases (by Actos) other than bladder cancer.

MED CHECK - TIP August 2016 / Vol.2 No.5 ・ Page 23

Figure 3: Theoretical explanation of time related bias including immortal time bias in the observational study:
Description of 12 typical individuals by the exposure state of pioglitazone and bladder cancer diagnosis

Figure 4:Theoretical explanation of time related bias in the Kaiser study; Description of 12
typical individuals by the exposure state of pioglitazone and bladder cancer diagnosisprescribed (or in the Kaiser investigation, the time before the= “prescribed more than twice within six months", he or she

second prescription for Actos was issued). The reason why

was subsequently considered as “user”, even if Actos was not

Actos was prescribed in the Case 4-7 is that an event (bladderprescribed after they became a user as the Case 1-3, 5 and 7.

cancer) did not occur during these periods.
If an event (bladder cancer) occurred during the period as

In the follow-up study [33] conducted after the PROactivein the Case 8, 11, Actos may not be prescribed after the event.

study [22], incidence of bladder cancer among Actos non-

Even if Actos is prescribed, the event during the subsequent

users was compared between those who received placebo andperiod is not used for the analysis.

those who received Actos in the PROactive study. Odds ratio

The odds ratio is 7.7 (eight years follow-up) and 5.5

was less than 1 (there is no significant difference) and the(ten years follow-up) without bias. However, the odds ratio

positive association between Actos use and bladder cancer

reversed to 0.98 (eight years follow-up) and 0.64 (ten years

disappeared after discontinuation of Actos.

follow-up), which are very favourable to the intervention if
immortal time bias is not corrected.

Therefore, if Actos was stopped in the Kaiser study, theparticipants should be classified as "the non-use group" afterthat (or at least after one year subsequently). However, theIn the Kaiser investigation, as in the definition of “exposure”,
once the participants are met with the definition of a "user”

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Kaiser study classified them as "the use group" mistakenly.
By using the cases (in the table on Figure 4), odds ratios arecalculated at 1.72 (eight years follow-up) and 0.95 (ten years

bladder cancer by Actos use is clearly shown due to the “time-

follow-up) if the definition of exposure by Kaiser methods was

related bias”. This time-related bias should be called “post-

applied, while the unbiased odds ratios are 7.7 (eight years

exposure bias”.

follow-up) and 5.5 (ten years follow-up).
Because the immortal time bias is avoided in the Kaiser
method, the underestimation effect size is reduced compared

Note that the effect of time-related bias becomes bigger
as the observation period becomes longer in both biased
methods.

with that in the Figure 1, but still underestimation of risk ofReferences

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The Informed Prescriber
2016

Vol.2

No.5

Editor-in-Chief: HAMA, Rokuro (MD)
Deputy Editor: KIMOTO, Yasusuke (MD); TANIDA, Noritoshi (MD)Managing Editor: SAKAGUCHI, Keiko
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Fumiko.(Pharmacist) ; TAKANO, Yoshihiko(MD[ pediatrician ]/Pharmacist) ; YANAGI, Motokazu (MD); YASUDA, Yoshinobu(Pharmacist)
Translators: NAKAMURA, Akari ; TAKAMACHI, Koji

Page 26 ・ MED CHECK - TIP August 2016/ Vol.2 No.5

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